Introduction to scalable ML for Developers

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.

1 Day (9am-5:00pm)

This transformational class will unlock your understanding of:

• 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
•   Use case identification and structure
•    How to structure and plan a machine learning project for your business

Concepts:

•      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:

•      Technology landscape
•      Capabilities and limitations
•      Selecting the right tools for the job
•      Implementation choices
•      Optimisation
•      Performance and scalability
•      Integration

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

Mandatory:

•      Programming skills in Java (or similar modern programming language)
•      Basic understanding of Hadoop architecture
•      Basic understanding of Hadoop MapReduce for data processing at scale

Useful, but not required:

•      Apache Pig programming
•      Prior experience with Apache Solr search engine
•      Matrix algebra

This course is a high-intensity 1-day accelerator designed to teach the basics of machine learning with a strong focus specifically on Recommender Systems.  The practical part of the course revolves around building a hybrid real-time Recommender.  Prior machine learning or Recommender System experience is not necessary since this course is aimed at the introductory level.

The course has been jointly designed and written by Big Data Partnership and Ted Dunning and Ellen Friedman, world renowned experts in Recommender Systems and co-authors of Mahout in Action (Manning) . All delegates will receive a copy of this book when they attend the class.