Continuing with our Object Detection release blog posts series, today, we'll showcase how to automate the training of the object detection models (and their predictions) that anyone will be able to create in BigML in short order. As discussed in previous posts, BigML already offers classification, regression, and unsupervised learning models (e.g., clustering, anomaly detection). They all accept images as just another input data type usable for model training. In fact, when images are uploaded a new Source is created for each and their corresponding IDs are added to a new Composite Source object with a new image field type. In summary, images can be combined with any other data type and can be assigned one or more labels by using the new label fields.

As pointed out in this KDnuggets article, we often only have a few examples of the thing that we want to predict in our data. The use cases are countless: only a small part of our website visitors purchase eventually, only a few of our transactions are fraudulent, etc. This is a real problem when using machine learning. That's because the algorithms usually need many examples of each class to extract the general rules in your data, and the instances in minority classes can be discarded as noise, causing some useful rules to never be found. The KDnuggets article explained several techniques that can be used to address this problem.

As pointed in this Kdnuggets article, it's often the case that we only have a few examples of the thing that we want to predict in our data. The use cases are countless: only a small part of our website visitors purchase eventually, only a few of our transactions are fraudulent, etc. This is a real problem when using Machine Learning. That's because the algorithms usually need many examples of each class to extract the general rules in your data, and the instances in minority classes can be discarded as noise, causing some useful rules to never be found. The Kdnuggets article explained several techniques that can be used to address this problem.

In our previous blog posts of this series, we have introduced Topic Models, BigML's latest resource that helps you find thematically related terms in your unstructured text data, explained how to use it through the BigML Dashboard and the API, and lastly showed how to apply Topic Models in a real-life use case. This post will focus on automating LDA workflows by using WhizzML, a DSL for Machine Learning that provides programmatic support for all the resources you work with in our platform. Let's dive in by creating a Topic Model and making a prediction with it. In BigML, you can perform single instance predictions (referred to as a Topic Distribution) or in batch mode, which is called Batch Topic Distribution. Firstly, we will create a Topic Model without specifying any particular configuration option, that is, relying on default settings.

There are so many Machine Learning algorithms and so many parameters for each one. Why can't we just use a meta-algorithm (maybe even one that uses Machine Learning) to select the best algorithm and parameters for our dataset? Many Machine Learning problems are formalized as an optimization wherein you're given some data, there are some free parameters, and you have some sort of function to measure the performance of those parameters on that data. Your goal is to choose the parameters to minimize (or maximize) the given function. But this sounds exactly like what we do when we select a Machine Learning algorithm!

Sure, you can use WhizzML to fill in missing values or to do some basic data cleaning, but what if you want to go crazy? WhizzML is a fully-fledged programming language, after all. We can go as far down the rabbit hole as we want. As we've mentioned before, one of the great things about writing programs in WhizzML is access to highly-scalable, library-free machine learning. To put in another way, cloud-based machine learning operations (learn an ensemble, create a dataset, etc.) are primitives built into the language.

Machine Learning (ML) services are quickly becoming a taken-for-granted part of the software developer's toolbox, in any domain. These days, databases or networking are a standard component of almost any non-trivial application, so easily integrated that almost no special expertise is required. We expect to see Machine Learning becoming, in the very near future, a similar layer in the software stack. This commoditization of ML services has been driven so far by Service-oriented platforms such as BigML, which have provided a key ingredient of the process: abstraction. Simple and easy to use REST APIs hide away not only the details of the sophisticated algorithms underlying the services at hand, but also the complexities of scaling those computations both over CPU cycles and input data volumes.

We are very excited with all the positive feedback about BigML's latest release. It was a huge milestone to announce WhizzML, the very first domain-specific language for automating Machine Learning workflows, implementing high-level Machine Learning algorithms, and sharing them with others is now publicly available. Thanks to everyone who attended. For those who couldn't make it, we'll publish the video recording soon. More that ever, BigML is committed to its mission to make Machine Learning beautifully simple for everyone.

BigML Spring 2016 release is here! GMT 02:00) for a FREE live webinar to learn about the latest and greatest version of BigML. We'll be focusing exclusively on WhizzML, a new domain-specific language that lets you automate Machine Learning workflows, implement high-level Machine Learning algorithms, and share them with others. WhizzML stands to make a big difference not only in how developers conceive of and implement smart applications, but also how analysts and scientists reduce the burden of repetitive analyses.