Productionizing Machine Learning Pipelines with Databricks and Azure ML

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Deployment of modern machine learning applications can require a significant amount of time, resources, and experience to design and implement – thus introducing overhead for small-scale machine learning projects.

Datos y análisis

Productionizing Machine Learning
Pipelines with Databricks & Azure ML
Trace Smith & Amirhessam Tahmassebi
Data Scientist - ExxonMobil

Agenda
Motivation
Tutorial Overview
Key Learnings
Live Tutorial

Machine Learning Templates
▪ Deployment of Machine Learning Applications
▪ Significant amount of time moving from dev to production
▪ Resources and experience to design and implement
▪ Developing Reproducible Frameworks
▪ Quickly jumpstart and accelerate Data Science projects into production
▪ Series of code repositories with production-ready code templates
▪ Developed around previous use-cases
▪ Segment into infrastructure code and business logic
▪ Data Scientist more focused on developing models
▪ Template Example
▪ Integrating Databricks with Azure Machine Learning and Azure DevOps for end-to-end model deployment
Model ManagementModel TrainingData Ingestion
Azure Machine
Learning
Azure
Pipelines

Tutorial Overview
source: https://www.kaggle.com/c/dogs-vs-cats
▪ Deep Learning: Computer Vision
▪ Image Classification – Convolution Neural Network
▪ Libraries: Tensorflow & PyTorch
▪ Dataset: Cats and Dog
▪ Open source dataset
▪ Reference: https://www.microsoft.com/en-us/download/
▪ Total Images: 25,000
▪ Images stored in Azure Blob Storage
▪ Mounted to Databricks Workspace
Source: https://cs231n.github.io/convolutional-networks/

Key Learnings
▪ Custom Containerization
▪ Databricks-connect
▪ Deep Learning (CNN)
▪ Parallelized Gridsearch
▪ Model Management w/ MLflow
▪ AzureML Workspace
▪ AzureML Pipelines
▪ Azure Model Registry
▪ Custom inference script
▪ Webservice Deployment
Azure Machine LearningMachine Learning
• Azure DevOps
• Code Quality Checks
▪ Automated Unit Testing
▪ Azure Pipelines
▪ Continuous deployment
Deployment

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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

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Processing Large Datasets for ADAS Applications using Apache Spark

Massive Data Processing in Adobe Using Delta Lake

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Generative AI on Enterprise Cloud with NiFi and Milvus

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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

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Productionizing Machine Learning Pipelines with Databricks and Azure ML

2. Productionizing Machine Learning Pipelines with Databricks & Azure ML Trace Smith & Amirhessam Tahmassebi Data Scientist - ExxonMobil

3. Agenda Motivation Tutorial Overview Key Learnings Live Tutorial

4. Machine Learning Templates ▪ Deployment of Machine Learning Applications ▪ Significant amount of time moving from dev to production ▪ Resources and experience to design and implement ▪ Developing Reproducible Frameworks ▪ Quickly jumpstart and accelerate Data Science projects into production ▪ Series of code repositories with production-ready code templates ▪ Developed around previous use-cases ▪ Segment into infrastructure code and business logic ▪ Data Scientist more focused on developing models ▪ Template Example ▪ Integrating Databricks with Azure Machine Learning and Azure DevOps for end-to-end model deployment Model ManagementModel TrainingData Ingestion Azure Machine Learning Azure Pipelines

5. Tutorial Overview source: https://www.kaggle.com/c/dogs-vs-cats ▪ Deep Learning: Computer Vision ▪ Image Classification – Convolution Neural Network ▪ Libraries: Tensorflow & PyTorch ▪ Dataset: Cats and Dog ▪ Open source dataset ▪ Reference: https://www.microsoft.com/en-us/download/ ▪ Total Images: 25,000 ▪ Images stored in Azure Blob Storage ▪ Mounted to Databricks Workspace Source: https://cs231n.github.io/convolutional-networks/

6. Key Learnings ▪ Custom Containerization ▪ Databricks-connect ▪ Deep Learning (CNN) ▪ Parallelized Gridsearch ▪ Model Management w/ MLflow ▪ AzureML Workspace ▪ AzureML Pipelines ▪ Azure Model Registry ▪ Custom inference script ▪ Webservice Deployment Azure Machine LearningMachine Learning • Azure DevOps • Code Quality Checks ▪ Automated Unit Testing ▪ Azure Pipelines ▪ Continuous deployment Deployment

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9. Questions?

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Productionizing Machine Learning Pipelines with Databricks and Azure ML

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