For reliability, accuracy and performance, both AI and machine learning heavily rely on large sets. Because the larger the pool of data, the better you can train the models. That’s why it’s critical for big data platforms to efficiently work with different data streams and systems, regardless of the structure of the data (or lack thereof), data velocity or volume.
However, that’s easier said than done.
Today every big data platform faces these systemic challenges:
Compute / Storage Overlap: Traditionally, compute and storage were never delineated. As data volumes grew, you had to invest in compute as well as storage.
Non-Uniform Access of Data: Over the years, too much dependency on business operations and applications have led companies to acquire, ingest and store data in different physical systems like file systems, databases and data warehouses (e.g. SQL Server or Oracle), big data systems (e.g….
Azure Machine Learning is part of the Cortana Intelligence Suite. It’s a cloud based collaborative drag and drop tool that can be used to build, test, and deploy predictive analytics solutions. We recently worked with Casella Waste Systems to analyze their customer and sales data using Azure ML. We found Azure ML to be a useful tool that allowed us to visualize our work and avoid coding when it wasn’t necessary. However, the process of creating successful experiments didn’t come without some speed bumps. Here are some things that we discovered along our ML journey, delivered in the spirit of a Buzzfeed article.
1. Your experiment starts out nice and simple
2. And ends up looking like this
3. You have multiple people working on an experiment
4. But you see this when everyone tries to edit the experiment at the same time