Automate Machine Learning Using Databricks AutoML
This course will teach you how you can build and train regression, classification, and forecasting models using Databricks AutoML. AutoML automates data preparation and model training thus allowing you to build models with little to no code.
What you'll learn
Databricks AutoML is an important step towards the democratization of machine learning. AutoML makes it easy for anyone to build and train robust models with little to no code.
In this course, Automate Machine Learning Using Databricks AutoML, you will be introduced to the basic concepts of Databricks AutoML.
First, you will see how AutoML automates every step of the machine learning process from data preparation, and data preprocessing to model training and evaluation.
Next, you will first train regression and classification models using the AutoML user interface to configure your model training, and you can configure settings to impute missing values, choose model frameworks and evaluate models.
Then, you will learn to use the AutoML Python API to train regression and classification models. The Python API offers simple regress() and classify() functions which you can configure using input parameters.
Finally, you will work with time series datasets and train forecasting models using both the AutoML UI and the AutoML Python API.
When you are finished with this course you will be able to confidently use AutoML to train regression, classification, and forecasting models and deploy them to production.
Table of contents
- Prerequisites and Outline 3m
- Introducing Databricks AutoML 6m
- Demo: Setting up the Databricks Machine Learning Environment 2m
- Demo: Creating a Delta Table 3m
- Demo: Configuring an AutoML Experiment 6m
- Demo: Running an AutoML Experiment 4m
- Demo: Tracking Metrics, Parameters, and Artifacts in Model Runs 3m
- Demo: Training a Classification Model Using AutoML 7m
- Demo: Viewing the Data Exploration Notebook 3m
- Demo: Viewing the Notebook for the Best Model 6m
- Demo: Serving the Best Model and Making Predictions 7m
- Introducing the AutoML Python API 2m
- Demo: Training a Regression Model Using the AutoML Python API 5m
- Demo: Viewing Training Results 5m
- Demo: Creating and Populating a Feature Table 6m
- Demo: Training AutoML Models Using Features from a Feature Store 3m
- Demo: Comparing Runs in an AutoML Experiment 6m
- Demo: Registering and Serving Model Predictions 6m
- Introducing AutoML for Forecasting 1m
- Demo: Configuring the AutoML Forecasting Experiment 5m
- Demo: Exploring the Data Analysis and Best Model Notebooks 7m
- Demo: Training a Forecasting Model Using the AutoML Python API 5m
- Demo: Accessing and Viewing Forecast Predictions 3m
- Summary and Further Study 1m
About the author
Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing ... more