Machine Learning with Apache Mahout

This one-day course is designed to help Software Engineers and Data Scientists understand the high-level concepts and classifications of machine learning systems, with a strong focus on building Recommender Systems.

You will gain an understanding of the tools and high-level conceptual ideas needed to understand what a machine learning solution is (and is not) capable of, and how to identify a suitable use case. You will learn how to construct an example solution at the conceptual level using pre-provided building blocks in order to get a feel for the general design patterns.

You will learn hands-on how to build a scalable hybrid real-time Recommender System based on Apache Hadoop, Apache Mahout, and Apache Solr, and how to optimise the system to deliver real business value.

Delegates will learn how to

·       Classes and categories of machine learning systems

·       Capabilities and limitations of end solutions, in business terms

·       Capabilities and limitations of technology, in solution capability terms

·       How to use case identification and structure

·       How to structure and plan a machine learning project for your business


Software Engineers, Data Scientists, or Technologists with a background in Java programming or a similar modern programming language.


·       Programming skills in Java (or similar modern programming language)

·       Basic understanding of Hadoop architecture

·       Basic understanding of Hadoop MapReduce for data processing 


·       Machine learning system classifications

·       Capabilities and limitations

Use Cases

·       Top level use case categorisations

·       Identifying and categorising your own use case

·       Deep-dive use case example


·       Technology landscape

·       Capabilities and limitations

·       Selecting the right tools for the job

·       Implementation choices

·       Optimisation

·       Performance and scalability

·       Integration

Program Details
Duration 1 Days
Capacity Max 12 Persons
Training Type Classroom / Virtual Classroom

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