How to use Watson Machine Learning

As a component of IBM Watson® Studio, IBM Watson Machine Learning helps information researchers and engineers speed up AI and AI sending on IBM Cloud Pak® for Data. Convey AI models at scale across any cloud on open, extensible engineering. With IBM you can: 
  • Convey any models including AI and profound learning models and choice improvement models
  • Progressively retrain models with persistent learning
  • Consequently, create APIs to assemble AI-fueled applications through DevOps
  • Oversee and screen models for model float, inclination, and hazard
  • Bring any tasks into creation, including open-source, outsider, and IBM apparatuses

What is WML and for what reason would we say we are building it? 

WML is a Bluemix administration that empowers clients to perform two key activities of AI.  


this is the way toward refining a calculation so it can 'gain' from a dataset. The yield of this activity is known as a model. A model envelops the learned coefficients of numerical articulations. 


the activity of anticipating a result utilizing a prepared model. The yield of the scoring activity is another dataset containing anticipated qualities.  

WML is intended to address the requirements of two essential personas: 

Data Scientists

Make AI pipelines that influence information changes and AI calculations. They commonly use scratchpad or outer tooling to prepare and assess their models. Information researchers regularly team up with Data architects to investigate and comprehend the information. 


construct keen applications that influence the expectations yield by AI models.

What's New Services? 

Here are a few features of the new highlights: 

  • Models as First-Class Entities: In Watson Data Platform and Data Science Experiencewe've made "Models" a top notch element. Models are currently connected with "Ventures". By partner Models with Projects, clients can undoubtedly share those models and work together with others. After some time, we mean to add extra community highlights to Models including remarks, rendition control, and the capacity to import Models made outside of DSX, for example, those adjusting to the PMML exchange design. The sending of Models made in the IBM SPSS offering are now upheld.
  • Model Builder: During the beta we presented another UI called "Model Builder". The goal of this UI was to work on the production of Machine Learning models utilizing a more natural visual developer experience. Criticism from our Beta members, it immediately became clear that the interface essentially was not natural enough and hence not simple to utilize. In light of this criticism, our plan group returned to the planning phase and returned with another plan for a less complex adaptation of the stream that gives the client two choices in the Model Builder: Automatic and Manual. The Automatic Path will consequently plan information for preparing and will give the client suggestions on the calculation and strategy to utilize dependent on the qualities of the information. In this programmed way, the client rapidly set up the information, train a model and send that model in a couple of snaps. Gartner as of late shared a report expressing that by 2020 over 40% of Data Science Tasks will be robotized. Utilizing work from IBM Research, this programmed way in the Model Builder is our initial phase around there.
  • Scratch pad insight: information researchers love note pads and are now preparing models in Scala, R and Python utilizing that interface. To oblige these clients, we are making the WML APIs accessible inside Jupyter journals in the IBM Data Science Experience. Presently it is feasible to cover the start to finish stream without leaving the journal! Train the model, save the model to a project and convey that model just by calling our natural APIs! 
  • Partner Watson Machine Learning administration with Projects: a new article in Forbes about the accepted procedures for joint effort between information researchers expressed that one of those practices is to share the computational climate. Consequently, we've empowered clients in DSX to not just offer information and examination resources inside the task, yet in addition to share fundamental administrations like Spark. Information researchers burn through tremendous measures of time on the grounds that computational conditions aren't as of now shared of course. We accept that information researcher shouldn't have to ensure that they are running a similar variant of a Python library as their associate to execute a similar code so we're making it conceivable to share the fundamental assistance in the venture.
  • Coordinated effort between the App Developer and Data Scientist: Watson Data Platform empowers cooperation between various personas: information researchers, application designers, information architects and investigators. Application designers will currently approach in the Bluemix Dashboard to the entirety of the models made in DSX, just as the capacity to make to effortlessly coordinate the ML APIs into their applications. For application designers who are new to the WML administration, we've caused accessible various assets to assist them with a beginning, including 3 examples ML models and new application layouts.

How to use Watson Machine Learning

There is a ton of new functionality and we can’t wait for you to try it out! We are providing 6 tutorials to help get you started:

Jupyter Notebooks:

  • Scala Jupyter Notebook end-to-end tutorial: Train, Save and Deploy a SparkML model
  • Python Jupyter Notebook end-to-end tutorial: Train, Save and Deploy a SparkML model
  • Python Jupyter Notebook: Recognition of hand-written digits, train, save and deploy Scikit Learn model
  • Scala Jupyter Notebook Auto-Modeling with Cognitive Assistance (CADS)
  • Putting a human face on machine learning

Automatic model builder:

  • Model Builder — Build a naive-Bayes model
  • Model Builder — Build a logistic regresion
  • Model Builder — build a predictive analytic model to determine whether a person has chronic kidney disease
  • Tutorial: Putting a human face on machine learning

Visual Modeling:

  • Create a flow using SparkML
  • Create a flow using SPSS engine