What Is IBM Watson Studio?

This article provided background information on IBM Data Science methodology, what role IBM Watson Studio can play, and a preview of what will be covered in this learning path.

Table of Content

1. IBM Watson Studio Introduction
2. Watson Studio Instant Value For Enterprises With AI
3. Comprehensive Set Of Tools For End-To-End AI Workflows
4. IBM Watson Machine Learning Service
5. Conclusion

IBM Watson Studio Introduction

IBM Watson Studio gives you the environment and tools to solve business problems by working collaboratively with data. You can choose the tools you need to analyze and visualize the data; to cleanse and shape the data; ingest streaming data; Or to build, train, and deploy machine learning models.

With IBM Watson Studio, you can

• Create website projects to organize resources (such as data connections, data assets, collaborators, and notebooks) to achieve analytics goals.
• Access data from connections to your cloud or on-premises data sources.
• Upload files to the project's object storage.
• Create and maintain data catalogs for searching, indexing, and sharing data.
• Refine the data by cleaning and shaping the data to prepare the data for analysis.
• Perform data science tasks by creating a Jupyter Notebook for Python or Scala to run the code that processes the data, and then view the results inline. Alternatively, you can use RStudio for R.
• Build, test, and deploy machine learning and deep learning models.
• Visualize your data.

Watson Studio Instant Value for Enterprises with AI

Till date, there was a distinction between data experts and domain experts. Only highly technical professionals in IT can organize and understand large amounts of data. Only domain experts can successfully convert data into the rich knowledge required by AI. But domain experts and IT professionals work in silos, with different tools and no visibility into each other's work. The result was AI that fell short of its promise to augment people's expertise.

Comprehensive set of tools for end-to-end AI workflows

At Watson Studio, we offer a choice of tools for the full AI lifecycle that includes best-in-class open source and IBM tools. You can choose between code or no-code tools to build and train your own ML/DL models, or easily re-train and adapt the pre-trained Watson API. Use rich capabilities and controls to fine-tune your models and automate your models' feedback loops so that they become smarter over time and adapt to constantly changing conditions.

Connect and prepare data

To implement AI, the first step in the workflow begins with connecting and accessing data. Data scientists spend 80% of their time finding and preparing data, and 57% of data scientists said that cleaning and organizing data is the least enjoyable part of their job. The problem is not just limited to data scientists. Business analysts face similar struggles obtaining the data they need to create reports – often waiting weeks for their IT team to extract data from source systems. To address the issue, we provide integrated capabilities to refine and dispute data with Data Refinery, a tool that makes fast, self-service data preparation a reality.

Watson tools and pre-trained models

Once you have joined the data, the next step is to build and train the model. Application developers can get started with best-in-class pre-trained Watson APIs that are among the most accurate in the industry. These models will understand emotion, classify subjects in text, identify personality insights, or recognize objects in a picture. We provide access to well documented APIs with samples and code snippets in most popular programming languages.

Choice of frameworks and best-in-breed tools

The machine and deep learning landscape is constantly changing. In Watson Studio, you will find support for the most popular tools giving users the option to easily train, save, deploy, and automate the re-training of those models. They come pre-installed and we manage the underlying infrastructure for you so you can focus on your projects.

IBM Watson Machine Learning Service

A key component of IBM Watson Studio is the IBM Watson Machine Learning Service and its set of REST APIs that can be called from any programming language to interact with machine learning models. The focus of the IBM Watson machine learning service is deployment, but you can use IBM SPSS Modeler or IBM Watson Studio to author and work with models and pipelines. SPSS Modeler and IBM Watson Studio both support Spark MLlib and Python scikit-learn and offer a variety of modeling methods that are derived from machine learning, artificial intelligence, and statistics.

Conclusion

One of the objectives of this learning path is to show how IBM Watson Studio provides, in addition to Jupyter Notebooks for Python, Scala, or R, alternative ways to go through a similar process that can be faster and without programming skills. can be obtained. In essence, these mechanisms are SPSS Modeler Flow, which allows a data scientist to build a model purely graphically by defining a flow, and the IBM AutoAI graphical tool inside IBM Watson Studio, which goes a step further than SPSS by providing a semi-automated approach. For building, evaluating, deploying and testing machine learning models. At the same time, the learning path shows how IBM Watson Studio provides out-of-the-box capabilities for profiling, visualizing and transforming data, again without the need for any programming.