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Overview
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.
Data Preparation for Machine Learning Managing and Exploring Data Data Preparation and Feature Engineering Feature Store Machine Learning Model Development Model Development Workflow Hyperparameter Tuning AutoML Machine Learning Model Deployment Model Deployment Fundamentals Batch Deployment Pipeline Deployment Real-time Deployment and Online Stores Machine Learning Operations Modern MLOps Architecting MLOps Solutions Implementation and Monitoring MLOps Solution
Data scientists, machine-learning engineers and AI/ML practitioners looking to build end-to-end ML workflows on the Databricks Lakehouse Platform. Professionals responsible for preparing data, training models, tuning hyperparameters and deploying ML solutions using Databricks tools, Spark and MLflow. Individuals seeking hands-on experience with feature engineering, model experimentation, tracking, evaluation and serving in a scalable environment. Practitioners with intermediate Python skills and basic ML knowledge who want to strengthen their platform-specific skills in Databricks. Teams building or modernising ML pipelines and aiming to move from experimentation to production-ready, governed machine-learning workflows.
At a minimum, you should be familiar with the following before attempting to take this content:
Coming Soon...
Module 1. Data Preparation for Machine Learning 1.1. Managing and Exploring Data Managing and Exploring Data in the Lakehouse 1.2. Data Preparation and Feature Engineering Fundamentals of Data Preparation and Feature Engineering Data Imputation Data Encoding Data Standardization 1.3. Feature Store Introduction to Feature Store Module 2. Machine Learning Model Development 2.1. Model Development Workflow Model Development and MLflow Evaluating Model Performance 2.2. Hyperparameter Tuning Hyperparameter Tuning Fundamentals Hyperparameter Tuning with Hyperopt 2.3. AutoML Automated Model Development with AutoML Module 3. Machine Learning Model Deployment 3.1. Model Deployment Fundamentals Model Deployment Strategies Model Deployment with MLflow 3.2. Batch Deployment Introduction to Batch Deployment 3.3. Pipeline Deployment Introduction to Pipeline Deployment 3.4. Real-time Deployment and Online Stores Introduction to Real-time Deployment Databricks Model Serving Module 4. Machine Learning Operations 4.1. Modern MLOps Defining MLOps MLOps on Databricks 4.2. Architecting MLOps Solutions Opinionated MLOps Principles Recommended MLOps Architectures 4.3. Implementation and Monitoring MLOps Solution MLOps Stacks Overview Type of Model Monitoring Monitoring in Machine Learning
Module 1. Data Preparation for Machine Learning
1.1. Managing and Exploring Data
1.2. Data Preparation and Feature Engineering
1.3. Feature Store
Module 2. Machine Learning Model Development
2.1. Model Development Workflow
2.2. Hyperparameter Tuning
2.3. AutoML
Module 3. Machine Learning Model Deployment
3.1. Model Deployment Fundamentals
3.2. Batch Deployment
3.3. Pipeline Deployment
3.4. Real-time Deployment and Online Stores
Module 4. Machine Learning Operations
4.1. Modern MLOps
4.2. Architecting MLOps Solutions
4.3. Implementation and Monitoring MLOps Solution
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