Predictions in the Clouds – Real Life Machine Learning Use Cases

Answering the needs of the market, all major cloud vendors have released their own Machine Learning platforms. These platforms enable enterprises to perform complex machine learning algorithms at the ease and scale of the clouds.

Microsoft’s Azure ML

Google’s Predictions API

Amazon’s Machine Learning

This meetup will have industry veterans share real life experiences of running predictive algorithms on any of these cloud platforms. They will detail end-to-end scenarios – right from ingesting data from enterprise systems to making predictive results available for consumption by enterprise systems.

The goal of this meetup is not to compare and contrast one cloud platform against the other but to showcase how each of these cloud platforms makes machine learning accessible to the masses, and at scale – which in turn will open up a whole new world of opportunities for machines to help us as we go about in our daily lives.



6:00 – 6:30 pm:
Registration and Networking.

6:30 – 7:15 pm:
Machine Learning in the Clouds – Basics of Decision Trees, Ensembles, Association, Anomaly, Clustering etc.
Poul Petersen, BigML

7:30 pm to 8:15 pm:
Comparative Analysis of Cloud Based Machine Learning Platforms
– Amazon ML, Google ML, Azure ML, DataBricks
Third Eye’s Data Sciences Team

8:15 – 9:00 pm:



Machine Learning in the Clouds – Basics of Decision Trees, Ensembles, Association, Anomaly, Clustering etc

Corvallis, OR based BigML team has been working for the past four years to democratize machine learning in the cloud – making it more consumable, programmable, and scalable. The net result is an intuitive platform that can be leveraged equally by business analysts, developers and data scientists who are eager to perform a variety of predictive analytics and machine learning tasks.

In this session, Poul Petersen, Chief Infrastructure Officer at BigML (MLSaas company), will talk about machine learning in the cloud (basics of decision trees, ensembles, association, anomaly, clustering) and how past is predicting the future and is now easier and more accessible than ever thanks to converging trends of open source technologies, cloud-based computing and a growing ‘big data’ imperative.