Comparing 4 Machine Learning APIs: Amazon Machine Learning, BigML, Google Prediction API and PredicSis on a real data from Kaggle, we find the most accurate, the fastest, the best tradeoff, and a surprise last place. By Louis Dorard UPDATE - NEW BIGML RESULTS: As pointed out by Francisco Martin, if you just change the objective field (SeriousDlqin2yrs) to be numeric instead of categorical, BigML's accuracy for a single model goes to 0.853 (whereas it was initially reported as 0.790 - the accuracy in the table above and the Kaggle rank below have been updated to reflect that). Amazon ML (Machine Learning) made a lot of noise when it came out last month. Shortly afterwards, someone posted a link to Google Prediction API on HackerNews and it quickly became one of the most popular posts. Google's product is quite similar to Amazon's but it's actually much older since it was introduced in 2011.

Machine learning is everywhere these days. It's in your email account filtering out spam and other emails you don't want to read. It's in your connected car helping the voice-controlled interface understand you. Right now, Amazon, Google, IBM, and Microsoft are the biggest players battling to dominate the very fast-growing machine learning cloud services market. IBM further strengthened its position in the market with the recent acquisition of AlchemyAPI, a leading deep learning-based machine learning services platform.

This is my third article on how to build Machine Learning models in the Cloud. Google Prediction API, on the other hand, was released all the way back in 2011, and offers a very stable and simple way to train Machine Learning models via a RESTful interface, although it might seem less friendly if you generally prefer browser interfaces. I am not going to explore the wide range of services offered by Google Cloud Platform, you can easily check the Developers Console out by yourself for free, sign up for the Free Trial offered by Google ( 300 in credit to use for 2 months), and check out Cloud Academy's courses on Google Cloud Platform. We can define Google's approach as a "black box", since you get no control over what happens under the hood: your model configuration is restricted to specifying "Classification" vs. "Regression," or providing a preprocessing PMML (Predictive Model Markup Language) file and a set of weighting parameters in the case of categorical models. On the other hand, your input features (your columns) can contain any type of data, although certain types are easier to work with (i.e.

There are various offerings out there if you want to use machine learning in your analysis nowadays. Nick WIlson spent his internship at BigML comparing three SaaS Machine Learning Services (BigML, Prior Knowledge and Google Prediction API), with WEKA as a benchmark. He wrote a series of blog posts about his findings. In his final post he gives a summary of his work, with links to the different blog posts for details. He let me re-blog his summary here.

For any cloud to be taken seriously, it has to meet an ever rising bar of features. Machine learning seems to be on that list, as all the major cloud providers now feature it. But how they go about doing it is another story. Aside from the "curated API vs. open-ended algorithm marketplace" models, there are the "everything and then some vs. just enough" variants. Here's how the four big cloud providers -- IBM, Microsoft, Google, and Amazon -- stack up next to each other in machine learning. When IBM first announced it would turn its Watson AI system into a consumable service, the questions piled up.

While not widely understood, machine learning has been easily accessible since Google Prediction API was released in 2011. With many applications in a wide variety of fields, this tutorial by Alex Casalboni on the Cloud Academy blog is a useful place to start learning how to build a machine learning model using Google Prediction API. The API offers a RESTful interface as a means to train a machine learning model, and is considered a "black box" due to the restricted access users have to internal configuration. This leaves users with only the "classification" vs "regression" configuration, or the applying of a PMML (Predictive Model Markup Language) file with weighting parameters for categorical models. This tutorial begins with some brief definitions before beginning on how to upload your dataset to Google Cloud Storage, as required by Google Prediction API.