Scalable Machine Learning with Apache Spark™
Summary
In this course, you will experience the full data science workflow, including data exploration, feature engineering, model building, and hyperparameter tuning. By the end of this course, you will have built an end-to-end distributed machine learning pipeline ready for production.
Description
This course guides students through the process of building machine learning pipelines using Apache Spark. You will build solutions to parallelize model training, hyperparameter tuning, and inference.
This course starts by introducing the core components of SparkML: transformers, estimators, and pipelines. To keep track of your machine learning experiments, you will use the open source libraries, MLflow and Delta Lake, to version and reproduce your models. Next, you will perform Bayesian hyperparameter tuning in a distributed setting with Hyperopt, followed by parallelizing the training and inference of single node models with Pandas UDFs. Lastly, you will learn how to leverage the Databricks Feature Store & glass-box AutoML offering to jumpstart your machine learning projects. There are plenty of hands-on labs, all available in Python.
Objectives
Upon completion of the course, students should be able to:
- Create data processing pipelines with Spark
- Build and tune machine learning models with SparkML
- Track, version, and deploy models with MLflow
- Perform distributed hyperparameter tuning with Hyperopt
- Use Spark to scale the inference of single-node models
- Use Databricks glass-box AutoML to automatically train and tune models
Prerequisites
- Intermediate experience with Python
- Beginning experience with the PySpark DataFrame API (or have taken the Apache Spark Programming with Databricks class)
- Working knowledge of machine learning and data science
Outline
Day 1 AM
|
Time |
Lesson |
Description |
|
20m |
Review |
Review of Spark concepts |
|
30m |
ML Overview (optional) |
Types of Machine Learning, Business applications of ML (NOTE: this class uses Airbnb's SF rental data to predict things such as price of rental)" |
|
10m |
Break |
|
|
35m |
Data Cleansing |
How to deal with null values, outliers, data imputation |
|
40m |
Data Exploration Lab |
Exploring your data, log-normal distribution, determine baseline metric to beat |
|
10m |
Break |
|
|
30m |
Linear Regression I |
Build simple univariate linear regression model SparkML APIs: transformer vs estimator |
Day 1 PM
|
Time |
Lesson |
Description |
|
20m |
Linear Regression I Lab |
Build multivariate linear regression model Evaluate RMSE and R2 |
|
30m |
Linear Regression II |
How to One Hot Encode in Spark Pipeline API Saving and loading models |
|
10m |
Break |
|
|
40m |
Linear Regression II Lab |
Simplify pipeline using RFormula Build linear regression model to predict on log-scale, then exponentiate prediction and evaluate |
|
30m |
MLflow Tracking |
Use MLflow to track experiments, log metrics, and compare runs |
|
10m |
Break |
|
|
30m |
MLflow Model Registry |
Register a model using MLflow Deploy that model into production Update a model in production to new version including a staging phase for testing Archive and delete models |
|
45m |
MLflow Lab |
Use MLflow to track models and Delta table Register Model |
Day 2 AM
|
Time |
Lesson |
Description |
|
20m |
Review |
What did we discover yesterday? |
|
40m |
Decision Trees |
Distributed implementation of decision trees and maxBins parameter (why you WILL get different results from sklearn) Feature importance |
|
10m |
Break |
|
|
40m |
Random Forests and Hyperparameter Tuning |
Random Forests K-Fold cross-validation SparkML's Parallelism parameter (introduced in Spark 2.3) Speed up Pipeline model training by 4x |
|
30m |
Hyperparameter Tuning Lab |
Perform grid search on a random forest Get the feature importances across the forest Save the model Identify differences between Sklearn's Random Forest and SparkML's |
|
10m |
Break |
|
|
20m |
Hyperopt |
Hyperparameter tuning for MLlib models |
|
20m |
Hyperopt Lab |
Distributed hyperparameter tuning for scikit-learn models with Spark |
Day 2 PM
|
Time |
Lesson |
Description |
|
25m |
AutoML |
Programmatically use Databricks AutoML to automatically train and tune your models |
|
20m |
AutoML Lab |
Use the Databricks AutoML UI to automatically train and tune your models |
|
15m |
Feature Store |
Build, merge, and evolve features with the Databricks Feature Store |
|
10m |
Break |
|
|
20m |
XGBoost |
Using 3rd party libraries with Spark Discuss gradient boosted trees and their variants |
|
15m |
Inference with Pandas UDF |
Build a single-node ML model, but apply in parallel using mapInPandas (introduced in Spark 3.0) |
|
20m |
Pandas UDFs Lab |
Lab to apply a single-node ML model at scale |
|
10m |
Break |
|
|
15m |
Training with Pandas Function API |
Build a single-node ML model for each IoT Device using applyInPandas (introduced in Spark 3.0) and track it with MLflow |
|
20m |
Koalas |
Use the new open-source library to write Pandas code that distributes using Spark under the hood |
Upcoming Classes
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Eastern Standard Time
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Eastern Daylight Time
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Onsite Training
Public Training
Virtual - Americas (half-day schedule)
-
9:00 AM - 1:00 PM PDT$ 1500.00 USD -
9:00 AM - 1:00 PM PST$ 1500.00 USD
Virtual - EMEA
-
9:00 AM - 5:00 PM CEST$ 1500.00 USD
-
9:00 AM - 5:00 PM CET$ 1500.00 USD
-
9:00 AM - 5:00 PM CET$ 1500.00 USD
-
9:00 AM - 5:00 PM CET$ 1500.00 USD
-
9:00 AM - 5:00 PM CEST$ 1500.00 USD
-
9:00 AM - 5:00 PM CEST$ 1500.00 USD
-
9:00 AM - 5:00 PM CEST$ 1500.00 USD
Virtual - US Eastern
-
9:00 AM - 5:00 PM EST$ 1500.00 USD
-
9:00 AM - 5:00 PM EST$ 1500.00 USD
-
9:00 AM - 5:00 PM EST$ 1500.00 USD
-
9:00 AM - 5:00 PM EDT$ 1500.00 USD
-
9:00 AM - 5:00 PM EDT$ 1500.00 USD
Virtual - US Pacific
-
9:00 AM - 5:00 PM PDT$ 1500.00 USD
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