Machine Learning Operations (MLOps) is a set of practices that combines Machine Learning (ML), DevOps, and Data Engineering to automate the end-to-end machine learning lifecycle.

In this blog post, we will compare two popular MLOps accelerators: AzureML MLOps v2 and Azure Databricks MLOps Stacks.

Before diving into the specifics of each accelerator, let’s understand the general approach to model development that is common to both platforms. This approach involves two key phases often referred to as the “inner loop” and the “outer loop”.

Inner Loop 🧪

The inner loop consists of the iterative data science workflow performed within a dedicated, secure workspace. This includes several sub-steps such as feature engineering, ML algorithm selection, hyperparameter tuning, model fitting, and model evaluation. The primary goal of the inner loop is to develop a machine learning model that performs well on the given task. This process is typically performed by data scientists and ML engineers who experiment with different models, tune parameters, and validate the model’s performance.

Outer Loop 💻

The outer loop involves the steps required to deploy the model into a production environment. This includes pre-production staging and testing, production deployment, and monitoring of the model, data, and infrastructure. The outer loop is typically managed by ML engineers and DevOps professionals who are responsible for ensuring that the model is correctly integrated into the production system and that it continues to perform well over time. 

Both the inner and outer loops are essential parts of the MLOps process. The inner loop focuses on model development, while the outer loop focuses on model deployment and monitoring. Together, they enable organizations to develop, deploy, and maintain machine learning models in a systematic and efficient manner.

Now, let’s dive into the specifics of AzureML MLOps v2 Accelerator and Azure Databricks MLOps Stacks Accelerator.

AzureML MLOps v2 Accelerator 🚀

AzureML MLOps v2 Accelerator is a solution accelerator that provides enterprise-ready templates to deploy machine learning models on the Azure platform. It aims to serve as the starting point for MLOps implementation in Azure. 🎯

Key Features 🔑

• Modularity: The solution accelerator provides a modular end-to-end approach for MLOps in Azure.
• Customizability: As each organization is unique, solutions can often be customized to fit the organization’s needs.
• Goals: The solution accelerator aims for simplicity, modularity, repeatability, security, collaboration, and enterprise readiness.
• Template-based Approach: It accomplishes these goals with a template-based approach for end-to-end data science, driving operational efficiency at each stage.

AzureML MLOps v2 Architecture

Below is the MLOps v2 architecture for a Classical Machine Learning scenario on tabular data using Azure Machine Learning

.

Azure Databricks MLOps Stack Accelerator 🚀

Azure Databricks MLOps Stack Accelerator automates the creation of infrastructure for an ML project workflow. It sets up the elements required to implement and operate ML for continuous deployment across development, staging, and production environments. 🏗️

Key Features 🔑

• Integration: MLOps Stacks is fully integrated into the Databricks CLI and Databricks Asset Bundles.
• Workflow: The environment created by MLOps Stacks implements the MLOps workflow recommended by Databricks.
• Customizability: You can customize the code to create stacks to match your organization’s processes or requirements.
• Components: The default MLOps Stack takes advantage of the unified Databricks platform and uses tools like Databricks notebooks, MLflow, Databricks Feature Store, Databricks Model Serving, Infrastructure-as-code: Databricks Asset Bundles, Orchestrator: Databricks Workflows, and CI/CD: GitHub Actions, Azure DevOps.

Databricks MLOps Stacks Architecture

Below is the Databricks MLOps Stacks Architecture.

Comparing MLOps Accelerators 🧠

Here’s a strategy to help you compare AzureML MLOps v2 Accelerator and Azure Databricks MLOps Stacks Accelerator:

1. Understand Your Requirements: Identify your organization’s specific needs and requirements for MLOps. This could include factors like the scale of operations, the complexity of models, the need for real-time updates, any existing Python scripts, developer familiarity with tooling, etc.
2. Evaluate Features: Compare the features of both accelerators in the context of your requirements. Look at aspects like modularity, customizability, integration with other tools, etc.
3. Consider the Ecosystem: Consider the larger ecosystem that these accelerators are a part of. For instance, if your organization heavily uses Azure services, AzureML MLOps v2 might offer better integration.
4. Assess Ease of Use: Evaluate how easy it is to use and implement these accelerators. This could be influenced by factors like the quality of documentation, community support, etc.
5. Cost Analysis: Conduct a cost analysis. While the accelerators themselves might be free, the services they use might incur costs.

Cost Comparison 💰

Comparing the costs of AzureML MLOps v2 Accelerator and Azure Databricks MLOps Stack Accelerator can be complex due to the different pricing models and the variety of services they use.

AzureML MLOps v2 Accelerator Costs

AzureML MLOps v2 Accelerator itself does not have an additional charge. However, you will incur separate charges for other Azure services consumed, including but not limited to Azure Blob Storage, Azure Key Vault, Azure Container Registry, and Azure Application Insights. The cost will depend on the scale of your operations and the specific Azure services you use. Azure offers a pay-as-you-go model where you pay for compute capacity by the second, with no long-term commitments or upfront payments. They also offer savings plans and reservations for compute services.

Azure Databricks MLOps Stacks Accelerator Costs

Databricks MLOps Stacks Accelerator is available to any Databricks customer free of charge. However, you will incur costs for the Databricks platform and any other services you use. Databricks pricing starts at $0.22 per Databricks unit (DBU) for running SQL queries. The cost will depend on the scale of your operations and the specific Databricks services you use.

Conclusion 🏁

Both AzureML MLOps v2 and Azure Databricks MLOps Stacks Accelerators provide robust, customizable, and enterprise-ready solutions for implementing MLOps. The choice between the two would depend on the specific needs and context of your organization. It’s recommended to explore both options and choose the one that best fits your organization’s MLOps strategy.

If you would like to engage with Mphasis Datalytyx to help perform these assessments, then please reach out to me!🎉

This blog is written by Sunny Sharma 

Disclaimer: Please note the opinions above are the author’s own and not necessarily my current employer’s opinion. This blog article is intended to generate discussion and dialogue with the audience. If I have inadvertently hurt your feelings in anyway, then I’m sorry.