Last week BigML announced WhizzML, a new domain-specific language for automating Machine Learning workflows, implementing high-level Machine Learning algorithms, and easily sharing them with others. If you missed the announcement event, you can watch the launch webinar by clicking the link below. This webinar will be complemented by a series of in-depth training sessions for the true innovators, who are looking to push the envelope when it comes to the uptake of Machine Learning in their organizations. Consider this your FREE invitation to join this exclusive four part online event. See the details below.
WhizzML marks a turning point in how companies can automate Machine Learning as it offers out-of-the-box scalability, abstracts away the complexity of underlying infrastructure, and helps analysts, developers, and scientists double or even triple their productivity by reducing the burden of repetitive, brittle and time-consuming Machine Learning tasks. If you complete the following four training sessions, you will not only leap ahead in your understanding of real life Machine Learning automation challenges but also receive a BigML T-shirt to commemorate your achievement.
The first session will cover all the basics describing how WhizzML is implemented on the BigML platform. Ryan Asensio, BigML’s Machine Learning Engineer, will be introducing the purpose of the language and some benefits over other ways of implementing Machine Learning workflows and algorithms. Join us on Monday, May 30, 2016 at 10:00 AM US PDT (Portland, Oregon. GMT -07:00) / 7:00 PM CEST (Valencia, Spain. GMT +02:00).
In this intermediate webinar, Charles Parker, BigML’s VP of Machine Learning Algorithms, will start exploring the WhizzML domain-specific language in greater detail, with a whirlwind tour of its syntax, programming constructs and basic standard library functions. We will also learn how to create and use WhizzML resources (libraries, scripts and executions) by means of several simple yet fully functional example workflows. It will take place on Tuesday, May 31, 2016 at 10:00 AM US PDT (Portland, Oregon. GMT -07:00) /7:00 PM CEST (Valencia, Spain. GMT +02:00). Register now, as space is limited!
In this advanced webinar, we will continue our exploration of the WhizzML language, diving into more complex examples and using more advanced features of the language. Charles Parker, BigML’s VP of Machine Learning Algorithms, will explain how some of the most effective Machine Learning algorithms can be implemented and automated on top of the BigML with WhizzML. Sign up and reserve your spot for Wednesday, June 1, 2016 at 10:00 AM US PDT (Portland, Oregon. GMT -07:00) / 7:00 PM CEST (Valencia, Spain. GMT +02:00).
In this advanced session, we will walk you through some real-world workflow automations with an eye towards the kind of problems posed by complex use cases, and use some of the best tricks to solve them with confidence. This webinar will be presented by Poul Petersen, BigML’s Chief Infrastructure Officer. It will take place on Thursday, June 2, 2016 at 10:00 AM US PDT (Portland, Oregon. GMT -07:00) / 7:00 PM CEST (Valencia, Spain. GMT +02:00). We hope to see you all there!
More training resources:
In addition to these online training sessions, if you prefer the self-study approach, you may want to download and read our WhizzML guides, documentation, tutorials, as well as the slide decks with basic, intermediate and advanced Machine Learning workflows. We have also prepared a number of useful scripts that you can practice with to get more hands on with WhizzML. You’ll find those on BigML’s Gallery. There are also plenty of example scripts and libraries available in the WhizzML Github repository. Please visit our release page and the dedicated WhizzML page to easily navigate to your resource of choice. Welcome to the world of WhizzML!
Looks great, ty for the webinar.
Is there a transcript of it as well as download of the slides to study?
Also where are the “slide decks with basic, intermediate and advanced Machine Learning workflows”?
Thanks! You can find the slides here: https://bigml.com/releases/spring-2016