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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
N/A
At a minimum, you should be familiar with the following before attempting to take this content:
Coming Soon...
Data Preparation for Machine Learning 1.Managing and Exploring Data Managing and Exploring Data in the Lakehouse 2.Data Preparation and Feature Engineering Fundamentals of Data Preparation and Feature Engineering Data Imputation Data Encoding Data Standardization 3.Feature Store Introduction to Feature Store Machine Learning Model Development 4.Model Development Workflow Model Development and MLflow Evaluating Model Performance 5.Hyperparameter Tuning Hyperparameter Tuning Fundamentals Hyperparameter Tuning with Hyperopt 6.AutoML Automated Model Development with AutoML Machine Learning Model Deployment 7.Model Deployment Fundamentals Model Deployment Strategies Model Deployment with MLflow 8.Batch Deployment Introduction to Batch Deployment 9.Pipeline Deployment Introduction to Pipeline Deployment 10.Real-time Deployment and Online Stores Introduction to Real-time Deployment Databricks Model Serving Machine Learning Operations 11.Modern MLOps Defining MLOps MLOps on Databricks 12.Architecting MLOps Solutions Opinionated MLOps Principles Recommended MLOps Architectures 13.Implementation and Monitoring MLOps Solution MLOps Stacks Overview Type of Model Monitoring Monitoring in Machine Learning
Data Preparation for Machine Learning
1.Managing and Exploring Data
2.Data Preparation and Feature Engineering
3.Feature Store
Machine Learning Model Development
4.Model Development Workflow
5.Hyperparameter Tuning
6.AutoML
Machine Learning Model Deployment
7.Model Deployment Fundamentals
8.Batch Deployment
9.Pipeline Deployment
10.Real-time Deployment and Online Stores
Machine Learning Operations
11.Modern MLOps
12.Architecting MLOps Solutions
13.Implementation and Monitoring MLOps Solution
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