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DSCI-272: Predicting with Cloudera Machine Learning

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Certificate

DSCI-272: Predicting with Cloudera Machine Learning

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Course Information

Course Name

DSCI-272: Predicting with Cloudera Machine Learning

Exam code

CDP-6001

Duration

4 Days

Certification

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.

Overview

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

  • Overview
  • CML Versus CDSW
  • ML Workspaces
  • Workspace Roles
  • Projects and Teams
  • Settings
  • Runtimes/Legacy Engines
  • Editors and IDE
  • Git
  • Embedded Web Applications
  • AMPs
  • SDX Overview
  • Data Catalog
  • Authorization
  • Lineage
  • Data Visualization Overview
  • CDP Data Visualization Concepts
  • Using Data Visualization in CML
  • Experiments in CML
  • Entering Code
  • Getting Help
  • Accessing the Linux Command Line
  • Working With Python Packages
  • Formatting Session Output
  • 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
  • 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
  • Inspecting a DataFrame
  • Inspecting a DataFrame Column
  • Spark SQL DataFrames
  • Working with Columns
  • Working with Rows
  • Working with Missing Values
  • Spark SQL Data Types
  • Working with Numerical Columns
  • Working with String Columns
  • Working with Date and Timestamp Columns
  • Working with Boolean Columns
  • Complex Collection Data Types
  • Arrays
  • Maps
  • Structs
  • User-Defined Functions
  • Example 1: Hour of Day
  • Example 2: Great-Circle Distance
  • 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
  • Combining and Splitting DataFrames
  • Joining DataFrames
  • Splitting a DataFrame
  • Summarizing Data with Aggregate Functions
  • Grouping Data
  • Pivoting Data
  • Window Functions
  • Example: Cumulative Count and Sum
  • Example: Compute Average Days Between Rides for Each Rider
  • Introduction to Machine Learning
  • Machine Learning Tools
  • Introduction to Apache Spark MLlib
  • Possible Workflows for Big Data
  • Exploring a Single Variable
  • Exploring a Pair of Variables
  • Monitoring Spark Applications
  • Configuring the Spark Environment
  • Assemble the Feature Vector
  • Fit the Linear Regression Model
  • Generate Label
  • Fit the Logistic Regression Model
  • Requirements for Hyperparameter Tuning
  • Tune the Hyperparameters Using Holdout Cross-Validation
  • Tune the Hyperparameters Using K-Fold Cross-Validation
  • Print and Plot the Home Coordinates
  • Fit a Gaussian Mixture Model
  • Explore the Cluster Profiles
  • Fit a Topic Model Using Latent Dirichlet Allocation
  • Recommender Models
  • Generate Recommendations
  • Fit the Pipeline Model
  • Inspect the Pipeline Model
  • Build a Scikit-Learn Model
  • Apply the Model Using a Spark UDF
  • Load the Serialized Model
  • Define a Wrapper Function to Generate a Prediction
  • Test the Function
  • Autoscaling Workloads
  • Working with GPUs
  • Why Monitor Models?
  • Common Models Metrics
  • Models Monitoring With Evidently
  • Continuous Model Monitoring
Course Price

RM13,600.00 exc. 8% tax

Training Dates

Product price:RM13,600.00 exc. 8% tax
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Certification Details