The upcoming release on BigML keeps pushing the envelope for more people to gain access to and make a bigger impact with Machine Learning. This release features a brand new resource providing a novel way to combine models: BigML Fusions. In this post, we'll do a quick introduction to Fusions before we move on to the remainder of our series of 6 blog posts (including this one) to give you a detailed perspective of what's behind this new capability. Today's post explains the basic concepts that will be followed by an example use case. This will be followed by three more articles focused on how to use Fusions through the BigML Dashboard, API, and WhizzML in an automated fashion.
This week we completed four in-depth training webinars focused on WhizzML, BigML's new domain-specific language for automating Machine Learning workflows, implementing high-level Machine Learning algorithms, and easily sharing them with others. We already have our first batch of WhizzML graduates merely a week after launch. However, many of you were either not able to secure a live webinar spot or not able to join us at the scheduled date and time. Don't fret if you missed any of these training sessions. You can now watch the whole series at your own pace on BigML's YouTube channel.
As part of our Fusions release, we have already demonstrated a use case and walked through an example using the BigML Dashboard. Our fourth of six blog posts on Fusions will demonstrate how to utilize Fusions by directly calling the BigML REST API. As a reminder, Fusions can be used for both classification and regression supervised Machine Learning problems, and function by aggregating the results of multiple models (decision trees, ensembles, logistic regressions, and/or deepnets), often achieving better performance as result. Using the BigML API, requires that you first set up the correct environment variables. For this tutorial we are using the same dataset of home sales from the Redfin search engine used in our previous tutorial of the BigML Dashboard, and available in the BigML Gallery.
I am certain you have heard of Artificial Intelligence. So, now that you have heard about it, you might be wondering what can Artificial Intelligence actually do for your company. Or is it just all hype? Well a lot of it is hype – I'm looking at you killer robots. As Andrew Ng said, "Fearing a rise of killer robots is like worrying about overpopulation on Mars".
Data Science Start-Ups in Focus -- we'll deep dive into start-ups in the area of data science and analytics to learn from their unique perspectives in the field. The goal here is to introduce readers to some new helpful platforms & tools, to show companies doing interesting things related to Machine Learning & the Big Data space, and to help shine a spotlight on specific happenings in the field that we think are worth your attention. While the topics of Machine Learning and Data Science are quite popular right now, the practices involved in both aren't actually understood by most people. There are a lot of materials about these topics available on the internet that people try to use directly with their data without understanding the implications. Even if it works out for some, the use of those models in production is risky without proper setups.