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.

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

8. Thanks!

9. Questions?

10. Feedback
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