Build a scalable Machine Learning model with a secure API

*This event is in Partnership with "London Project Data and Analytics Meetup * https://www.meetup.com/London-Project-Data-and-Analytics-meetup

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The term "Machine Learning" or "AI" is fairly well understood by a lot of people but when it comes to actually building a machine learning model and applying it to real data, what does that mean and how is that done?

Using no code you will create a Machine Learning model from data and you will see how IBM Auto AI performs the complex role of a data scientist. You will create a simple spreadsheet containing data you might get from a sensor. This allows you to get familiar with how a model is created from real data. You will create a Machine Learning model from the data with no code (using the powerful IBM Auto AI service). Critically, you will then be able to see what this abstract idea of a Machine Learning model looks like, and how it can be practically used. Here, “practically used” means put somewhere where it can run, can be tested with some manually input data and can be called from code/your app/system.

Pre-reqs
None, but an IBM Cloud account is required if you want to follow along (https://ibm.biz/BdfApv).

🎓 What you will learn

• How to take data and build a machine leaning model from it using IBM Auto AI
• How to deploy that model so it can be used securely and at scale
• How to test that mode
• How to call that model from your code/your app/system

💻 The tech

• Watson Machine Learning (https://www.ibm.com/watson)
• Watson Studio (https://www.ibm.com/cloud/watson-studio)
• IBM Auto AI (https://www.ibm.com/cloud/watson-studio/autoai)

🎙 Speaker: Ian Marshall, Technical Consultant, UK IBM Hybrid Cloud Build Team

Ian Marshall is an IT Specialist with over 30 years’ experience at IBM. With technical systems background he currently focuses on platform deployment and management within cloud and on prem. Using IBM's Cloud Pak for Multi-cloud Management running on Red Hat OpenShift and associated modules to conduct metering, monitoring, governance of containerised and traditional workloads. He works with new technology adopters with their choice of and understanding of IBM's cloud services and subsequent integrations into their solution.