DSCI-272: Predicting with Cloudera Machine Learning( Predicting With Cloudera Machine Learning ) Course Overview

DSCI-272: Predicting with Cloudera Machine Learning( Predicting With Cloudera Machine Learning ) Course Overview

DSCI-272: Predicting with Cloudera Machine Learning offers an immersive four-day instructor-led training experience tailored for intermediate learners engaged in Enterprise data science. Through this course, participants will master the end-to-end Machine learning workflows using Cloudera Machine Learning (CML) on the Cloudera Data Platform (CDP). You'll tackle hands-on exercises to Explore, visualize, and analyze data, and also, importantly, train, evaluate, and deploy machine learning models. The practical application focuses on using Python and PySpark within CML environments, ensuring learners can effectively manage and Deploy ML models as a REST API, preparing them for real-world data science challenges.

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  • Live Training (Duration : 32 Hours)
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  • Live Training (Duration : 32 Hours)
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  • Classroom Training fee on request

♱ Excluding VAT/GST

You can request classroom training in any city on any date by Requesting More Information

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Course Prerequisites

To effectively participate in DSCI-272: Predicting with Cloudera Machine Learning, it is essential for learners to meet the following prerequisites:


  • Basic Knowledge of Python: Familiarity with Python programming to manage data operations and understand the syntax and basic libraries.
  • Understanding of Basic Machine Learning Concepts: Knowledge of fundamental machine learning concepts and workflows.
  • Experience with Apache Spark: A foundational understanding of Apache Spark's core concepts and operations would be beneficial.
  • Familiarity with Data Manipulation Tools: Comfort with using data manipulation and analysis tools, particularly involved in handling large datasets.
  • Introductory Knowledge of SQL and DataFrames: Understanding how to execute SQL queries and operate with DataFrames will help in data exploration and manipulation tasks.
  • Basic Command Line Usage Skills: Ability to navigate and perform tasks using the command line interface, particularly in a Linux environment.

These prerequisites are designed to ensure participants can fully engage with the course material and practical exercises, maximizing the learning outcomes and ability to apply the skills in real-world scenarios.


Target Audience for DSCI-272: Predicting with Cloudera Machine Learning( Predicting With Cloudera Machine Learning )

DSCI-272: Predicting with Cloudera Machine Learning is an intermediate level course designed to equip data professionals with the skills to develop and deploy machine learning workflows using Cloudera's CML and CDP.


• Data Scientists
• Data Engineers
• Machine Learning Engineers
• Developers involved in data science projects
• Solution Architects
• IT Professionals working with big data and machine learning platforms
• Analytics Professionals seeking to leverage Cloudera's machine learning tools




Learning Objectives - What you will Learn in this DSCI-272: Predicting with Cloudera Machine Learning( Predicting With Cloudera Machine Learning )?

Introduction to the Learning Outcomes of DSCI-272: Predicting with Cloudera Machine Learning This course equips participants with the skills to create, evaluate, and deploy machine learning models using Cloudera Machine Learning on the Cloudera Data Platform.

Learning Objectives and Outcomes:

  • Understand and utilize Cloudera SDX and other Cloudera Data Platform components for locating and managing data suitable for machine learning experiments.
  • Employ Apache Spark and Spark ML for conducting scalable data analysis and machine learning on big datasets.
  • Execute full machine learning workflows, including data preparation, model training, and model evaluation within the CML environment.
  • Deploy machine learning models as REST APIs, allowing for integration into broader application ecosystems.
  • Effectively manage and monitor machine learning models post-deployment to ensure performance and accuracy.
  • Use the Applied ML Prototypes (AMPs) to jump-start machine learning projects with pre-built solutions.
  • Connect to various data source types within Cloudera Machine Learning for comprehensive data exploration and visualization.
  • Manipulate and transform data using DataFrame operations in PySpark within the Cloudera ecosystem.
  • Address complex data types and apply user-defined functions in Spark to enhance data analysis capabilities.
  • Master MLlib in Spark for executing machine

Target Audience for DSCI-272: Predicting with Cloudera Machine Learning( Predicting With Cloudera Machine Learning )

DSCI-272: Predicting with Cloudera Machine Learning is an intermediate level course designed to equip data professionals with the skills to develop and deploy machine learning workflows using Cloudera's CML and CDP.


• Data Scientists
• Data Engineers
• Machine Learning Engineers
• Developers involved in data science projects
• Solution Architects
• IT Professionals working with big data and machine learning platforms
• Analytics Professionals seeking to leverage Cloudera's machine learning tools




Learning Objectives - What you will Learn in this DSCI-272: Predicting with Cloudera Machine Learning( Predicting With Cloudera Machine Learning )?

Introduction to the Learning Outcomes of DSCI-272: Predicting with Cloudera Machine Learning This course equips participants with the skills to create, evaluate, and deploy machine learning models using Cloudera Machine Learning on the Cloudera Data Platform.

Learning Objectives and Outcomes:

  • Understand and utilize Cloudera SDX and other Cloudera Data Platform components for locating and managing data suitable for machine learning experiments.
  • Employ Apache Spark and Spark ML for conducting scalable data analysis and machine learning on big datasets.
  • Execute full machine learning workflows, including data preparation, model training, and model evaluation within the CML environment.
  • Deploy machine learning models as REST APIs, allowing for integration into broader application ecosystems.
  • Effectively manage and monitor machine learning models post-deployment to ensure performance and accuracy.
  • Use the Applied ML Prototypes (AMPs) to jump-start machine learning projects with pre-built solutions.
  • Connect to various data source types within Cloudera Machine Learning for comprehensive data exploration and visualization.
  • Manipulate and transform data using DataFrame operations in PySpark within the Cloudera ecosystem.
  • Address complex data types and apply user-defined functions in Spark to enhance data analysis capabilities.
  • Master MLlib in Spark for executing machine
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