Scale machine learning workflows and operationalize models with advanced MLOps on Databricks.
This course covers advanced topics such as distributed model training with Apache Spark, hyperparameter tuning, MLflow tracking, Unity Catalog governance, and MLOps practices including CI/CD, model monitoring, drift detection and deployment strategies.
- Why get trained: Learn how to build, tune and deploy scalable machine learning models using Apache Spark, MLflow, Unity Catalog and MLOps tools such as Databricks Workflows and CI/CD pipelines.
- Why it matters: Advanced ML and MLOps capabilities enable organizations to operationalize AI, ensure model reliability and scale production-grade machine learning systems.
- Who should attend: Experienced data scientists, machine learning engineers and MLOps practitioners responsible for building, deploying and managing production-ready ML systems.
Build advanced machine learning and MLOps capabilities to scale and operationalize AI solutions on Databricks with Trainocate. HRD Corp Claimable.
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.
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
Prerequisites
The content was developed for participants with these skills/knowledge/abilities:
- The user should have intermediate-level knowledge of traditional machine learning concepts, development, and the use of Python and Git for ML projects.
- It is recommended that the user has intermediate-level experience with Python.
Target Audience
- Everyone who is interested
Module 1: Overview of Machine Learning Operations on Databricks
- Review of MLOps
- Streamlining Development to Deployment
Module 2: Continuous Workflows for Machine Learning Operations
- Streamlining MLOps
- Streamlining MLOps with Databricks
Module 3: Testing Strategies with Databricks
- Automate Comprehensive Testing
- Model Rollout Strategies with Databricks
Module 4: Model Quality and Lakehouse Monitoring
- Introduction to Monitoring
- Lakehouse Monitoring
Module 5: Streamlining Multiple Environment Deployments – DABs
- Build ML assets as Code
- Course Summary and Next Steps
Dates & Locations
June 25, 2026 - June 25, 2026
July 22, 2026 - July 22, 2026
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%
Training & Certification Guide
Frequently Asked Questions
Speak to a Training Consultant
All courses are HRD Claimable.
Get in touch with our team via the form or WhatsApp us on +6011-5119 6631
