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We wanted to design and build an accessible platform that would not only allow everybody to uncover the hidden predictive power of data with ease, but also would make the whole experience 'enjoyable'.

A few years later, after many long days and nights of hard work, we are very proud of how our platform has come to help a diverse set of organizations of all sizes and industries.

With BigML, they have been able to build sophisticated Machine Learning-based solutions affordably by distilling the predictive patterns from their data into real-life intelligent applications usable by anyone.

BigML's platform, private deployments, and rich toolset will continue to help our customers create, rapidly experiment, fully automate and manage Machine Learning workflows that power best-in-class intelligent applications.

How to choose a machine learning API to build predictive apps

Let’s take an example of a predictive feature that a developer may be interested in adding to his app: priority filtering.

Example data can be analyzed to create a model of the relationships between inputs and outputs, so that when a new input is given (new message), a prediction of the output (importance) can be made thanks to the model.

The exact names and profiles of the 2 methods above would vary from one API to the other, but besides that, it’s not obvious how these APIs differ from one another and how to choose the right one based on your apps’ needs.

You’ll find below a list of a few things to consider… Predictive APIs make it easier to use Machine Learning, but as a consequence they also give limited control over the predictive models that you can use, so it’s important to check this aspect of their offerings.

When predicting churn, it’s best if your support team knows which customers are at risk of cancelling their subscription and they know why, so that they have more information for taking action and making each customer stay.

Another interesting application where explaining predictions is a key requirement is job recommendations by employment agencies: people need to know why a job was recommended to them.

With BigML you can create “ensembles” of models on a given dataset: training and predictions take longer than with a single model, but the more models there are in your ensemble, the more accuracy you’re likely to get.

With Amazon ML, you can tweak settings such as the target size of the model and the number of passes to be made over the data: the bigger these are, the longer model training and predictions will take, but you can expect to gain some accuracy.

To answer these questions, you first need to figure out: Model training time is usually not critical because in most applications you don’t need to train or update models frequently (i.e.

For instance, predictions could be made and stored when the user is not active, so that when he returns to the app they have already been integrated (think about product recommendations, or priority detection on email clients, or the feature on your smartphone that predicts your next destination).

When calling an API served by another machine, one thing that would come into consideration in your choice of a predictive API provider would be your app hosting platform, as you would gain a few milliseconds by staying on the same network.

Once you’ve estimated how many predictions and models you need to create, you’ve decided where predictions will be made and if they’ll be made in batch or not, you’ll have enough information to estimate the cost of using each API and you’ll be able to take that into account in your choice!

Besides, in order to make better predictions of what functionality and content a user will need at any given time, you need to take into account as much contextual information as possible and to merge different sources of data: location, calendar, contacts, messages… Even though I am personally ok with sharing a bit of each with different apps, I am wary of giving it all to a single app.

The winner of DIAmond Award 2016 liberates Machine Learning from being a non-repeatable, fringe activity practiced by few hard to hire, hard to retain experts, utilizing complex and expensive tools.

The intuitive web interface welcomes non-PhDs to build highly interpretable and exportable models based on a collection of highly scalable and proven algorithms.

Advances in Machine Learning have improved the capability of voice recognition dramatically and the availability of voice recognition APIs like Amazon’s Alexa Voice Service have made it possible for the rapid adoption of voice controlled applications.

It is great at identifying patterns in sparse data and zeroing on variables that strongly influence the outcome of events of interest to the insurers like: Will the policy be profitable?

Each newly discovered pattern can have direct impact on mitigating exposure in future claims thus resulting in huge cost savings for the industry.

Game changer BigML has set out to create the Machine Learning platform of the 21st century from scratch, so that practitioners and developers can be mainly concerned with driving more value and business results from their data.

BigML’s flexible deployment model fits the full spectrum of needs ranging from multi-tenant cloud to virtual private cloud and on-premises.

For an insurance company requiring full control over all resources, BigML’s on-premises version is a game changer as it enables it to abide by stringent regulatory, security and other company policy imperatives.

BigML's platform, private deployments, and rich toolset will continue to help customers create, rapidly experiment, fully automate and manage Machine Learning workflows that power best-in-class intelligent applications.

BigML is active in promoting Machine Learning in academia through an education program, reaching over 600 universities worldwide making real BigML's motto: "Machine Learning made beautifully simple for everyone".

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