IBM Watson Machine Learning – General Availability

Share this post:

What is Watson Machine Learning (WML)?

After 12 months in beta and with input from hundreds of beta users, we are excited to announce the general availability of the Watson Machine Learning (WML) service.  WML is designed to address the needs of two primary personas:

  • Data Scientists: create machine leaning pipelines that transform data and apply machine learning algorithms to train predictive models.They typically use notebooks such as Jupyter, that is built into tools like the IBM Data Science Experience (DSX), to train and evaluate these models.
  • Developers: build intelligent applications that leverage the predictions generated by machine learning models.

WML  allows users to deploy and monitor machine learning models that were trained using SPSS, Scikit Learn and Spark ML.

  • Training: the process of applying an estimation algorithm to ‘learn’ from a data set which generates a model that can make predictions related to that data set.
  • Scoring: the operation of predicting an outcome using a trained model.


Automated Model Building

WML’s model building wizard is exposed through IBM Data Science Experience (DSX)  to provide a guided workflow that walks users through creating machine learning models.  Start by selecting to either build manually and choosing the algorithms best suited for your needs, or select the Automatic option and get help selecting the algorithm that would work best for your data. The Automatic option guides you through preparing data for training, and offers recommendations on algorithms to apply based on characteristics of your data.


Model Deployment

Training models are not the primary value proposition of the WML service.  Data scientists already have a comprehensive suite of open source tools to perform these training tasks. Instead, the challenge now is to operationalize those models.  How can machine learning models be deployed into production to create business value? And once those models are in production, how do they adapt and evolve over time? These are the challenges that WML is intended to address.  Once you’ve trained your model, WML helps you easily deploy to a REST endpoint that will automatically scale as needed.  The starting point is WML’s tooling which displays all the models you’ve uploaded via the APIs or shared with WML via DSX.   These models can be created in SPSS, Jupyter Notebooks, WML’s Model Builder UI, or imported from outside using your own model training tools as long as they export to one of our supported model formats.
 List of Trained Models

Integration with Data Science Experience

Once a model is trained in Data Science Experience, data scientists can easily collaborate with other team members (e.g. app developers) by sharing their models. Once shared, these models appear in WML and can be quickly deployed to a REST endpoint.  And speaking of REST, WML provides a powerful set of REST APIs which are fully documented here:
DSX Integration
Additional screens guide users through subsequent model build steps.
  • Upload your dataset followed by guided data cleansing
  • Specify the target variable to predict
  • Selecting the type of model to build (e.g. which machine learning algorithm to apply to the data).

Several tutorials are provided to guide you through using WML’s new Model Builder to create different model types.  Once a model is created, it can then be deployed as in the next section.

Like the other Watson services, IBM Watson Machine Learning is available on Bluemix, IBM’s open cloud development platform.  If you haven’t already tried it — or you have and want to see how WML improves upon it — head over to IBM Data Science Experience to check out this exciting new capability!

See you inside of WML and please provide feedback!!
IBM Data Science Experience team

More stories
June 13, 2018

Elinar Takes the Mystery out of GDPR with help from IBM Cloud and Watson

Even though GDPR May 25 deadline has come and gone, many companies are still scrambling to meet compliance. Here at Elinar, we are busier than ever. Our data discover solution, AI Miner, built with Watson APIs, for use on the IBM Cloud platform, can quickly uncover mysterious data with a quick scan on your data mass for records which have privacy settings that need updating to meet GDPR compliance.

Continue reading

May 23, 2018

How to rapidly develop applications with microservices (part 1)

This is the first post in a series on how to move your team towards the best long-term cloud platform adoption decision. Since adopting a cloud platform involves a significant commitment, and implies the confirmation that comes from previous work on one or more pilot projects, the primary goal of this series is to get you to the step of defining an appropriate cloud-based pilot project for your team.

Continue reading

February 12, 2018

A/B testing using App Launch on IBM Cloud Services

Yes, its mobile, mobile everywhere!! Every business aspires to have its own mobile app, hence the need for a mobile app is rapidly growing. To combat the competition, some app owners work hard on innovation, few others on engaging the customers and some try to evaluate their marketing strategies.

Continue reading