Predicting with Cloudera Machine Learning

Course Information

Course Name

DSCI-272: Predicting with Cloudera Machine Learning

Exam code

CDP-6001

Duration

4 Days

Certification

Cloudera Machine Learning Engineer

Overview

Enterprise data science teams need collaborative access to business data, tools, and computing resources required to develop and deploy machine learning workflows. Cloudera Machine Learning (CML), part of the Cloudera Data Platform (CDP), provides the solution, giving data science teams the required resources.

This four-day course covers machine learning workflows and operations using CML. Participants explore, visualize, and analyze data. You will also train, evaluate, and deploy machine learning models.

The course walks through an end-to-end data science and machine learning workflow based on realistic scenarios and datasets from a fictitious technology company. The demonstrations and exercises are conducted in Python (with PySpark) using CML.

Audience Profile

The course is designed for data scientists who need to understand how to utilize Cloudera Machine Learning and the Cloudera Data Platform to achieve faster model development and deliver production machine learning at scale. Data engineers, developers, and solution architects who collaborate with data scientists will also find this course valuable.

Prerequisities​

At Course Completion

Through lecture and hands-on exercises, you will learn how to:

• Utilize Cloudera SDX and other components of the Cloudera Data Platform to locate data for machine learning experiments

• Use an Applied ML Prototype (AMP)

• Manage machine learning experiments

• Connect to various data sources and explore data

• Utilize Apache Spark and Spark ML

• Deploy an ML model as a REST API

• Manage and monitor deployed ML models

Course Outline

Module 1: Introduction to CML

• Overview

• CML Versus CDSW

• ML Workspaces

• Workspace Roles

• Projects and Teams

• Settings

• Runtimes/Legacy Engines

Module 2: Introduction to AMPs and the Workbench

• Editors and IDE

• Git

• Embedded Web Applications

• AMPs

Module 3: Data Access and Lineage

• SDX Overview

• Data Catalog

• Authorization

• Lineage

Module 4: Data Visualization in CML

• Data Visualization Overview

• CDP Data Visualization Concepts

• Using Data Visualization in CML

Module 5: Experiments

• Experiments in CML

Module 6: Introduction to the CML Native Workbench

• Entering Code

• Getting Help

• Accessing the Linux Command Line

• Working With Python Packages

• Formatting Session Output

Module 7: Spark Overview

• How Spark Works

• The Spark Stack

• File Formats in Spark

• Spark Interface Languages

• Introduction to PySpark

• How DataFrame Operations Become Spark Jobs

• How Spark Executes a Job

Module 8: Running a Spark Application

• Running a Spark Application

• Reading data into a Spark SQL DataFrame

• Examining the Schema of a DataFrame

• Computing the Number of Rows and Columns of a DataFrame

• Examining a Few Rows of a DataFrame

• Stopping a Spark Application

Module 9: Inspecting a Spark DataFrame

• Inspecting a DataFrame

• Inspecting a DataFrame Column

Module 10: Transforming DataFrames

• Spark SQL DataFrames

• Working with Columns

• Working with Rows

• Working with Missing Values

Module 11: Transforming DataFrame Columns

• Spark SQL Data Types

• Working with Numerical Columns

• Working with String Columns

• Working with Date and Timestamp Columns

• Working with Boolean Columns

Module 12: Complex Types

• Complex Collection Data Types

• Arrays

• Maps

• Structs

Module 13: User-Defined Functions

• User-Defined Functions

• Example 1: Hour of Day

• Example 2: Great-Circle Distance

Module 14: Reading and Writing DataFrames

• Working with Delimited Text Files

• Working with Text Files

• Working with Parquet Files

• Working with Hive Tables

• Working with Object Stores

• Working with Pandas DataFrames

Module 15: Combining and Splitting DataFrames

• Combining and Splitting DataFrames

• Joining DataFrames

• Splitting a DataFrame

Module 16: Summarizing and Grouping DataFrames

• Summarizing Data with Aggregate Functions

• Grouping Data

• Pivoting Data

Module 17: Window Functions

• Window Functions

• Example: Cumulative Count and Sum

• Example: Compute Average Days Between Rides for Each Rider

Module 18: Machine Learning Overview

• Introduction to Machine Learning

• Machine Learning Tools

Module 19: Apache Spark MLlib

• Introduction to Apache Spark MLlib

Module 20: Exploring and Visualizing DataFrames

• Possible Workflows for Big Data

• Exploring a Single Variable

• Exploring a Pair of Variables

Module 21: Monitoring, Tuning, and Configuring Spark Applications

• Monitoring Spark Applications

• Configuring the Spark Environment

Module 22: Fitting and Evaluating Regression Models

• Assemble the Feature Vector

• Fit the Linear Regression Model

Module 23: Fitting and Evaluating Classification Models

• Generate Label

• Fit the Logistic Regression Model

Module 24: Tuning Algorithm Hyperparameters Using Grid Search

• Requirements for Hyperparameter Tuning

• Tune the Hyperparameters Using Holdout Cross-Validation

• Tune the Hyperparameters Using K-Fold Cross-Validation

Module 25: Fitting and Evaluating Clustering Models

• Print and Plot the Home Coordinates

• Fit a Gaussian Mixture Model

• Explore the Cluster Profiles

Module 26: Processing Text: Fitting and Evaluating Topic Models

• Fit a Topic Model Using Latent Dirichlet Allocation

Module 27: Fitting and Evaluating Recommender Models

• Recommender Models

• Generate Recommendations

Module 28: Working with Machine Learning Pipelines

• Fit the Pipeline Model

• Inspect the Pipeline Model

Module 29: Applying a Scikit-Learn Model to a Spark DataFrame

• Build a Scikit-Learn Model

• Apply the Model Using a Spark UDF

Module 30: Deploying a Machine Learning Model as a REST API in CML

• Load the Serialized Model

• Define a Wrapper Function to Generate a Prediction

• Test the Function

Module 31: Autoscaling, Performance, and GPU Settings

• Autoscaling Workloads

• Working with GPUs

Module 32: Model Metrics and Monitoring

• Why Monitor Models?

• Common Models Metrics

• Models Monitoring With Evidently

• Continuous Model Monitoring

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