Machine Learning with Databricks
Contact us to book this course
Machine Learning and AI
On-Site, Virtual
2 days
This course is your gateway to mastering machine learning workflows on Databricks. Dive into data preparation, model development, deployment, and operations, guided by expert instructors. Learn essential skills for data exploration, model training, and deployment strategies tailored for Databricks. By course end, you'll have the knowledge and confidence to navigate the entire machine learning lifecycle on the Databricks platform, empowering you to build and deploy robust machine learning solutions efficiently.
Objectives
After consuming this content, you should be able to:
-
Describe the comprehensive features of the Databricks Data Intelligence Platform tailored for machine learning, including data storage, governance, and exploratory data analysis techniques with Spark and integrated visualization tools.
-
Explain fundamental machine learning concepts, MLflow components for model development, and hyperparameter tuning methods, while performing practical skills in utilizing MLflow for model tracking and tuning, and leveraging Databricks AutoML for rapid model experimentation.
-
Master deployment strategies for batch, pipeline, and real-time scenarios, understanding their advantages and limitations, and demonstrating proficiency in performing batch, pipeline, and real-time inference using Databricks features like DLT and Model Serving.
-
Develop expertise in modern machine learning operations encompassing DevOps, DataOps, and ModelOps principles, and architect machine learning operations solutions based on Databricks-recommended best practices. Perform an end-to-end implementation of a machine learning project using MLOps Stacks and other Databricks capabilities.
Prerequisites
At a minimum, you should be familiar with the following before attempting to take this content:
-
Familiarity with Databricks workspace and notebooks
-
Familiarity with Delta Lake and Lakehouse
-
Intermediate level knowledge of Python
Course outline
- Managing and Exploring Data
- Managing and Exploring Data in the Lakehouse
- Data Preparation and Feature Engineering
- Fundamentals of Data Preparation and Feature Engineering
- Data Imputation
- Data Encoding
- Data Standardization
- Feature Store
- Introduction to Feature Store
- Model Development Workflow
- Model Development and MLflow
- Evaluating Model Performance
- Hyperparameter Tuning
- Hyperparameter Tuning Fundamentals
- Hyperparameter Tuning with Hyperopt
- AutoML
- Automated Model Development with AutoML
- Model Deployment Fundamentals
- Model Deployment Strategies
- Model Deployment with MLflow
- Batch Deployment
- Introduction to Batch Deployment
- Pipeline Deployment
- Introduction to Pipeline Deployment
- Real-time Deployment and Online Stores
- Introduction to Real-time Deployment
- Databricks Model Serving
- Modern MLOps
- Defining MLOps
- MLOps on Databricks
- Architecting MLOps Solutions
- Opinionated MLOps Principles
- Recommended MLOps Architectures
- Implementation and Monitoring MLOps Solution
- MLOps Stacks Overview
- Type of Model Monitoring
- Monitoring in Machine Learning
