How to Build, Train, Test, and Deploy ML Models

At times it can be tough to figure out how to work with Machine Learning models, especially if you don’t have the right tools. Watson Studio on the IBM Cloud provides a one-stop-shop for all things regarding building, training, testing, and deploying Machine Learning models.

At times it can be tough to figure out how to work with Machine Learning models , especially if you don’t have the right tools. Watson Studio on the IBM Cloud provides a one stop shop for all things regarding building, training , testing and deploying Machine Learning models. Come gain an understanding and appreciation of the practicalities of data science using Watson Studio, IBM’s data science and AI platform. Learn how to leverage data within Watson Studio, to train , test and deploy Machine Learning, to solve data-intensive problems.

This workshop will lead participants through a discovery of Watson Studio, IBM’s Cloud Data Science and AI platform.

Hands-on lab will contribute to acquiring practical skills, by providing a first hand approach to the methodologies and tooling, through examples and hands-on experience.

Attendees will leave with an understanding and appreciation of the many facets of Data Science, and how to begin understanding patterns and deriving insight from their data.

Come learn how to quickly store, train, test and deploy custom visual recognition and natural language model with Watson Studio! You will learn how to quickly and accurately tag, classify, and train and manage your visual and natural language data, using machine learning in a secure cloud environment! Learning Outcomes :

• Understand the Basics of Watson Studio
• Learn how to build, classify and train custom visual content and natural language using machine learning

Come learn how to use Juypter Notebooks in a secure cloud environment to analyze data. You will learn how to prepare and normalize the data for machine model building, split data into training sets for model validation, train the model by using machine learning algorithms and finally deploy the model so that it can be accessed outside of the notebook. Learning Outcomes :

• Basics of Jupyter Notebooks on Watson Studio
• Learn how to normalize, split, train with Python and deploy data in a Jupyter Notebook

Pooja Mistry (@poojamakes) is a Developer Advocate for the IBM Cloud Platform. She works on expanding the reach of IBM’s technology to the developer community. Her area of interest includes prototyping with Node-RED, analyzing data, creating custom models with AI/ML services, NLU/NLP, building chatbots, and using open source libraries and SDKs to develop fun and exciting projects! Pooja is passionate about the intersection of technology and the community. She loves to learn, teach, and share her knowledge with the developer ecosystem. She strongly believes in helping new technologists get up and running with technology and feel confident in their abilities to make!