CI/DC in MLOps by J.B. Hunt

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CI/DC in MLOps by J.B. Hunt - Implementing a Framework for Self-Service Experimentation and Deployment at Scale by Wesly Clark and Cara Phillips

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CI/CD in MLOps
Wesly Clark and Cara Phillips
In partnership with
Implementing a Framework for Self-Service
Experimentation and Deployment at Scale

2
J.B. HUNT AT-A-GLANCE
400+
locations across
U.S., Canada,
Mexico, & Asia
17,000+
tractors
and ICs
133,000+
trailing units
30,000+
employees
$9B
trailing 12-
month
revenue
94,000+
carriers
59-year legacy as a leader in the transportation and logistics industry

WE HELP
Organizations understand how emerging digital technologies
can enable new business models and transform existing one
Intelligent Applications
AI & Machine Learning
Internet of Things
BI & Big Data
WE BRING
Deep expertise across the Microsoft data analytics,
application development and productivity platforms to enable
new ways of work
Recent Case Studies
WE DELIVER
Engagements to help organizations accomplish their
business goals
Projects Strategy Training
Quick
Starts
Design
Sprints
Managed
Services
WE BUILD
Solutions that help transform organizations through
improved insight and productivity
artisconsulting.com 972.702.9500 artis_info@artisconsulting.com
Business Intelligence
Big Data
AI & Machine Learning
Intelligent Applications
Power Apps
IoT
Founded in 2002, Artis Consulting is a professional services firm dedicated to delivering technology solutions that provide the right information, to the right person, at the right time.

PRESENTATION AGENDA
J.B. HUNT TRANSPORT
• Mission Statement
• MLOps Lifecycle
• Motivating Values/Concepts
• Architecture Diagrams
• Clusters and Libraries Tech Demo
• Job Deployment Tech Demo
• Recap

Creating the most efficient transportation network in North America
Mission Statement:
Augment
Analysis
Empower
Expertise
Remove
Redundancy
Automate
Action
Detect
Direction
Predict
Possibilities

MLOps High Level Lifecycle
ML Environment
Creation
Data Analysis
Feature/Model
Selection
Model
Registration
Experimentation/
Monitoring
Job Deployment/
(Re)Training
Model
Deployment/
Serving
Data Expectations/
Feature Store
Data Collection/
Ingestion
Continuously Develop/Iterate Models

Motivating Values/Concepts
Predictable Repeatable
Automated Clearly Defined
Environments
Secure/Self-Service Platform and
Cloud Agnostic

Framework Architecture Diagrams

PRESENTATION AGENDA
J.B. HUNT TRANSPORT
• Mission Statement
• MLOps Lifecycle
• Motivating Values/Concepts
• Architecture Diagrams
• Clusters and Libraries Tech Demo
• Job Deployment Tech Demo
• Recap

THANK YOU

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In this talk, I would like to introduce an open-source tool built by our team that simplifies the data conversion from Apache Spark to deep learning frameworks. Imagine you have a large dataset, say 20 GBs, and you want to use it to train a TensorFlow model. Before feeding the data to the model, you need to clean and preprocess your data using Spark. Now you have your dataset in a Spark DataFrame. When it comes to the training part, you may have the problem: How can I convert my Spark DataFrame to some format recognized by my TensorFlow model? The existing data conversion process can be tedious. For example, to convert an Apache Spark DataFrame to a TensorFlow Dataset file format, you need to either save the Apache Spark DataFrame on a distributed filesystem in parquet format and load the converted data with third-party tools such as Petastorm, or save it directly in TFRecord files with spark-tensorflow-connector and load it back using TFRecordDataset. Both approaches take more than 20 lines of code to manage the intermediate data files, rely on different parsing syntax, and require extra attention for handling vector columns in the Spark DataFrames. In short, all these engineering frictions greatly reduced the data scientists’ productivity. The Databricks Machine Learning team contributed a new Spark Dataset Converter API to Petastorm to simplify these tedious data conversion process steps. With the new API, it takes a few lines of code to convert a Spark DataFrame to a TensorFlow Dataset or a PyTorch DataLoader with default parameters. In the talk, I will use an example to show how to use the Spark Dataset Converter to train a Tensorflow model and how simple it is to go from single-node training to distributed training on Databricks.

Simplify Data Conversion from Spark to TensorFlow and PyTorch

Simplify Data Conversion from Spark to TensorFlow and PyTorch

Simplify Data Conversion from Spark to TensorFlow and PyTorch

There is no doubt Kubernetes has emerged as the next generation of cloud native infrastructure to support a wide variety of distributed workloads. Apache Spark has evolved to run both Machine Learning and large scale analytics workloads. There is growing interest in running Apache Spark natively on Kubernetes. By combining the flexibility of Kubernetes and scalable data processing with Apache Spark, you can run any data and machine pipelines on this infrastructure while effectively utilizing resources at disposal. In this talk, Rajesh Thallam and Sougata Biswas will share how to effectively run your Apache Spark applications on Google Kubernetes Engine (GKE) and Google Cloud Dataproc, orchestrate the data and machine learning pipelines with managed Apache Airflow on GKE (Google Cloud Composer). Following topics will be covered: – Understanding key traits of Apache Spark on Kubernetes- Things to know when running Apache Spark on Kubernetes such as autoscaling- Demonstrate running analytics pipelines on Apache Spark orchestrated with Apache Airflow on Kubernetes cluster.

Scaling your Data Pipelines with Apache Spark on Kubernetes

Scaling your Data Pipelines with Apache Spark on Kubernetes

Scaling your Data Pipelines with Apache Spark on Kubernetes

Pipelines have become ubiquitous, as the need for stringing multiple functions to compose applications has gained adoption and popularity. Common pipeline abstractions such as “fit” and “transform” are even shared across divergent platforms such as Python Scikit-Learn and Apache Spark. Scaling pipelines at the level of simple functions is desirable for many AI applications, however is not directly supported by Ray’s parallelism primitives. In this talk, Raghu will describe a pipeline abstraction that takes advantage of Ray’s compute model to efficiently scale arbitrarily complex pipeline workflows. He will demonstrate how this abstraction cleanly unifies pipeline workflows across multiple platforms such as Scikit-Learn and Spark, and achieves nearly optimal scale-out parallelism on pipelined computations. Attendees will learn how pipelined workflows can be mapped to Ray’s compute model and how they can both unify and accelerate their pipelines with Ray.

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

Scaling and Unifying SciKit Learn and Apache Spark Pipelines

In this talk about zipline, we will introduce a new type of windowing construct called a sawtooth window. We will describe various properties about sawtooth windows that we utilize to achieve online-offline consistency, while still maintaining high-throughput, low-read latency and tunable write latency for serving machine learning features.We will also talk about a simple deployment strategy for correcting feature drift – due operations that are not “abelian groups”, that operate over change data.

Sawtooth Windows for Feature Aggregations

Sawtooth Windows for Feature Aggregations

Sawtooth Windows for Feature Aggregations

We want to present multiple anti patterns utilizing Redis in unconventional ways to get the maximum out of Apache Spark.All examples presented are tried and tested in production at Scale at Adobe. The most common integration is spark-redis which interfaces with Redis as a Dataframe backing Store or as an upstream for Structured Streaming. We deviate from the common use cases to explore where Redis can plug gaps while scaling out high throughput applications in Spark. Niche 1 : Long Running Spark Batch Job – Dispatch New Jobs by polling a Redis Queue · Why? o Custom queries on top a table; We load the data once and query N times · Why not Structured Streaming · Working Solution using Redis Niche 2 : Distributed Counters · Problems with Spark Accumulators · Utilize Redis Hashes as distributed counters · Precautions for retries and speculative execution · Pipelining to improve performance

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

In the era of microservices, decentralized ML architectures and complex data pipelines, data quality has become a bigger challenge than ever. When data is involved in complex business processes and decisions, bad data can, and will, affect the bottom line. As a result, ensuring data quality across the entire ML pipeline is both costly, and cumbersome while data monitoring is often fragmented and performed ad hoc. To address these challenges, we built whylogs, an open source standard for data logging. It is a lightweight data profiling library that enables end-to-end data profiling across the entire software stack. The library implements a language and platform agnostic approach to data quality and data monitoring. It can work with different modes of data operations, including streaming, batch and IoT data. In this talk, we will provide an overview of the whylogs architecture, including its lightweight statistical data collection approach and various integrations. We will demonstrate how the whylogs integration with Apache Spark achieves large scale data profiling, and we will show how users can apply this integration into existing data and ML pipelines.

Re-imagine Data Monitoring with whylogs and Spark

Re-imagine Data Monitoring with whylogs and Spark

Re-imagine Data Monitoring with whylogs and Spark

Machine learning (ML) models are typically part of prediction queries that consist of a data processing part (e.g., for joining, filtering, cleaning, featurization) and an ML part invoking one or more trained models. In this presentation, we identify significant and unexplored opportunities for optimization. To the best of our knowledge, this is the first effort to look at prediction queries holistically, optimizing across both the ML and SQL components. We will present Raven, an end-to-end optimizer for prediction queries. Raven relies on a unified intermediate representation that captures both data processing and ML operators in a single graph structure. This allows us to introduce optimization rules that (i) reduce unnecessary computations by passing information between the data processing and ML operators (ii) leverage operator transformations (e.g., turning a decision tree to a SQL expression or an equivalent neural network) to map operators to the right execution engine, and (iii) integrate compiler techniques to take advantage of the most efficient hardware backend (e.g., CPU, GPU) for each operator. We have implemented Raven as an extension to Spark’s Catalyst optimizer to enable the optimization of SparkSQL prediction queries. Our implementation also allows the optimization of prediction queries in SQL Server. As we will show, Raven is capable of improving prediction query performance on Apache Spark and SQL Server by up to 13.1x and 330x, respectively. For complex models, where GPU acceleration is beneficial, Raven provides up to 8x speedup compared to state-of-the-art systems. As part of the presentation, we will also give a demo showcasing Raven in action.

Raven: End-to-end Optimization of ML Prediction Queries

Raven: End-to-end Optimization of ML Prediction Queries

Raven: End-to-end Optimization of ML Prediction Queries

Semantic segmentation is the classification of every pixel in an image/video. The segmentation partitions a digital image into multiple objects to simplify/change the representation of the image into something that is more meaningful and easier to analyze [1][2]. The technique has a wide variety of applications ranging from perception in autonomous driving scenarios to cancer cell segmentation for medical diagnosis. Exponential growth in the datasets that require such segmentation is driven by improvements in the accuracy and quality of the sensors generating the data extending to 3D point cloud data. This growth is further compounded by exponential advances in cloud technologies enabling the storage and compute available for such applications. The need for semantically segmented datasets is a key requirement to improve the accuracy of inference engines that are built upon them. Streamlining the accuracy and efficiency of these systems directly affects the value of the business outcome for organizations that are developing such functionalities as a part of their AI strategy. This presentation details workflows for labeling, preprocessing, modeling, and evaluating performance/accuracy. Scientists and engineers leverage domain-specific features/tools that support the entire workflow from labeling the ground truth, handling data from a wide variety of sources/formats, developing models and finally deploying these models. Users can scale their deployments optimally on GPU-based cloud infrastructure to build accelerated training and inference pipelines while working with big datasets. These environments are optimized for engineers to develop such functionality with ease and then scale against large datasets with Spark-based clusters on the cloud.

Processing Large Datasets for ADAS Applications using Apache Spark

Processing Large Datasets for ADAS Applications using Apache Spark

Processing Large Datasets for ADAS Applications using Apache Spark

At Adobe Experience Platform, we ingest TBs of data every day and manage PBs of data for our customers as part of the Unified Profile Offering. At the heart of this is a bunch of complex ingestion of a mix of normalized and denormalized data with various linkage scenarios power by a central Identity Linking Graph. This helps power various marketing scenarios that are activated in multiple platforms and channels like email, advertisements etc. We will go over how we built a cost effective and scalable data pipeline using Apache Spark and Delta Lake and share our experiences. What are we storing? Multi Source – Multi Channel Problem Data Representation and Nested Schema Evolution Performance Trade Offs with Various formats Go over anti-patterns used (String FTW) Data Manipulation using UDFs Writer Worries and How to Wipe them Away Staging Tables FTW Datalake Replication Lag Tracking Performance Time!

Massive Data Processing in Adobe Using Delta Lake

Massive Data Processing in Adobe Using Delta Lake

Massive Data Processing in Adobe Using Delta Lake

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Stage Level Scheduling Improving Big Data and AI Integration

Stage Level Scheduling Improving Big Data and AI Integration

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Simplify Data Conversion from Spark to TensorFlow and PyTorch

Simplify Data Conversion from Spark to TensorFlow and PyTorch

Simplify Data Conversion from Spark to TensorFlow and PyTorch

Scaling your Data Pipelines with Apache Spark on Kubernetes

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Scaling and Unifying SciKit Learn and Apache Spark Pipelines

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Sawtooth Windows for Feature Aggregations

Sawtooth Windows for Feature Aggregations

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Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Re-imagine Data Monitoring with whylogs and Spark

Re-imagine Data Monitoring with whylogs and Spark

Re-imagine Data Monitoring with whylogs and Spark

Raven: End-to-end Optimization of ML Prediction Queries

Raven: End-to-end Optimization of ML Prediction Queries

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Processing Large Datasets for ADAS Applications using Apache Spark

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DATA SUMMIT 24 Building Real-Time Pipelines With FLaNK

DATA SUMMIT 24 Building Real-Time Pipelines With FLaNK

DATA SUMMIT 24 Building Real-Time Pipelines With FLaNK

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DATA SUMMIT 24 Building Real-Time Pipelines With FLaNK

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1. CI/CD in MLOps Wesly Clark and Cara Phillips In partnership with Implementing a Framework for Self-Service Experimentation and Deployment at Scale

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4. PRESENTATION AGENDA J.B. HUNT TRANSPORT • Mission Statement • MLOps Lifecycle • Motivating Values/Concepts • Architecture Diagrams • Clusters and Libraries Tech Demo • Job Deployment Tech Demo • Recap

5. Creating the most efficient transportation network in North America Mission Statement: Augment Analysis Empower Expertise Remove Redundancy Automate Action Detect Direction Predict Possibilities

6. MLOps High Level Lifecycle ML Environment Creation Data Analysis Feature/Model Selection Model Registration Experimentation/ Monitoring Job Deployment/ (Re)Training Model Deployment/ Serving Data Expectations/ Feature Store Data Collection/ Ingestion Continuously Develop/Iterate Models

7. Motivating Values/Concepts Predictable Repeatable Automated Clearly Defined Environments Secure/Self-Service Platform and Cloud Agnostic

8. Framework Architecture Diagrams

9. PRESENTATION AGENDA J.B. HUNT TRANSPORT • Mission Statement • MLOps Lifecycle • Motivating Values/Concepts • Architecture Diagrams • Clusters and Libraries Tech Demo • Job Deployment Tech Demo • Recap