BigML is happy to announce new options to analyze your text fields. With the newly added capabilities, supervised and unsupervised models will be able to identify more patterns in your text data. The configuration options include:

• 15 new languages! BigML can now process text in 22 different languages: Arabic, Catalan, Chinese, Czech, Danish, Dutch, English, Farsi/Persian, Finish, French, German, Hungarian, Italian, Japanese, Korean, Polish, Portuguese, Turkish, Romanian, Russian, Spanish, and Swedish.
• The maximum n-gram size to consider for your text analysis: bigrams, trigrams, four-grams and five-grams.
• New stop words removal techniques like the ability to remove stop words for all languages and the degree of aggressiveness for stopword removal.
• Stemming for the new languages.
• Filters to exclude certain groups of uninteresting words from your models such as HTML keywords, non-dictionary words, numeric digits or non-language characters. You can also choose to exclude unigrams from your text and keep only n-grams that include more than one word.

You can configure these options from your source so they will be taken into account by all your models. Moreover, as you iterate your model, you can easily configure these options for your topic models regardless of your original source configuration.

The BigML Zapier app allows you to easily automate your Machine Learning workflows without any coding. Import your data in real-time from the most popular web apps and the BigML app will automatically make predictions for you as your new data is being generated. Then you can simply choose which service or app you want to send the predictions to, and the BigML Zapier app will take care of integrating those predictions into your processes. See some workflow examples and try it for free today!

OptiML is an optimization process for model selection and parametrization that automatically finds the best supervised model to help you solve classification and regression problems.

Using Bayesian Parameter Optimization, OptiML creates and evaluates hundreds of supervised models (decision trees, ensembles, logistic regressions, and deepnets) and returns a list of the best models for your data. Eliminating the need for manual, trial-and-error based exploration of algorithms and parameters, OptiML saves significant time and provides improved performance for Machine Learning practitioners of all levels.

To solve Machine Learning problems, you usually need several iterations that employ different algorithms and configurations to build your final models and workflows. Now, BigML makes it even easier and faster for you to find the right resources at a glance from among many that belong to the same project by listing the values of your configured parameters for each resource.

As a complement to our popular decision trees visualization and the sunburst, we are launching a third view for your models: the Partial Dependence Plot. This heatmap chart also allows you to analyze the marginal impact of each input field on predictions for classification and regression models built by using ensembles and logistic regressions.

Solving a Machine Learning problem is an iterative process that requires the creation of a great number of intermediary datasets, models, evaluations and predictions to get the final model. Now, BigML simplifies it keeping your account organized and up-to-date by allowing the deletion of multiple resources at the same time. Just click the deletion icon found in the resources listing in the Dashboard, and select the resources to be deleted.

We are happy to announce BigML Certifications, for organizations and professionals that want to master BigML to successfully deliver real-life Machine Learning projects. These courses are ideal for software developers, system integrators, analysts, or scientists, to boost their skill set and deliver sophisticated data-driven solutions. We offer two separate courses, each of them consisting of 4 weekly online classes of 3 hours each:

• Certified Engineer: all you need to know about advanced modeling, advanced data transformations, and how to use the BigML API (and its wrappers) in combination with WhizzML to build and automate your Machine Learning workflows.

• Certified Architect: learn how to implement your Machine Learning solutions so they are scalable, impactful, capable of being integrated with third-party systems, and easy to maintain and retrain.

If you successfully pass the certification exam, BigML will award you with a diploma. In addition, BigML Certified Partners will receive business referrals that help them source new Machine Learning projects.

Flatline is BigML’s Lisp-like language that enables you to programmatically perform an array of data transformations, including filtering and new field generation. Flatliner is a handy code editor (available in our Labs section) that helps you test your Flatline expressions.

With the Winter Release, you'll now be able to add sources to BigML through Google Cloud Storage and Google Drive, similar to our prior integrations with Dropbox and Azure Data Marketplace. You can also now log into BigML using your Google ID.

We're happy to introduce Projects to help you organize your machine learning resources. You only have to create a new project using the web interface or the API resource and update a new source to this project. All the new resources created from this source will be associated to the same project.

Our team is constantly working on innovative applications built on top of BigML's API. We're now unveiling several of these in early access through our BigML Labs.

BigML is very pleased to announce that we've launched this new website to better serve our customers in Australia & New Zealand. This site will contain all of the content and functionality of our https://bigml.com site, but will provide faster performance as well as some localized content (e.g., local events and local training opportunities). Read more about in this blog post.