Advanced Machine Learning with Databricks
This course is aimed at data scientists and machine learning practitioners and consists of two, four-hours modules.
Machine Learning at Scale
In this course, you will gain theoretical and practical knowledge of Apache Spark’s architecture and its application to machine learning workloads within Databricks. You will learn when to use Spark for data preparation, model training, and deployment, while also gaining hands-on experience with Spark ML and pandas APIs on Spark. This course will introduce you to advanced concepts like hyperparameter tuning and scaling Optuna with Spark. This course will use features and concepts introduced in the associate course such as MLflow and Unity Catalog for comprehensive model packaging and governance.
Advanced Machine Learning Operations
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 Declarative Automation Bundles (DABs), Lakeflow Jobs, and 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.
Note: Databricks Academy is transitioning to a notebook-based format for classroom sessions within the Databricks environment, discontinuing the use of slide decks for lectures. You can access the lecture notebooks in the Vocareum lab environment.
The content was developed for participants with these skills/knowledge/abilities:
• Basic understanding of DS/ML concepts (e.g. classification and regression models), common model metrics (e.g. F1-score), and Python libraries (e.g. scikit-learn and XGBoost).
• 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.
• Access to a Databricks workspace with administrator permissions and familiarity with basic Databricks operations (create clusters, run notebooks, basic notebook operations)
• Intermediate experience with Git version control, including repository management and Personal Access Token (PAT) configuration for GitHub integration
• Basic knowledge of CI/CD workflows, pipeline configurations, and DevOps concepts for automated deployment processes
• Intermediate programming experience with Python, MLflow for model tracking and management, and Unity Catalog for data governance
• Familiarity with machine learning model development lifecycle, including feature engineering, model training, validation, and deployment concepts
• Experience with command line interfaces, particularly Databricks CLI installation, configuration, and authentication using personal access tokens
• Understanding of model deployment strategies, including A/B testing, traffic distribution, and real-time inference concepts
• Basic knowledge of Lakeflow Jobs for job creation, task dependencies, and workflow orchestration
Outline
1. Machine Learning at Scale
Model Development with Spark
• Spark Architecture Overview
• Introduction to Spark ML for Model Development
• Model Tracking and Packaging with MLflow
• Demo: Model Development with Spark
• Lab: Model Development with Spark
Model Tuning with Optuna on Spark
• Overview of Hyperparameter Tuning
• Scalable HPO Frameworks on Databricks
• Demo: Hyperparameter Tuning with SparkML
• Demo: HPO with Ray Tune
Model Deployment with Spark
• Deployment with Spark
• Inference with Spark
• Demo: Model Deployment with Spark
• Demo: Optimization Strategies with Spark and Delta Lake
• Lab: Model Deployment with Spark
Pandas APIs
• Scaling with Pandas APIs
• Pandas UDFs and Function APIs
• Demo: Pandas APIs
• Lab: Pandas APIs
2. Advanced Machine Learning Operations
Overview of Machine Learning Operations
• Review of MLOps
• Streamlining Development to Deployment
Streamlining MLOps with Databricks
• Streamlining MLOps
• Streamlining MLOps with Databricks
• Demo: Building a CI/CD Pipeline with Databricks CLI
• Lab: Building a CI/CD Pipeline with Databricks CLI
Model Rollout Strategies with Databricks
• Automate Comprehensive Testing
• Demo: Common Testing Strategies
• Demo: Integration Tests with Lakeflow Jobs
• Model Rollout Strategies with Databricks
• Demo: Model Rollout Strategies with Databricks AI Model Serving
• Lab: Rollout Strategies with Lakeflow Jobs
Data Profiling
• Data Profiling
• Demo: Data Profiling Model Quality
• Lab: Monitoring Drift with Data Profiling
Build ML Assets as Code
• Build ML Assets as Code
• Demo: Working with Declarative Automation Bundles
Upcoming Public Classes
Date | Time | Your Local Time | Language | Price |
|---|---|---|---|---|
Oct 14 - 15 | 10 AM - 02 PM (Asia/Kolkata) | - | English | $1000.00 |
Oct 28 - 29 | 01 PM - 05 PM (America/New_York) | - | English | $1000.00 |
Nov 03 | 10 AM - 06 PM (Asia/Singapore) | - | English | $1000.00 |
Nov 03 | 09 AM - 05 PM (Europe/London) | - | English | $1000.00 |
Nov 03 | 09 AM - 05 PM (America/Los_Angeles) | - | English | $1000.00 |
Public Class Registration
If your company has purchased success credits or has a learning subscription, please fill out the Training Request form. Otherwise, you can register below.
Private Class Request
If your company is interested in private training, please submit a request.
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