Machine Learning Model Deployment
This course is designed to introduce three primary machine learning deployment strategies and illustrate the implementation of each strategy on Databricks. Following an exploration of the fundamentals of model deployment, the course delves into batch inference, offering hands-on demonstrations and labs for utilizing a model in batch inference scenarios, along with considerations for performance optimization. The second part of the course comprehensively covers pipeline deployment, while the final segment focuses on real-time deployment. Participants will engage in hands-on demonstrations and labs, deploying models with Model Serving and utilizing the serving endpoint for real-time inference.
Note:
1. This is the third course in the 'Machine Learning with Databricks’ series.
2. Databricks Academy is transitioning from video lectures to a more streamlined PDF format with slides and notes for all self-paced courses. Please note that demo videos will still be available in their original format. We would love to hear your thoughts on this change, so please share your feedback through the course survey at the end. Thank you for being a part of our learning community!
At a minimum, you should be familiar with the following before attempting to take this content:
• Familiarity with the Databricks Data Intelligence Platform and basic workspace operations (create clusters, run code in notebooks, use basic notebook operations, import repos from git)
• Intermediate programming experience with Python, including data manipulation libraries (pandas, numpy) and working with APIs (databricks-sdk, REST endpoints)
• Basic knowledge of MLflow for experiment tracking, model logging, model registry operations, and model versioning
• Understanding of machine learning fundamentals, including model training, evaluation, batch inference, and real-time deployment concepts
• Intermediate experience with Unity Catalog for data governance and model registry management
• Basic familiarity with Feature Engineering concepts, including feature tables, feature lookups, and offline vs online feature stores
• Understanding of Delta Lake operations (create tables, perform updates, optimize files, and liquid clustering) and data storage optimization techniques
• Basic knowledge of Apache Spark and PySpark for distributed data processing and User Defined Functions (UDFs)
Self-Paced
Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos
Registration options
Databricks has a delivery method for wherever you are on your learning journey
Self-Paced
Custom-fit learning paths for data, analytics, and AI roles and career paths through on-demand videos
Register now
Instructor-Led
Public and private courses taught by expert instructors across half-day to two-day courses
Register now
Blended Learning
Self-paced and weekly instructor-led sessions for every style of learner to optimize course completion and knowledge retention. Go to Subscriptions Catalog tab to purchase
Purchase now
Skills@Scale
Comprehensive training offering for large scale customers that includes learning elements for every style of learning. Inquire with your account executive for details
