ModelDB: A System to Manage Machine Learning Models: Spark Summit East talk by Manasi Vartak

•

7 j'aime•3,842 vues

Building a machine learning model is an iterative process. A data scientist will build many tens to hundreds of models before arriving at one that meets some acceptance criteria. However, the current style of model building is ad-hoc and there is no practical way for a data scientist to manage models that are built over time. In addition, there are no means to run complex queries on models and related data. In this talk, we present ModelDB, a novel end-to-end system for managing machine learning (ML) models. Using client libraries, ModelDB automatically tracks and versions ML models in their native environments (e.g. spark.ml, scikit-learn). A common set of abstractions enable ModelDB to capture models and pipelines built across different languages and environments. The structured representation of models and metadata then provides a platform for users to issue complex queries across various modeling artifacts. Our rich web frontend provides a way to query ModelDB at varying levels of granularity. ModelDB has been open-sourced at https://github.com/mitdbg/modeldb.

Données & analyses

ModelDB: A system
to manage machine
learning models
Manasi Vartak
PhD Student, MIT DB Group

People
Manasi Vartak
PhD student, MIT
Srinidhi Viswanathan
MEng, MIT
Samuel Madden
Faculty, MIT
Matei Zaharia
Faculty, Stanford
Harihar Subramanyam
MEng, MIT
Wei-En Lee
MEng student, MIT

Building a default
prediction algorithm
Profession Credit History Risk of Default
Politician Reasonable 0.3
Struggling
artist
Poor 0.7
Investor
Has more
money than our
company
0.0
… … … …
Barack
Obama
Lindsay
Lohan
Warren
Buffet

Model 3
RandomForestClassifier
val udf1: (Int => Int) = (delayed..)
df.withColumn(“timesDelayed”, udf1)

RandomForestClassifier
df.withColumn(“timesDelayed”, udf1)
.withColumn(“percentPaid”, udf2)
val lrGrid = new ParamGridBuilder()
.addGrid(rf.maxDepth, Array(5, 10, 15))
.addGrid(rf.numTrees, Array(50, 100))
Model 5
credit-default-clean.csv

df.withColumn(“timesDelayed”, udf1)
.withColumn(“percentPaid”, udf2)
.withColumn(“creditUsed”, udf3)
…
val lrGrid = new ParamGridBuilder()
.addGrid(lr.elasticNetParam, Array(0.01, 0.1, 0.5, 0.7))
val scaler = new StandardScaler()
.setInputCol(“features”)
…
val labelIndexer1 = new LabelIndexer()
val labelIndexer2 = new LabelIndexer()
…
Model 50
val udf1: (Int => Int) = (delayed..)
val udf2: (String, Int) = …
credit-default-clean.csv

No one in here tracks (all of)
their models
…and this is not unusual
I’m willing to bet…

Why is this a problem?
• No record of experiments
• Insights lost along the way
• Difﬁcult to reproduce results
• Cannot search for or query models
• Difﬁcult to collaborate
Did my colleague do that
already?
How did normalization
affect my ROC?
How does someone review
your model?
Where’s the LR
model I tried last
week with featureX?
What params did I use?

Model Management
track, store and index modeling artifacts
so that they may subsequently be
reproduced, shared, queried, and
analyzed

ModelDB: a system to
manage machine
learning models
http://modeldb.csail.mit.edu

ModelDB: an end-to-end
model management system
Model artifact
Storage &
Versioning
Query
Ingest models,
metadata
Collaboration,
Reproducibilitytrack
store &
index
query, reproduce++

ModelDB Architecture &
Design Decisions
1. Support for diverse
languages and environments
2. Minimal changes to
existing workflows
3. Rich visual interface
4. Support for complex
queries
spark.ml
scikit-learn
ModelDB
Backend
Storage
thrift
Scala
Python
…
ModelDB
Frontend:
vis + query
Native Client
Events

ModelDB Features
• Experiment tracking
• Versioning
• Reproducibility
• Comparisons, queries, search
• Collaboration
Log models, params, pipelines
etc. via ModelDB API
Model search, query,
comparison via frontend
Central repository of models
Review models, annotate
All pipeline details, params
logged
Every modeling run = version

Ongoing Work
• Uniﬁed querying of modeling artifacts
• Mining data in ModelDB
• Model monitoring and retraining

ModelDB available now!
http://modeldb.csail.mit.edu
*MIT License

ModelDB available now!
• Download, try it out!
• Tell us what you think; what can we do better?
• Contribute! (see Issues on repo for some ideas)

ModelDB: a system to
manage machine
learning models
mvartak@csail.mit.edu | @DataCereal
http://modeldb.csail.mit.edu

Contenu connexe

Tendances

Comparison of Machine Learning Algorithms

butest

Model validation in machine learning represents an indispensable step in the development of AI models. It involves verifying the efficacy of an AI model by assessing its performance against certain predefined standards. This process does not merely involve feeding data to a model, training it, and deploying it. Rather, model validation in machine learning necessitates a rigorous check on the model’s results to ascertain it aligns with our expectations.

Model validation techniques in machine learning.pdf

AnastasiaSteele10

Artificial Intelligence, Machine Learning and Deep Learning

Sujit Pal

XenMobile Packet Flow

Nuno Alves

Computer Vision - RANSAC

Wael Badawy

Machine Learning Project

Abhishek Singh

credit card fraud detection

jagan477830

Agenda: 1) Defence, Modern Warfare, and Cybersecurity in 202X 2) Data in Motion with Apache Kafka as Defence Backbone 3) Situational Awareness 4) Threat Intelligence 5) Forensics and AI / Machine Learning 6) Air-Gapped and Zero Trust Environments 7) SIEM / SOAR Modernization Technologies discussed in the presentation include Apache Kafka, Kafka Streams, kqlDB, Kafka Connect, Elasticsearch, Splunk, IBM QRadar, Zeek, Netflow, PCAP, TensorFlow, AWS, Azure, GCP, Sigma, Confluent Cloud,

Data Streaming with Apache Kafka in the Defence and Cybersecurity Industry

Kai Wähner

House price prediction

AdityaKumar1505

Machine Learning and computing power have made huge improvements in the last decade. It’s now possible to unlock complex problems in multidimensional space with ensemble, brute force algorithms or deep neural networks, with performances that were unthinkable a few years ago. However the use of black box models is still frown upon in a business setting. In fact the decision functions of those models are often impossible to interpret for humans, can be biased or just based on absurd assumption. What if your risk model denies loans to people on ethnic ground? SHAP comes as an innovative framework to obtain local explanations for the output of a model, making the black box much more transparent.

Shap

Giovanni Bruner

Artificial Intelligence and Expert Systems

Siddhant Agarwal

Hidden Markov Model - The Most Probable Path

Lê Hòa

Hidden Markov Models with applications to speech recognition

butest

Machine learning

InfoFarm

Machine Learning

Shrey Malik

Loan approval prediction based on machine learning approach

Eslam Nader

Machine Learning for Dummies

Venkata Reddy Konasani

Machine Learning for Disease Prediction

Mustafa Oğuz

Machine Learning presentation.

butest

In machine learning, support vector machines (SVMs, also support vector networks[1]) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. The basic SVM takes a set of input data and predicts, for each given input, which of two possible classes forms the output, making it a non-probabilistic binary linear classifier.

Support Vector Machines for Classification

Prakash Pimpale

Tendances (20)

Comparison of Machine Learning Algorithms

Model validation techniques in machine learning.pdf

Artificial Intelligence, Machine Learning and Deep Learning

XenMobile Packet Flow

Computer Vision - RANSAC

Machine Learning Project

credit card fraud detection

Data Streaming with Apache Kafka in the Defence and Cybersecurity Industry

House price prediction

Shap

Artificial Intelligence and Expert Systems

Hidden Markov Model - The Most Probable Path

Hidden Markov Models with applications to speech recognition

Machine learning

Machine Learning

Loan approval prediction based on machine learning approach

Machine Learning for Dummies

Machine Learning for Disease Prediction

Machine Learning presentation.

Support Vector Machines for Classification

Similaire à ModelDB: A System to Manage Machine Learning Models: Spark Summit East talk by Manasi Vartak

ModelDB: A System to Manage Machine Learning Models: Spark Summit East talk b...

Spark Summit

AI Class Topic 4: Text Analytics, Sentiment Analysis and Apache Spark

Value Amplify Consulting

Robust MLOps with Open-Source: ModelDB, Docker, Jenkins, and Prometheus

Manasi Vartak

Telecom datascience master_public

Vincent Michel

Data products derive their value from data and generate new data in return; as a result, machine learning techniques must be applied to their architecture and their development. Machine learning fits models to make predictions on unknown inputs and must be generalizable and adaptable. As such, fitted models cannot exist in isolation; they must be operationalized and user facing so that applications can benefit from the new data, respond to it, and feed it back into the data product. Data product architectures are therefore life cycles and understanding the data product lifecycle will enable architects to develop robust, failure free workflows and applications. In this talk we will discuss the data product life cycle, explore how to engage a model build, evaluation, and selection phase with an operation and interaction phase. Following the lambda architecture, we will investigate wrapping a central computational store for speed and querying, as well as incorporating a discussion of monitoring, management, and data exploration for hypothesis driven development. From web applications to big data appliances; this architecture serves as a blueprint for handling data services of all sizes!

Data Product Architectures

Benjamin Bengfort

Talk from Software Engineering for Machine Learning Workshop (SW4ML) at the Neural Information Processing Systems (NIPS) 2014 conference in Montreal, Canada on 2014-12-13. Abstract: Building a real system that incorporates machine learning as a part can be a difficult effort, both in terms of the algorithmic and engineering challenges involved. In this talk I will focus on the engineering side and discuss some of the practical issues we’ve encountered in developing real machine learning systems at Netflix and some of the lessons we’ve learned over time. I will describe our approach for building machine learning systems and how it comes from a desire to balance many different, and sometimes conflicting, requirements such as handling large volumes of data, choosing and adapting good algorithms, keeping recommendations fresh and accurate, remaining responsive to user actions, and also being flexible to accommodate research and experimentation. I will focus on what it takes to put machine learning into a real system that works in a feedback loop with our users and how that imposes different requirements and a different focus than doing machine learning only within a lab environment. I will address the particular software engineering challenges that we’ve faced in running our algorithms at scale in the cloud. I will also mention some simple design patterns that we’ve fond to be useful across a wide variety of machine-learned systems.

Lessons Learned from Building Machine Learning Software at Netflix

Justin Basilico

This talk introduces the main techniques of Recommender Systems and Topic Modeling. Then, we present a case of how we've combined those techniques to build Smart Canvas, a SaaS that allows people to bring, create and curate content relevant to their organization, and also helps to tear down knowledge silos. We give a deep dive into the design of our large-scale recommendation algorithms, giving special attention to a content-based approach that uses topic modeling techniques (like LDA and NMF) to discover people’s topics of interest from unstructured text, and social-based algorithms using a graph database connecting content, people and teams around topics. Our typical data pipeline that includes the ingestion millions of user events (using Google PubSub and BigQuery), the batch processing of the models (with PySpark, MLib, and Scikit-learn), the online recommendations (with Google App Engine, Titan Graph Database and Elasticsearch), and the data-driven evaluation of UX and algorithms through A/B testing experimentation. We also touch topics about non-functional requirements of a software-as-a-service like scalability, performance, availability, reliability and multi-tenancy and how we addressed it in a robust architecture deployed on Google Cloud Platform. Short-Bio: Gabriel Moreira is a scientist passionate about solving problems with data. He is Head of Machine Learning at CI&T and Doctoral student at Instituto Tecnológico de Aeronáutica - ITA. where he has also got his Masters on Science. His current research interests are recommender systems and deep learning. https://www.meetup.com/pt-BR/machine-learning-big-data-engenharia/events/239037949/

Discovering User's Topics of Interest in Recommender Systems @ Meetup Machine...

Gabriel Moreira

Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets. The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.

Introduction to Machine Learning with SciKit-Learn

Benjamin Bengfort

C19013010 the tutorial to build shared ai services session 1

Bill Liu

Recsys 2016

Mindaugas Zickus

Data Scientists and Machine Learning practitioners, nowadays, seem to be churning out models by the dozen and they continuously experiment to find ways to improve their accuracies. They also use a variety of ML and DL frameworks & languages , and a typical organization may find that this results in a heterogenous, complicated bunch of assets that require different types of runtimes, resources and sometimes even specialized compute to operate efficiently. But what does it mean for an enterprise to actually take these models to "production" ? How does an organization scale inference engines out & make them available for real-time applications without significant latencies ? There needs to be different techniques for batch (offline) inferences and instant, online scoring. Data needs to be accessed from various sources and cleansing, transformations of data needs to be enabled prior to any predictions. In many cases, there maybe no substitute for customized data handling with scripting either. Enterprises also require additional auditing and authorizations built in, approval processes and still support a "continuous delivery" paradigm whereby a data scientist can enable insights faster. Not all models are created equal, nor are consumers of a model - so enterprises require both metering and allocation of compute resources for SLAs. In this session, we will take a look at how machine learning is operationalized in IBM Data Science Experience (DSX), a Kubernetes based offering for the Private Cloud and optimized for the HortonWorks Hadoop Data Platform. DSX essentially brings in typical software engineering development practices to Data Science, organizing the dev->test->production for machine learning assets in much the same way as typical software deployments. We will also see what it means to deploy, monitor accuracies and even rollback models & custom scorers as well as how API based techniques enable consuming business processes and applications to remain relatively stable amidst all the chaos. Speaker Piotr Mierzejewski, Program Director Development IBM DSX Local, IBM

Machine Learning Models in Production

DataWorks Summit

This session was presented at the CFA Institute on May 6th 2020 This deep-dive session discusses core methods and applications to provide an understanding of supervised and unsupervised machine learning. Participants will be introduced to advanced topics that include time series analysis, reinforcement learning, anomaly detection, and natural language processing. Case studies will also examine how to predict interest rates and credit risk with alternative data sets and how to analyze earning calls from EDGAR using Natural Language Processing Techniques.

Machine Learning and AI: Core Methods and Applications

QuantUniversity

B sc it syit sem 3 sem 4 syllabus as per mumbai university

tanujaparihar

Beautiful Models in PHP

brandonsavage

Scaling up Machine Learning Development

Matei Zaharia

Machine Learning with TensorFlow.js

Brian Greig

Lecture_1_Intro.pdf

paijitk

This talk introduces the main techniques of Recommender Systems and Topic Modeling. Then, we present a case of how we've combined those techniques to build Smart Canvas (www.smartcanvas.com), a service that allows people to bring, create and curate content relevant to their organization, and also helps to tear down knowledge silos. We present some of Smart Canvas features powered by its recommender system, such as: - Highlight relevant content, explaining to the users which of his topics of interest have generated each recommendation. - Associate tags to users’ profiles based on topics discovered from content they have contributed. These tags become searchable, allowing users to find experts or people with specific interests. - Recommends people with similar interests, explaining which topics brings them together. We give a deep dive into the design of our large-scale recommendation algorithms, giving special attention to our content-based approach that uses topic modeling techniques (like LDA and NMF) to discover people’s topics of interest from unstructured text, and social-based algorithms using a graph database connecting content, people and teams around topics. Our typical data pipeline that includes the ingestion millions of user events (using Google PubSub and BigQuery), the batch processing of the models (with PySpark, MLib, and Scikit-learn), the online recommendations (with Google App Engine, Titan Graph Database and Elasticsearch), and the data-driven evaluation of UX and algorithms through A/B testing experimentation. We also touch topics about non-functional requirements of a software-as-a-service like scalability, performance, availability, reliability and multi-tenancy and how we addressed it in a robust architecture deployed on Google Cloud Platform.

Discovering User's Topics of Interest in Recommender Systems

Gabriel Moreira

CEDAR Technologies for AIRR Submissions

Syed Ahmad Chan Bukhari, PhD

On the Customization of Model Management Systems for File-Centric IDEs

David Méndez-Acuña

Similaire à ModelDB: A System to Manage Machine Learning Models: Spark Summit East talk by Manasi Vartak (20)

ModelDB: A System to Manage Machine Learning Models: Spark Summit East talk b...

AI Class Topic 4: Text Analytics, Sentiment Analysis and Apache Spark

Robust MLOps with Open-Source: ModelDB, Docker, Jenkins, and Prometheus

Telecom datascience master_public

Data Product Architectures

Lessons Learned from Building Machine Learning Software at Netflix

Discovering User's Topics of Interest in Recommender Systems @ Meetup Machine...

Introduction to Machine Learning with SciKit-Learn

C19013010 the tutorial to build shared ai services session 1

Recsys 2016

Machine Learning Models in Production

Machine Learning and AI: Core Methods and Applications

B sc it syit sem 3 sem 4 syllabus as per mumbai university

Beautiful Models in PHP

Scaling up Machine Learning Development

Machine Learning with TensorFlow.js

Lecture_1_Intro.pdf

Discovering User's Topics of Interest in Recommender Systems

CEDAR Technologies for AIRR Submissions

On the Customization of Model Management Systems for File-Centric IDEs

Plus de Spark Summit

In this session we will present a Configurable FPGA-Based Spark SQL Acceleration Architecture. It is target to leverage FPGA highly parallel computing capability to accelerate Spark SQL Query and for FPGA’s higher power efficiency than CPU we can lower the power consumption at the same time. The Architecture consists of SQL query decomposition algorithms, fine-grained FPGA based Engine Units which perform basic computation of sub string, arithmetic and logic operations. Using SQL query decomposition algorithm, we are able to decompose a complex SQL query into basic operations and according to their patterns each is fed into an Engine Unit. SQL Engine Units are highly configurable and can be chained together to perform complex Spark SQL queries, finally one SQL query is transformed into a Hardware Pipeline. We will present the performance benchmark results comparing the queries with FGPA-Based Spark SQL Acceleration Architecture on XEON E5 and FPGA to the ones with Spark SQL Query on XEON E5 with 10X ~ 100X improvement and we will demonstrate one SQL query workload from a real customer.

FPGA-Based Acceleration Architecture for Spark SQL Qi Xie and Quanfu Wang

Spark Summit

In this talk, we’ll present techniques for visualizing large scale machine learning systems in Spark. These are techniques that are employed by Netflix to understand and refine the machine learning models behind Netflix’s famous recommender systems that are used to personalize the Netflix experience for their 99 millions members around the world. Essential to these techniques is Vegas, a new OSS Scala library that aims to be the “missing MatPlotLib” for Spark/Scala. We’ll talk about the design of Vegas and its usage in Scala notebooks to visualize Machine Learning Models.

VEGAS: The Missing Matplotlib for Scala/Apache Spark with DB Tsai and Roger M...

Spark Summit

This presentation introduces how we design and implement a real-time processing platform using latest Spark Structured Streaming framework to intelligently transform the production lines in the manufacturing industry. In the traditional production line there are a variety of isolated structured, semi-structured and unstructured data, such as sensor data, machine screen output, log output, database records etc. There are two main data scenarios: 1) Picture and video data with low frequency but a large amount; 2) Continuous data with high frequency. They are not a large amount of data per unit. However the total amount of them is very large, such as vibration data used to detect the quality of the equipment. These data have the characteristics of streaming data: real-time, volatile, burst, disorder and infinity. Making effective real-time decisions to retrieve values from these data is critical to smart manufacturing. The latest Spark Structured Streaming framework greatly lowers the bar for building highly scalable and fault-tolerant streaming applications. Thanks to the Spark we are able to build a low-latency, high-throughput and reliable operation system involving data acquisition, transmission, analysis and storage. The actual user case proved that the system meets the needs of real-time decision-making. The system greatly enhance the production process of predictive fault repair and production line material tracking efficiency, and can reduce about half of the labor force for the production lines.

Apache Spark Structured Streaming Helps Smart Manufacturing with Xiaochang Wu

Spark Summit

As common sense would suggest, weather has a definite impact on traffic. But how much? And under what circumstances? Can we improve traffic (congestion) prediction given weather data? Predictive traffic is envisioned to significantly impact how driver’s plan their day by alerting users before they travel, find the best times to travel, and over time, learn from new IoT data such as road conditions, incidents, etc. This talk will cover the traffic prediction work conducted jointly by IBM and the traffic data provider. As a part of this work, we conducted a case study over five large metropolitans in the US, 2.58 billion traffic records and 262 million weather records, to quantify the boost in accuracy of traffic prediction using weather data. We will provide an overview of our lambda architecture with Apache Spark being used to build prediction models with weather and traffic data, and Spark Streaming used to score the model and provide real-time traffic predictions. This talk will also cover a suite of extensions to Spark to analyze geospatial and temporal patterns in traffic and weather data, as well as the suite of machine learning algorithms that were used with Spark framework. Initial results of this work were presented at the National Association of Broadcasters meeting in Las Vegas in April 2017, and there is work to scale the system to provide predictions in over a 100 cities. Audience will learn about our experience scaling using Spark in offline and streaming mode, building statistical and deep-learning pipelines with Spark, and techniques to work with geospatial and time-series data.

Improving Traffic Prediction Using Weather Data with Ramya Raghavendra

Spark Summit

Graph is on the rise and it’s time to start learning about scalable graph analytics! In this session we will go over two Spark-based Graph Analytics frameworks: Tinkerpop and GraphFrames. While both frameworks can express very similar traversals, they have different performance characteristics and APIs. In this Deep-Dive by example presentation, we will demonstrate some common traversals and explain how, at a Spark level, each traversal is actually computed under the hood! Learn both the fluent Gremlin API as well as the powerful GraphFrame Motif api as we show examples of both simultaneously. No need to be familiar with Graphs or Spark for this presentation as we’ll be explaining everything from the ground up!

A Tale of Two Graph Frameworks on Spark: GraphFrames and Tinkerpop OLAP Artem...

Spark Summit

Building accurate machine learning models has been an art of data scientists, i.e., algorithm selection, hyper parameter tuning, feature selection and so on. Recently, challenges to breakthrough this “black-arts” have got started. In cooperation with our partner, NEC Laboratories America, we have developed a Spark-based automatic predictive modeling system. The system automatically searches the best algorithm, parameters and features without any manual work. In this talk, we will share how the automation system is designed to exploit attractive advantages of Spark. The evaluation with real open data demonstrates that our system can explore hundreds of predictive models and discovers the most accurate ones in minutes on a Ultra High Density Server, which employs 272 CPU cores, 2TB memory and 17TB SSD in 3U chassis. We will also share open challenges to learn such a massive amount of models on Spark, particularly from reliability and stability standpoints. This talk will cover the presentation already shown on Spark Summit SF’17 (#SFds5) but from more technical perspective.

No More Cumbersomeness: Automatic Predictive Modeling on Apache Spark Marcin ...

Spark Summit

In Sweden, from the Rise ICE Data Center at www.hops.site, we are providing to reseachers both Spark-as-a-Service and, more recently, Tensorflow-as-a-Service as part of the Hops platform. In this talk, we examine the different ways in which Tensorflow can be included in Spark workflows, from batch to streaming to structured streaming applications. We will analyse the different frameworks for integrating Spark with Tensorflow, from Tensorframes to TensorflowOnSpark to Databrick’s Deep Learning Pipelines. We introduce the different programming models supported and highlight the importance of cluster support for managing different versions of python libraries on behalf of users. We will also present cluster management support for sharing GPUs, including Mesos and YARN (in Hops Hadoop). Finally, we will perform a live demonstration of training and inference for a TensorflowOnSpark application written on Jupyter that can read data from either HDFS or Kafka, transform the data in Spark, and train a deep neural network on Tensorflow. We will show how to debug the application using both Spark UI and Tensorboard, and how to examine logs and monitor training.

Apache Spark and Tensorflow as a Service with Jim Dowling

Spark Summit

Apache Spark and Tensorflow as a Service with Jim Dowling

Spark Summit

With the rapid growth of available datasets, it is imperative to have good tools for extracting insight from big data. The Spark ML library has excellent support for performing at-scale data processing and machine learning experiments, but more often than not, Data Scientists find themselves struggling with issues such as: low level data manipulation, lack of support for image processing, text analytics and deep learning, as well as the inability to use Spark alongside other popular machine learning libraries. To address these pain points, Microsoft recently released The Microsoft Machine Learning Library for Apache Spark (MMLSpark), an open-source machine learning library built on top of SparkML that seeks to simplify the data science process and integrate SparkML Pipelines with deep learning and computer vision libraries such as the Microsoft Cognitive Toolkit (CNTK) and OpenCV. With MMLSpark, Data Scientists can build models with 1/10th of the code through Pipeline objects that compose seamlessly with other parts of the SparkML ecosystem. In this session, we explore some of the main lessons learned from building MMLSpark. Join us if you would like to know how to extend Pipelines to ensure seamless integration with SparkML, how to auto-generate Python and R wrappers from Scala Transformers and Estimators, how to integrate and use previously non-distributed libraries in a distributed manner and how to efficiently deploy a Spark library across multiple platforms.

MMLSpark: Lessons from Building a SparkML-Compatible Machine Learning Library...

Spark Summit

The Next Accelerator Logging Service (NXCALS) is a new Big Data project at CERN aiming to replace the existing Oracle-based service. The main purpose of the system is to store and present Controls/Infrastructure related data gathered from thousands of devices in the whole accelerator complex. The data is used to operate the machines, improve their performance and conduct studies for new beam types or future experiments. During this talk, Jakub will speak about NXCALS requirements and design choices that lead to the selected architecture based on Hadoop and Spark. He will present the Ingestion API, the abstractions behind the Meta-data Service and the Spark-based Extraction API where simple changes to the schema handling greatly improved the overall usability of the system. The system itself is not CERN specific and can be of interest to other companies or institutes confronted with similar Big Data problems.

Next CERN Accelerator Logging Service with Jakub Wozniak

Spark Summit

In Between (A mobile App for couples, downloaded 20M in Global), from daily batch for extracting metrics, analysis and dashboard. Spark is widely used by engineers and data analysts in Between, thanks to the performance and expendability of Spark, data operating has become extremely efficient. Entire team including Biz Dev, Global Operation, Designers are enjoying data results so Spark is empowering entire company for data driven operation and thinking. Kevin, Co-founder and Data Team leader of Between will be presenting how things are going in Between. Listeners will know how small and agile team is living with data (how we build organization, culture and technical base) after this presentation.

Powering a Startup with Apache Spark with Kevin Kim

Spark Summit

Improving Traffic Prediction Using Weather Datawith Ramya Raghavendra

Spark Summit

In many cases, Big Data becomes just another buzzword because of the lack of tools that can support both the technological requirements for developing and deploying of the projects and/or the fluency of communication between the different profiles of people involved in the projects. In this talk, we will present Moriarty, a set of tools for fast prototyping of Big Data applications that can be deployed in an Apache Spark environment. These tools support the creation of Big Data workflows using the already existing functional blocks or supporting the creation of new functional blocks. The created workflow can then be deployed in a Spark infrastructure and used through a REST API. For better understanding of Moriarty, the prototyping process and the way it hides the Spark environment to the Big Data users and developers, we will present it together with a couple of examples based on a Industry 4.0 success cases and other on a logistic success case.

Hiding Apache Spark Complexity for Fast Prototyping of Big Data Applications—...

Spark Summit

Large-scale testing of new data products or enhancements to existing products in a research and development environment can be a technical challenge for data scientists. In some cases, tools available to data scientists lack production-level capacity, whereas other tools do not provide the algorithms needed to run the methodology. At Nielsen, the Databricks platform provided a solution to both of these challenges. This breakout session will cover a specific Nielsen business case where two methodology enhancements were developed and tested at large-scale using the Databricks platform. Development and large-scale testing of these enhancements would not have been possible using standard database tools.

How Nielsen Utilized Databricks for Large-Scale Research and Development with...

Spark Summit

Spline: Apache Spark Lineage not Only for the Banking Industry with Marek Nov...

Spark Summit

Since the invention of SQL and relational databases, data production has been about specifying how data is transformed through queries. While Apache Spark can certainly be used as a general distributed query engine, the power and granularity of Spark’s APIs enables a revolutionary increase in data engineering productivity: goal-based data production. Goal-based data production concerns itself with specifying WHAT the desired result is, leaving the details of HOW the result is achieved to a smart data warehouse running on top of Spark. That not only substantially increases productivity, but also significantly expands the audience that can work directly with Spark: from developers and data scientists to technical business users. With specific data and architecture patterns spanning the range from ETL to machine learning data prep and with live demos, this session will demonstrate how Spark users can gain the benefits of goal-based data production.

Goal Based Data Production with Sim Simeonov

Spark Summit

Have you imagined a simple machine learning solution able to prevent revenue leakage and monitor your distributed application? To answer this question, we offer a practical and a simple machine learning solution to create an intelligent monitoring application based on simple data analysis using Apache Spark MLlib. Our application uses linear regression models to make predictions and check if the platform is experiencing any operational problems that can impact in revenue losses. The application monitor distributed systems and provides notifications stating the problem detected, that way users can operate quickly to avoid serious problems which directly impact the company’s revenue and reduce the time for action. We will present an architecture for not only a monitoring system, but also an active actor for our outages recoveries. At the end of the presentation you will have access to our training program source code and you will be able to adapt and implement in your company. This solution already helped to prevent about US$3mi in losses last year.

Preventing Revenue Leakage and Monitoring Distributed Systems with Machine Le...

Spark Summit

Getting Ready to use Redis with Apache Spark is a technical tutorial designed to address integrating Redis with an Apache Spark deployment to increase the performance of serving complex decision models. To set the context for the session, we start with a quick introduction to Redis and the capabilities Redis provides. We cover the basic data types provided by Redis and cover the module system. Using an ad serving use-case, we look at how Redis can improve the performance and reduce the cost of using complex ML-models in production. Attendees will be guided through the key steps of setting up and integrating Redis with Spark, including how to train a model using Spark then load and serve it using Redis, as well as how to work with the Spark Redis module. The capabilities of the Redis Machine Learning Module (redis-ml) will be discussed focusing primarily on decision trees and regression (linear and logistic) with code examples to demonstrate how to use these feature. At the end of the session, developers should feel confident building a prototype/proof-of-concept application using Redis and Spark. Attendees will understand how Redis complements Spark and how to use Redis to serve complex, ML-models with high performance.

Getting Ready to Use Redis with Apache Spark with Dvir Volk

Spark Summit

Here we present a general supervised framework for record deduplication and author-disambiguation via Spark. This work differentiates itself by – Application of Databricks and AWS makes this a scalable implementation. Compute resources are comparably lower than traditional legacy technology using big boxes 24/7. Scalability is crucial as Elsevier’s Scopus data, the biggest scientific abstract repository, covers roughly 250 million authorships from 70 million abstracts covering a few hundred years. – We create a fingerprint for each content by deep learning and/or word2vec algorithms to expedite pairwise similarity calculation. These encoders substantially reduce compute time while maintaining semantic similarity (unlike traditional TFIDF or predefined taxonomies). We will briefly discuss how to optimize word2vec training with high parallelization. Moreover, we show how these encoders can be used to derive a standard representation for all our entities namely such as documents, authors, users, journals, etc. This standard representation can simplify the recommendation problem into a pairwise similarity search and hence it can offer a basic recommender for cross-product applications where we may not have a dedicate recommender engine designed. – Traditional author-disambiguation or record deduplication algorithms are batch-processing with small to no training data. However, we have roughly 25 million authorships that are manually curated or corrected upon user feedback. Hence, it is crucial to maintain historical profiles and hence we have developed a machine learning implementation to deal with data streams and process them in mini batches or one document at a time. We will discuss how to measure the accuracy of such a system, how to tune it and how to process the raw data of pairwise similarity function into final clusters. Lessons learned from this talk can help all sort of companies where they want to integrate their data or deduplicate their user/customer/product databases.

Deduplication and Author-Disambiguation of Streaming Records via Supervised M...

Spark Summit

The use of large-scale machine learning and data mining methods is becoming ubiquitous in many application domains ranging from business intelligence and bioinformatics to self-driving cars. These methods heavily rely on matrix computations, and it is hence critical to make these computations scalable and efficient. These matrix computations are often complex and involve multiple steps that need to be optimized and sequenced properly for efficient execution. This work presents new efficient and scalable matrix processing and optimization techniques based on Spark. The proposed techniques estimate the sparsity of intermediate matrix-computation results and optimize communication costs. An evaluation plan generator for complex matrix computations is introduced as well as a distributed plan optimizer that exploits dynamic cost-based analysis and rule-based heuristics The result of a matrix operation will often serve as an input to another matrix operation, thus defining the matrix data dependencies within a matrix program. The matrix query plan generator produces query execution plans that minimize memory usage and communication overhead by partitioning the matrix based on the data dependencies in the execution plan. We implemented the proposed matrix techniques inside the Spark SQL, and optimize the matrix execution plan based on Spark SQL Catalyst. We conduct case studies on a series of ML models and matrix computations with special features on different datasets. These are PageRank, GNMF, BFGS, sparse matrix chain multiplications, and a biological data analysis. The open-source library ScaLAPACK and the array-based database SciDB are used for performance evaluation. Our experiments are performed on six real-world datasets are: social network data ( e.g., soc-pokec, cit-Patents, LiveJournal), Twitter2010, Netflix recommendation data, and 1000 Genomes Project sample. Experiments demonstrate that our proposed techniques achieve up to an order-of-magnitude performance.

MatFast: In-Memory Distributed Matrix Computation Processing and Optimization...

Spark Summit

Plus de Spark Summit (20)

FPGA-Based Acceleration Architecture for Spark SQL Qi Xie and Quanfu Wang

VEGAS: The Missing Matplotlib for Scala/Apache Spark with DB Tsai and Roger M...

Apache Spark Structured Streaming Helps Smart Manufacturing with Xiaochang Wu

Improving Traffic Prediction Using Weather Data with Ramya Raghavendra

A Tale of Two Graph Frameworks on Spark: GraphFrames and Tinkerpop OLAP Artem...

No More Cumbersomeness: Automatic Predictive Modeling on Apache Spark Marcin ...

Apache Spark and Tensorflow as a Service with Jim Dowling

MMLSpark: Lessons from Building a SparkML-Compatible Machine Learning Library...

Next CERN Accelerator Logging Service with Jakub Wozniak

Powering a Startup with Apache Spark with Kevin Kim

Improving Traffic Prediction Using Weather Datawith Ramya Raghavendra

Hiding Apache Spark Complexity for Fast Prototyping of Big Data Applications—...

How Nielsen Utilized Databricks for Large-Scale Research and Development with...

Spline: Apache Spark Lineage not Only for the Banking Industry with Marek Nov...

Goal Based Data Production with Sim Simeonov

Preventing Revenue Leakage and Monitoring Distributed Systems with Machine Le...

Getting Ready to Use Redis with Apache Spark with Dvir Volk

Deduplication and Author-Disambiguation of Streaming Records via Supervised M...

MatFast: In-Memory Distributed Matrix Computation Processing and Optimization...

Dernier

👉 Amritsar Call Girl 👉📞 6367187148 👉📞 Just📲 Call Ruhi Call Girl Phone No Amritsar No💰Advance Cash On Delivery Service Escorts Service Available Whatsapp Chaya ☎️ : [+91-6367187148] Escorts Service Amritsar are always ready to make their clients happy. Their exotic looks and sexy personalities are sure to turn heads. You can enjoy with them, including massages and erotic encounters.#P12Our area Escorts are young and sexy, so you can expect to have an exotic time with them. They are trained to satiate your naughty nerves and they can handle anything that you want. They are also intelligent, so they know how to make you feel comfortable and relaxed SERVICE ✅ ❣️ ⭐➡️HOT & SEXY MODELS // COLLEGE GIRLS HOUSE WIFE RUSSIAN , AIR HOSTES ,VIP MODELS . AVAILABLE FOR COMPLETE ENJOYMENT WITH HIGH PROFILE INDIAN MODEL AVAILABLE HOTEL & HOME ★ SAFE AND SECURE HIGH CLASS SERVICE AFFORDABLE RATE ★ SATISFACTION,UNLIMITED ENJOYMENT. ★ All Meetings are confidential and no information is provided to any one at any cost. ★ EXCLUSIVE PROFILes Are Safe and Consensual with Most Limits Respected ★ Service Available In: - HOME & HOTEL Star Hotel Service .In Call & Out call SeRvIcEs : ★ A-Level (star escort) ★ Strip-tease ★ BBBJ (Bareback Blowjob)Receive advanced sexual techniques in different mode make their life more pleasurable. ★ Spending time in hotel rooms ★ BJ (Blowjob Without a Condom) ★ Completion (Oral to completion) ★ Covered (Covered blowjob Without condom ★ANAL SERVICES.

👉 Amritsar Call Girl 👉📞 6367187148 👉📞 Just📲 Call Ruhi Call Girl Phone No Amri...

karishmasinghjnh

Model Call Girl Services in Delhi reach out to us at 🔝 9953056974 🔝✔️✔️ Our agency presents a selection of young, charming call girls available for bookings at Oyo Hotels. Experience high-class escort services at pocket-friendly rates, with our female escorts exuding both beauty and a delightful personality, ready to meet your desires. Whether it's Housewives, College girls, Russian girls, Muslim girls, or any other preference, we offer a diverse range of options to cater to your tastes. We provide both in-call and out-call services for your convenience. Our in-call location in Delhi ensures cleanliness, hygiene, and 100% safety, while our out-call services offer doorstep delivery for added ease. We value your time and money, hence we kindly request pic collectors, time-passers, and bargain hunters to refrain from contacting us. Our services feature various packages at competitive rates: One shot: ₹2000/in-call, ₹5000/out-call Two shots with one girl: ₹3500/in-call, ₹6000/out-call Body to body massage with sex: ₹3000/in-call Full night for one person: ₹7000/in-call, ₹10000/out-call Full night for more than 1 person: Contact us at 🔝 9953056974 🔝. for details Operating 24/7, we serve various locations in Delhi, including Green Park, Lajpat Nagar, Saket, and Hauz Khas near metro stations. For premium call girl services in Delhi 🔝 9953056974 🔝. Thank you for considering us!

CHEAP Call Girls in Saket (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICE

9953056974 Low Rate Call Girls In Saket, Delhi NCR

Mg Road Call Girls Service: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore Escorts Service Booking Contact Details :- WhatsApp Chat :- +91-7737669865 4-May-2024(SMW) Call Girls In Model Towh +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in , Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service NCRWelcome To Escorts Service – An All Over New Very Sexy Hot Call Girls Agency Service Escorts In South NCR’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

Mg Road Call Girls Service: 🍓 7737669865 🍓 High Profile Model Escorts | Banga...

amitlee9823

➥🔝 7737669865 🔝▻ Mathura Call-girls in Women Seeking Men 🔝Mathura🔝 Escorts Service Booking Contact Details :- WhatsApp Chat :- +91-7737669865 Call Girls In Model Towh +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in , Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service NCRWelcome To Escorts Service – An All Over New Very Sexy Hot Call Girls Agency Service Escorts In South NCR’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At #K09 Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

➥🔝 7737669865 🔝▻ Mathura Call-girls in Women Seeking Men 🔝Mathura🔝 Escorts...

amitlee9823

Saudi Arabia [ Abortion pills) Jeddah/riaydh/dammam/+966572737505☎️] cytotec tablets uses abortion pills 💊💊 How effective is the abortion pill? 💊💊 +966572737505) "Abortion pills in Jeddah" how to get cytotec tablets in Riyadh " Abortion pills in dammam*💊💊 The abortion pill is very effective. If you’re taking mifepristone and misoprostol, it depends on how far along the pregnancy is, and how many doses of medicine you take:💊💊 +966572737505) how to buy cytotec pills At 8 weeks pregnant or less, it works about 94-98% of the time. +966572737505[ 💊💊💊 At 8-9 weeks pregnant, it works about 94-96% of the time. +966572737505) At 9-10 weeks pregnant, it works about 91-93% of the time. +966572737505)💊💊 If you take an extra dose of misoprostol, it works about 99% of the time. At 10-11 weeks pregnant, it works about 87% of the time. +966572737505) If you take an extra dose of misoprostol, it works about 98% of the time. In general, taking both mifepristone and+966572737505 misoprostol works a bit better than taking misoprostol only. +966572737505 Taking misoprostol alone works to end the+966572737505 pregnancy about 85-95% of the time — depending on how far along the+966572737505 pregnancy is and how you take the medicine. +966572737505 The abortion pill usually works, but if it doesn’t, you can take more medicine or have an in-clinic abortion. +966572737505 When can I take the abortion pill?+966572737505 In general, you can have a medication abortion up to 77 days (11 weeks)+966572737505 after the first day of your last period. If it’s been 78 days or more since the first day of your last+966572737505 period, you can have an in-clinic abortion to end your pregnancy.+966572737505 Why do people choose the abortion pill? Which kind of abortion you choose all depends on your personal+966572737505 preference and situation. With+966572737505 medication+966572737505 abortion, some people like that you don’t need to have a procedure in a doctor’s office. You can have your medication abortion on your own+966572737505 schedule, at home or in another comfortable place that you choose.+966572737505 You get to decide who you want to be with during your abortion, or you can go it alone. Because+966572737505 medication abortion is similar to a miscarriage, many people feel like it’s more “natural” and less invasive. And some+966572737505 people may not have an in-clinic abortion provider close by, so abortion pills are more available to+966572737505 them. +966572737505 Your doctor, nurse, or health center staff can help you decide which kind of abortion is best for you. +966572737505 More questions from patients: Saudi Arabia+966572737505 CYTOTEC Misoprostol Tablets. Misoprostol is a medication that can prevent stomach ulcers if you also take NSAID medications. It reduces the amount of acid in your stomach, which protects your stomach lining. The brand name of this medication is Cytotec®.+966573737505) Unwanted Kit is a combination of two medici

Abortion pills in Doha Qatar (+966572737505 ! Get Cytotec

Abortion pills in Riyadh +966572737505 get cytotec

Call Girls Indiranagar Just Call 👗 7737669865 👗 Top Class Call Girl Service Bangalore Booking Contact Details :- WhatsApp Chat :- +91-7737669865 2-May-2024(SMW) Call Girls In Model Towh Bangalore +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in Bangalore NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in Bangalore, Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service Bangalore NCRWelcome To Bangalore Escorts Service – An All Over New Bangalore Very Sexy Hot Call Girls Agency Service Escorts In South BangaloreNCRBangalore’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Bangalore Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

Call Girls Indiranagar Just Call 👗 7737669865 👗 Top Class Call Girl Service B...

amitlee9823

Call Girls In Nandini Layout ☎ 7737669865 🥵 Book Your One night Stand Booking Contact Details :- WhatsApp Chat :- +91-7737669865 Call Girls In Model Towh +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in , Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service NCRWelcome To Escorts Service – An All Over New Very Sexy Hot Call Girls Agency Service Escorts In South NCR’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At #K09 Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

Call Girls In Nandini Layout ☎ 7737669865 🥵 Book Your One night Stand

amitlee9823

This project aims to predict whether a loan application will be approved or denied based on various factors such as applicant's income, credit score, loan amount, etc. Using a dataset containing historical loan application data, we employed machine learning algorithms to build a predictive model. The model was trained on features such as applicant's income, credit history, loan amount, loan term, and others. After training the model, we evaluated its performance using metrics like accuracy, precision, recall, and F1 score. The insights from this project can help financial institutions streamline their loan approval process and make informed decisions. Visit for more information: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

Predicting Loan Approval: A Data Science Project

Boston Institute of Analytics

Probability Grade 10 Third Quarter Lessons

JoseMangaJr1

Escorts Service Kumaraswamy Layout ☎ 7737669865☎ Book Your One night Stand (Bangalore) Booking Contact Details :- WhatsApp Chat :- +91-7737669865 2-May-2024(SMW) Call Girls In Model Towh Bangalore +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in Bangalore NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in Bangalore, Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service Bangalore NCRWelcome To Bangalore Escorts Service – An All Over New Bangalore Very Sexy Hot Call Girls Agency Service Escorts In South BangaloreNCRBangalore’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Bangalore Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

Escorts Service Kumaraswamy Layout ☎ 7737669865☎ Book Your One night Stand (B...

amitlee9823

Call Girls In Hsr Layout ☎ 7737669865 🥵 Book Your One night Stand Booking Contact Details :- WhatsApp Chat :- +91-7737669865 Call Girls In Model Towh +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in , Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service NCRWelcome To Escorts Service – An All Over New Very Sexy Hot Call Girls Agency Service Escorts In South NCR’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At #K09 Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

Call Girls In Hsr Layout ☎ 7737669865 🥵 Book Your One night Stand

amitlee9823

5CL-ADBA,5cladba, Chinese supplier, safety is guaranteed

amy56318795

hybrid Seed Production In Chilli & Capsicum.pptx

9to5mart

Junnasandra Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore Escorts Service Booking Contact Details :- WhatsApp Chat :- +91-7737669865 2-May-2024(SMW) Call Girls In Model Towh Bangalore +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in Bangalore NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in Bangalore, Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service Bangalore NCRWelcome To Bangalore Escorts Service – An All Over New Bangalore Very Sexy Hot Call Girls Agency Service Escorts In South BangaloreNCRBangalore’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Bangalore Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

Junnasandra Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore...

amitlee9823

➥🔝 7737669865 🔝▻ Dindigul Call-girls in Women Seeking Men 🔝Dindigul🔝 Escorts Service Booking Contact Details :- WhatsApp Chat :- +91-7737669865 Call Girls In Model Towh +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in , Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service NCRWelcome To Escorts Service – An All Over New Very Sexy Hot Call Girls Agency Service Escorts In South NCR’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At #K09 Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

➥🔝 7737669865 🔝▻ Dindigul Call-girls in Women Seeking Men 🔝Dindigul🔝 Escor...

amitlee9823

Ashok Vihar Call Girls in Delhi (–9953330565) Escort Service In Delhi NCR PROVIDE 100% REAL GIRLS ALL ARE GIRLS LOOKING MODELS AND RAM MODELS ALL GIRLS” INDIAN , RUSSIAN ,KASMARI ,PUNJABI HOT GIRLS AND MATURED HOUSE WIFE BOOKING ONLY DECENT GUYS AND GENTLEMAN NO FAKE PERSON FREE HOME SERVICE IN CALL FULL AC ROOM SERVICE IN SOUTH DELHI Ultimate Destination for finding a High Profile Independent Escorts in Delhi.Gurgaon.Noida..!.Like You Feel 100% Real Girl Friend Experience. We are High Class Delhi Escort Agency offering quality services with discretion. We only offer services to gentlemen people. We have lots of girls working with us like students, Russian, models, house wife, and much More We Provide Short Time and Full Night Service Call ☎☎+91–9953330565 ❤꧂ • In Call and Out Call Service in Delhi NCR • 3* 5* 7* Hotels Service in Delhi NCR • 24 Hours Available in Delhi NCR • Indian, Russian, Punjabi, Kashmiri Escorts • Real Models, College Girls, House Wife, Also Available • Short Time and Full Time Service Available • Hygienic Full AC Neat and Clean Rooms Avail. In Hotel 24 hours • Daily New Escorts Staff Available • Minimum to Maximum Range Available. Location;- Delhi, Gurgaon, NCR, Noida, and All Over in Delhi Hotel and Home Services HOTEL SERVICE AVAILABLE :-REDDISSON BLU,ITC WELCOM DWARKA,HOTEL-JW MERRIOTT,HOLIDAY INN MAHIPALPUR AIROCTY,CROWNE PLAZA OKHALA,EROSH NEHRU PLACE,SURYAA KALKAJI,CROWEN PLAZA ROHINI,SHERATON PAHARGANJ,THE AMBIENC,VIVANTA,SURAJKUND,ASHOKA CONTINENTAL , LEELA CHANKYAPURI,_ALL 3* 5* 7* STARTS HOTEL SERVICE BOOKING CALL Call WHATSAPP Call ☎+91–9953330565❤꧂ NIGHT SHORT TIME BOTH ARE AVAILABLE

Call Girls In Shalimar Bagh ( Delhi) 9953330565 Escorts Service

9953056974 Low Rate Call Girls In Saket, Delhi NCR

Call Girl In Dwarka ☎92055#41914 ¶¶ Indian,Russian Best Quality full Educated And Full Cooperative Independent Call Girls Escort Services In New Delhi- I Have Extremely Beautiful Broad Minded Cute Sexy & Hot Call Girls and Escorts, We Are Located in 3* 4* 5* Hotels in Delhi. Safe & Secure High Class Services Affordable Rate 100% Satisfaction, Unlimited Enjoyment. Any Time for Model/Teens Escort in Delhi High class luxury and premium escorts agency Indian Russian Call Girls In Delhi Booking Good High Profile Escorts (Call Girls) In Delhi 5 Star Hotel ,Incall Service,OutCall Service, We provide services by Call Girls,College Girls,Modals Get High Profile queens,Well Educated,Good Looking,Full Cooperative Model, Russian Models,Punjabi Girls Kashmeri Girls Services etc… We Provide Hottest Female With Safe And Consensual With Most Limits Respected Complete Satisfaction Guaranteed…Service. Call Me Spacial For Including Incall//outcall Service In New Delhi Indian Russian Escorts Service

Call Girls in Sarai Kale Khan Delhi 💯 Call Us 🔝9205541914 🔝( Delhi) Escorts S...

Delhi Call girls

Call Girls In Attibele ☎ 7737669865 🥵 Book Your One night Stand Booking Contact Details :- WhatsApp Chat :- +91-7737669865 Call Girls In Model Towh +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in , Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service NCRWelcome To Escorts Service – An All Over New Very Sexy Hot Call Girls Agency Service Escorts In South NCR’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At #K09 Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

Call Girls In Attibele ☎ 7737669865 🥵 Book Your One night Stand

amitlee9823

Call Girls Bommasandra Just Call 👗 7737669865 👗 Top Class Call Girl Service Bangalore Booking Contact Details :- WhatsApp Chat :- +91-7737669865 2-May-2024(SMW) Call Girls In Model Towh Bangalore +91-7737669865 !! Best Woman Seeking Man Call Girls Service, Escorts Service in Home Hotel in Bangalore NCR 24 Hours Available Service Call Girls, Contact Us +91-7737669865 (Any Time. Any Where) Call Girls in Bangalore, Noida, Gurgaon, Ghaziabad,Sexy Indian Female Escorts Service Bangalore NCRWelcome To Bangalore Escorts Service – An All Over New Bangalore Very Sexy Hot Call Girls Agency Service Escorts In South BangaloreNCRBangalore’s No. 1 High Profile Independent Female Escorts Service. We Provide Good Quality Educated Profile At Very Regnebal Price 100% Safe And Original.We Are Provide Escorts Service All OYO Hotels ,3*,4*,5* Star Hotel And Home Flat, Apartment. Guest-House. Services In -Call And Out – Call Both Are Services Available. 24Hrs. Any Time Any Where. In All Over Bangalore Noida Gurgaon Ghaziabad Faridabad.More Information And Contact Profile Real Pic Visit Our Website City Wise Escorts Service Agency.Good Looking Cheap And Best Models Girls U Can Get Best Click On Link……Night Call Girls Now In Hotel Le Meridien Gurgaon Near Female Escort One Shot — 5000/in call (time 1 hour), 6000/out call Two shot with one girl — 8000/in call (time 2 hour), 10000/out call Body to body massage with sex- 8000/in call (time 1 hour) Full night Service for one person– 12000/in call, 13000/out call (shot limit 3-4 shots) Full night Service for more than 1 person — please contact Us —7737669865 We are available 24*7 all days of the year. Call us — 7737669865 Thank you for Visiting.

Call Girls Bommasandra Just Call 👗 7737669865 👗 Top Class Call Girl Service B...

amitlee9823

(Vivek)Call Us, 8448380779,Call girls in Delhi NCr – We Offer best in class call girls. escort Service At Affordable Price At low Rate with Space Night 8000 We Are One Of The Oldest Escort and Call girls Agencies in Delhi. You Will Find That Our Female Escorts Are Full Of Fun, Sexy And They Would Love Enjoy Your Company. We Have A Fantastic Selection Of Escort Ladies Available For In-Calls As Well As Out-Calls. Our Escorts Are Not Only Beautiful But All Have Great Personalities Making Them The Perfect Companion For Any Occasion. In-Call:- You Can Come At Our Place in Delhi Our place Which Is Very Clean Hygienic 100% safe Accommodation. Out-Call:- You have To Come Pick The Girl From My Place We Are Also Provide Door Step Services (Delhi Ncr, Noida, Gurgaon, Faridabad, Ghaziabad Note:- Pic Collectors Time Passers Bargainers Stay Away As We Respect The Value For Your Money Time And Expect The Same From You Hygienic:- Full Ac room And Clean Rooms Available In Hotel 24 * 7 Hourly In Delhi NCR More Details, With WhatsApp Number, +91-8448380779

BDSM⚡Call Girls in Mandawali Delhi >༒8448380779 Escort Service

Delhi Call girls

Dernier (20)

👉 Amritsar Call Girl 👉📞 6367187148 👉📞 Just📲 Call Ruhi Call Girl Phone No Amri...

CHEAP Call Girls in Saket (-DELHI )🔝 9953056974🔝(=)/CALL GIRLS SERVICE

Mg Road Call Girls Service: 🍓 7737669865 🍓 High Profile Model Escorts | Banga...

➥🔝 7737669865 🔝▻ Mathura Call-girls in Women Seeking Men 🔝Mathura🔝 Escorts...

Abortion pills in Doha Qatar (+966572737505 ! Get Cytotec

Call Girls Indiranagar Just Call 👗 7737669865 👗 Top Class Call Girl Service B...

Call Girls In Nandini Layout ☎ 7737669865 🥵 Book Your One night Stand

Predicting Loan Approval: A Data Science Project

Probability Grade 10 Third Quarter Lessons

Escorts Service Kumaraswamy Layout ☎ 7737669865☎ Book Your One night Stand (B...

Call Girls In Hsr Layout ☎ 7737669865 🥵 Book Your One night Stand

5CL-ADBA,5cladba, Chinese supplier, safety is guaranteed

hybrid Seed Production In Chilli & Capsicum.pptx

Junnasandra Call Girls: 🍓 7737669865 🍓 High Profile Model Escorts | Bangalore...

➥🔝 7737669865 🔝▻ Dindigul Call-girls in Women Seeking Men 🔝Dindigul🔝 Escor...

Call Girls In Shalimar Bagh ( Delhi) 9953330565 Escorts Service

Call Girls in Sarai Kale Khan Delhi 💯 Call Us 🔝9205541914 🔝( Delhi) Escorts S...

Call Girls In Attibele ☎ 7737669865 🥵 Book Your One night Stand

Call Girls Bommasandra Just Call 👗 7737669865 👗 Top Class Call Girl Service B...

BDSM⚡Call Girls in Mandawali Delhi >༒8448380779 Escort Service

ModelDB: A System to Manage Machine Learning Models: Spark Summit East talk by Manasi Vartak

1. ModelDB: A system to manage machine learning models Manasi Vartak PhD Student, MIT DB Group

2. People Manasi Vartak PhD student, MIT Srinidhi Viswanathan MEng, MIT Samuel Madden Faculty, MIT Matei Zaharia Faculty, Stanford Harihar Subramanyam MEng, MIT Wei-En Lee MEng student, MIT

3. Building a default prediction algorithm Profession Credit History Risk of Default Politician Reasonable 0.3 Struggling artist Poor 0.7 Investor Has more money than our company 0.0 … … … … Barack Obama Lindsay Lohan Warren Buffet

4. Accuracy: 62% Model 1

5. Model 3 RandomForestClassifier val udf1: (Int => Int) = (delayed..) df.withColumn(“timesDelayed”, udf1)

6. RandomForestClassifier df.withColumn(“timesDelayed”, udf1) .withColumn(“percentPaid”, udf2) val lrGrid = new ParamGridBuilder() .addGrid(rf.maxDepth, Array(5, 10, 15)) .addGrid(rf.numTrees, Array(50, 100)) Model 5 credit-default-clean.csv

7. df.withColumn(“timesDelayed”, udf1) .withColumn(“percentPaid”, udf2) .withColumn(“creditUsed”, udf3) … val lrGrid = new ParamGridBuilder() .addGrid(lr.elasticNetParam, Array(0.01, 0.1, 0.5, 0.7)) val scaler = new StandardScaler() .setInputCol(“features”) … val labelIndexer1 = new LabelIndexer() val labelIndexer2 = new LabelIndexer() … Model 50 val udf1: (Int => Int) = (delayed..) val udf2: (String, Int) = … credit-default-clean.csv

8. No one in here tracks (all of) their models …and this is not unusual I’m willing to bet…

9. Why is this a problem? • No record of experiments • Insights lost along the way • Difﬁcult to reproduce results • Cannot search for or query models • Difﬁcult to collaborate Did my colleague do that already? How did normalization affect my ROC? How does someone review your model? Where’s the LR model I tried last week with featureX? What params did I use?

10. Model Management track, store and index modeling artifacts so that they may subsequently be reproduced, shared, queried, and analyzed

11. ModelDB: a system to manage machine learning models http://modeldb.csail.mit.edu

12. ModelDB: an end-to-end model management system Model artifact Storage & Versioning Query Ingest models, metadata Collaboration, Reproducibilitytrack store & index query, reproduce++

13. Demo

14. ModelDB w/ scikit-learn

15. ModelDB Architecture & Design Decisions 1. Support for diverse languages and environments 2. Minimal changes to existing workflows 3. Rich visual interface 4. Support for complex queries spark.ml scikit-learn ModelDB Backend Storage thrift Scala Python … ModelDB Frontend: vis + query Native Client Events

16. ModelDB Features • Experiment tracking • Versioning • Reproducibility • Comparisons, queries, search • Collaboration Log models, params, pipelines etc. via ModelDB API Model search, query, comparison via frontend Central repository of models Review models, annotate All pipeline details, params logged Every modeling run = version

17. Ongoing Work • Uniﬁed querying of modeling artifacts • Mining data in ModelDB • Model monitoring and retraining

18. ModelDB available now! http://modeldb.csail.mit.edu *MIT License

19. ModelDB available now! • Download, try it out! • Tell us what you think; what can we do better? • Contribute! (see Issues on repo for some ideas)

20. ModelDB: a system to manage machine learning models mvartak@csail.mit.edu | @DataCereal http://modeldb.csail.mit.edu

ModelDB: A System to Manage Machine Learning Models: Spark Summit East talk by Manasi Vartak

Recommandé

Recommandé

Contenu connexe

Tendances

Tendances (20)

Similaire à ModelDB: A System to Manage Machine Learning Models: Spark Summit East talk by Manasi Vartak

Similaire à ModelDB: A System to Manage Machine Learning Models: Spark Summit East talk by Manasi Vartak (20)

Plus de Spark Summit

Plus de Spark Summit (20)

Dernier

Dernier (20)

ModelDB: A System to Manage Machine Learning Models: Spark Summit East talk by Manasi Vartak