Productionzing ML Model Using MLflow Model Serving

•

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Productionzing ML Models are needs to ensure model integrity while it efficiently replicate runtime environments across servers besides it keep track of how each of our models were created. It helps us better trace the root cause of changes and issues over time as we acquire new data and update our model. We have greater accountability over our models and the results they generate. MLflow Model Serving delivers cost-effective and on-click deployment of model for real-time inferences. Also the Model Version deployed in the Model Serving can also be conveniently managed with MLflow Model Registry. We will going to cover following topics Deployment, Consumption and Monitoring. For deployment, we will demo the different version deployment and validate the deployment. For consumption, we demo connecting power bi and generate prediction report using ML Model deployed in MLflow serving. Lastly will wrap up with managing the MLflow serving like, access rights and monitoring capabilities.

Données & analyses

Productionzing ML Model using
MLflow Model Serving
• Nagaraj Sengodan
• Nitin Raj Soundararajan

S Nagaraj has helped build the larger
enterprise-wide distributed systems.
Senior Manager in Architecture and
Engineering team, helping envision
and deliver the future for enterprise
analytics via Databricks and Azure
Synapse.
Nitin Raj Soundararajan is a technical
consultant focusing on advanced data
analytics, data engineering, cloud
scale analytics and data science.

Agenda
§ MLflow
§ MLflow Serving
§ Manage Served Versions
§ Monitor Served Models
§ Customize Serving Cluster
§ Q & A

MLFlow
• Open machine learning platform
• Works with any ML Library & Language
• Runs the same way anywhere (e.g. any cloud)
• Open interface design (use with any code you already
have)

MLFlow
Machine Learning
lifecycle
MLflow Tracking - Record and query experiments: code, data, config, and
results
MLflow Projects - Package data science code in a format to reproduce runs
on any platform
MLflow Models - Deploy machine learning models in diverse serving
environments
MLflow Registry - Store, annotate, discover, and manage models in a
central repository

MLFlow
AutoML
End-to-End ML Lifecycle
ML Runtime and
Environments
Batch and
Streaming
Online Serving
Data Science Workspace
Prep Data Build Model Deploy/Monitor Model
Open,
pluggable
architecture

MLFlow – Model Serving
Prep Data Build Model Deploy/Monitor Model
AutoML
End-to-End ML Lifecycle
Batch and
Streaming
Online Serving
Data Science Workspace
Open,
pluggable
architecture
ML Runtime and
Environments

MLflow Serving
• Expose Mlflow model predictions as REST endpoint
• Small cluster is automatically provisioned
• HTTP endpoint publicly exposed
• Limited production capability for now
• For now, intended for light loads and testing

Model Serving from Model Registry
Models
Flavor 2
Flavor 1
Custom
Models

Model Serving from Model Registry
Models Tracking
Flavor 2
Flavor 1
Custom
Models
Parameters Metrics Artifacts
Models
Metadata

Model Serving from Model Registry
Staging Production Archived
Data Scientists Deployment Engineers
v1
v2
v3
Models Tracking
Flavor 2
Flavor 1
Model Registry
Custom
Models
Parameters Metrics Artifacts
Models
Metadata

Manage Served Versions
• All active (non-archived) model versions are deployed
• Manage model access rights
• Source deployed model versions
• Source via UI
• Source via REST API request

Monitor Served Models
• Displays status indicators for the serving cluster as well as
individual model versions
• Inspect the state of the serving cluster - displays a list of
all serving events for this model
• To inspect the state of a single model version

Customize Serving Cluster
• Modify the memory size and number of cores of a serving
cluster
• Ability to add the tags
• Ability to edit or delete an existing tags

Demo Code & Deck
• https://github.com/KRSNagaraj/Mod
elServing-DataAISummit2021

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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

Databricks

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

Databricks

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

Databricks

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

Databricks

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

Databricks

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

Databricks

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

Databricks

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

Databricks

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

Databricks

Plus de Databricks (20)

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Scaling and Unifying SciKit Learn and Apache Spark Pipelines

Sawtooth Windows for Feature Aggregations

Redis + Apache Spark = Swiss Army Knife Meets Kitchen Sink

Re-imagine Data Monitoring with whylogs and Spark

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

Processing Large Datasets for ADAS Applications using Apache Spark

Massive Data Processing in Adobe Using Delta Lake

Dernier

Gen AI on Enterprise Cloud Apache NiFi Milvus Apache Kafka Apache Flink Cloudera Machine Learning Cloudera DataFlow https://medium.com/@tspann/building-a-milvus-connector-for-nifi-34372cb3c7fa https://www.meetup.com/futureofdata-princeton/events/300737266/ https://lu.ma/q7pcfyjn?source=post_page-----34372cb3c7fa--------------------------------&tk=TTyakY If you're interested in working with Generative AI on the cloud, this virtual workshop is for you. Tim Spann from Cloudera and Yujian Tang from Zilliz will cover how you can implement your own GenAI workflows on the cloud at enterprise scale. 9:00 - 9:05: Intro 9:05 - 9:15: What is Milvus 9:15 - 9:25: Cloudera Development Platform 9:25 - 10:00: Demo Location https://www.youtube.com/watch?v=IfWIzKsoHnA https://github.com/tspannhw/SpeakerProfile https://www.linkedin.com/in/yujiantang/

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Productionzing ML Model Using MLflow Model Serving

1. Productionzing ML Model using MLflow Model Serving • Nagaraj Sengodan • Nitin Raj Soundararajan

2. S Nagaraj has helped build the larger enterprise-wide distributed systems. Senior Manager in Architecture and Engineering team, helping envision and deliver the future for enterprise analytics via Databricks and Azure Synapse. Nitin Raj Soundararajan is a technical consultant focusing on advanced data analytics, data engineering, cloud scale analytics and data science.

3. Agenda § MLflow § MLflow Serving § Manage Served Versions § Monitor Served Models § Customize Serving Cluster § Q & A

4. MLFlow • Open machine learning platform • Works with any ML Library & Language • Runs the same way anywhere (e.g. any cloud) • Open interface design (use with any code you already have)

5. MLFlow Machine Learning lifecycle MLflow Tracking - Record and query experiments: code, data, config, and results MLflow Projects - Package data science code in a format to reproduce runs on any platform MLflow Models - Deploy machine learning models in diverse serving environments MLflow Registry - Store, annotate, discover, and manage models in a central repository

6. MLFlow AutoML End-to-End ML Lifecycle ML Runtime and Environments Batch and Streaming Online Serving Data Science Workspace Prep Data Build Model Deploy/Monitor Model Open, pluggable architecture

7. MLFlow – Model Serving Prep Data Build Model Deploy/Monitor Model AutoML End-to-End ML Lifecycle Batch and Streaming Online Serving Data Science Workspace Open, pluggable architecture ML Runtime and Environments

8. Serving

9. MLflow Serving • Expose Mlflow model predictions as REST endpoint • Small cluster is automatically provisioned • HTTP endpoint publicly exposed • Limited production capability for now • For now, intended for light loads and testing

10. Model Serving from Model Registry Models Flavor 2 Flavor 1 Custom Models

11. Model Serving from Model Registry Models Tracking Flavor 2 Flavor 1 Custom Models Parameters Metrics Artifacts Models Metadata

12. Model Serving from Model Registry Staging Production Archived Data Scientists Deployment Engineers v1 v2 v3 Models Tracking Flavor 2 Flavor 1 Model Registry Custom Models Parameters Metrics Artifacts Models Metadata

13. Model serving from Model Registry Staging Production Archived Data Scientists Deployment Engineers v1 v2 v3 Models Tracking Flavor 2 Flavor 1 Model Registry Custom Models In-Line Code Containers Batch & Stream Scoring Cloud Inference Services OSS Serving Solutions Serving Parameters Metrics Artifacts Models Metadata

14. MLFlow Serving

15. Demo

16. Manage Served Versions • All active (non-archived) model versions are deployed • Manage model access rights • Source deployed model versions • Source via UI • Source via REST API request

17. Monitor Served Models • Displays status indicators for the serving cluster as well as individual model versions • Inspect the state of the serving cluster - displays a list of all serving events for this model • To inspect the state of a single model version

18. Customize Serving Cluster • Modify the memory size and number of cores of a serving cluster • Ability to add the tags • Ability to edit or delete an existing tags

19. Demo Code & Deck • https://github.com/KRSNagaraj/Mod elServing-DataAISummit2021

20. Q & A

21. Feedback Your feedback is important to us. Don’t forget to rate and review the sessions.

Productionzing ML Model Using MLflow Model Serving

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