Following our previous posts on Image processing in BigML, the turn has arrived to discuss automation for datasets with images. As BigMLers will already know, BigML offers automation that can be used on the server-side thanks to the WhizzML language, which has been designed especially for Machine Learning tasks, but it also offers client-side bindings for many programming languages. In this post, we'll review the Python bindings approach. From the Machine Learning point of view, each image is a source for many fields, like the light levels in some regions, their color information, or the shapes it contains. In that sense, we need to think of an image as a composed field, just as we could think about other composed fields like text or date-time fields.

Creating a Machine Learning model is a difficult task because we need to write a lot of code to try different models and find out the performing model for that particular problem.

Machine Learning and Artificial Intelligence are the most used terms nowadays. Everyone nowadays wants to learn and experiment with Machine Learning but it doesn't seem easy, because you need to learn a lot about different concepts of machine learning and how to implement them. There are many learning interfaces in the market that makes machine learning an easy task for non-experts but still, you need knowledge of data science and different machine learning models. H2O AutoML is an easy to use machine learning interface which automates the whole process of Machine Learning model selection by automatically training and tuning the model in a stipulated time limit. A user just needs the dataset, should know the target variable and set a time limit, with all this he can test different machine learning model and select the best model with results.

We often hear how widespread artificial intelligence has become and how it is increasingly affecting our daily lives. But for most people the nature of the tech is a mystery -- we know it's powerful but we don't know what makes it tick, much less how it's built. While research over the past decade has greatly advanced model structures and learning methods, creating algorithms remains relatively time-consuming and difficult. This has prompted research into automation efforts, or AutoML, aimed at the simplification and democratization of AI. In a recent ICML paper, Google researchers propose an "AutoML-Zero" approach designed to automatically search for machine learning (ML) algorithms from scratch, requiring minimal human expertise or input.

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.