Course Overview
In this course, you will be provided with a comprehensive understanding of the machine learning lifecycle and MLOps, emphasizing best practices for data and model management, testing, and scalable architectures. It covers key MLOps components, including CI/CD, pipeline management, and environment separation, while showcasing Databricks’ tools for automation and infrastructure management, such as Databricks Asset Bundles (DABs), Workflows, and Mosaic AI Model Serving. You will learn about monitoring, custom metrics, drift detection, model rollout strategies, A/B testing, and the principles of reliable MLOps systems, providing a holistic view of implementing and managing ML projects in Databricks.
What are the skills covered
- Overview of Machine Learning Operations on Databricks
- Continuous Workflows for Machine Learning Operations
- Testing Strategies with Databricks
- Model Quality and Lakehouse Monitoring
- Streamlining Multiple Environment Deployments – DABs
Who should attend this course
- Everyone who is interested
Course Curriculum
What are the Prerequisites
The content was developed for participants with these skills/knowledge/abilities:
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The user should have intermediate-level knowledge of traditional machine learning concepts, development, and the use of Python and Git for ML projects.
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It is recommended that the user has intermediate-level experience with Python.
Course Modules
Exam & Certification
Databricks Certified Machine Learning Professional exam.
The Databricks Certified Machine Learning Professional certification exam assesses an individual’s ability to design, implement, and manage enterprise-scale machine learning solutions using advanced Databricks platform capabilities. This includes proficiency in building scalable ML pipelines with SparkML, implementing distributed training and hyperparameter tuning, leveraging advanced MLflow features, and utilizing Feature Store concepts for automated feature pipelines.
The certification exam evaluates expertise in MLOps practices, including testing strategies, environment management with Databricks Asset Bundles, automated retraining workflows, and monitoring using Lakehouse Monitoring for drift detection. Additionally, test-takers are assessed on their ability to implement deployment strategies, custom model serving, and model rollout management. Individuals who pass this certification exam can be expected to perform advanced machine learning engineering tasks at enterprise scale, implementing production-ready ML systems with comprehensive monitoring, testing, and deployment practices using the full feature set of Databricks.
This exam covers:
- Model Development – 44%
- ML Ops – 44%
- Model Deployment – 12%
