Welcome to the MLOps Gym, where we guide you through the essential steps of implementing MLOps practices on Databricks, ensuring that your machine learning projects move from ad hoc experimentation to robust, scalable, and reproducible workflows. In this blog series, we will take you through three key phases to elevate your MLOps proficiency: Crawl, Walk, and Run.
Crawl: Building the foundations for repeatable workflows
In this phase, we will focus on transitioning from ad hoc data science practices to establishing repeatable workflows.
Among other topics, here, we will delve into:
- the fundamentals of setting up Databricks environments such as cluster configuration, Unity Catalog, etc.
- leveraging version control to ensure consistency and reproducibility in your machine learning experiments.
- proper use of MLflow
Whether you are new to MLOps or looking to refine your approach, the Crawl phase provides you with the foundation for success.
Walk: Integrating CI/CD
Our focus in this phase shifts toward integrating Continuous Integration and Continuous Deployment (CI/CD) practices into your MLOps pipeline.
Here are some of the topics we will cover:
- automating testing, deployment, and monitoring processes
- ensuring reproducibility through data versioning and dependency management
By mastering CI/CD on Databricks, you will streamline project management, accelerate time to market, and foster a culture of agility and innovation.
Run: Elevating MLOps with rigor and quality
In this final phase, we will explore the advanced techniques to enhance the rigor and quality of your MLOps practice.
We will dive deeper into topics such as:
- model explainability, bias detection, and end-to-end lineage
- empowering you to maintain model integrity, compliance, and reliability at scale.
Elevating your MLOps game with the prescribed best practices, tools, and frameworks will enable you to meet the demands of enterprise-grade machine learning deployments.
You won't want to miss this...
As a companion to this article, we spoke to one of our most valued partners, Gavi Regunath, about this subject. You can watch the recording of that discussion on the Advancing Analytics YouTube channel.
Coming up next!
We are thrilled to announce the MLOps Gym series will be publishing more content in the coming weeks, with more blogs elaborating on the Crawl, Walk, Run phases! Stay tuned as we roll out this comprehensive guide, taking you from the basics of establishing repeatable workflows to the advanced techniques to enhance the rigor and quality of your MLOps practice.
Next blog in this series: MLOps Gym - Beginners Guide to MLflow
