How to Deploy and Productize Machine Learning Models

Did you know that only 20% of companies have successfully deployed a model from their Machine Learning and AI solutions?

AI Engineers, ML Engineers and Data Scientists can create great models but getting them deployed is the real challenge. They need to know how to deliver production AI services and integrate it with portal or basic web/mobile applications in a flexible and reliable way.

During this talk, I will take you through ways you can deploy and productize your Machine Learning models using various tools and platforms such as Microsoft Azure Machine Learning Studio, Amazon SageMaker.

Speaker: Sapphire Duffy, AI Engineer, Kainos

CONNECT DIGITAL 2020
FRIDAYS IN JUNE | JUNE 5, 12, 19 | ANYWHERE

WWCode is taking its signature inclusive developer conference online so you can safely join from anywhere. We’re making the conference free for everyone to ensure that you can stay connected when you need it most.

At WWCode CONNECT Digital, you’ll experience all of the great things you’ve come to expect from a WWCode conference, including a digital swag bag, opportunities to connect with your community, and great technical talks from industry leaders, experts, and peers who are changing the world through technology everyday. Explore topics like blockchain, mobile, web development, cloud, datascience, security, and AI/ML through talks, workshops, code labs, and demos. Get actionable advice, share best practices, and meet people who are using their tech superpowers for good.

Experience tech, reimagined at WWCode Connect Digital.

[drodri]

Great presentation! here's a quick outline for those interested in skipping around:

04:25 - Gap of knowledge
06:26 - Project Lifecycle
08:13 - Data Collecting and Cleaning
13:22 - Deploying the model
20:12 - Monitoring the model
25:02 - What's required to Productize a ML model?
28:00 - Cloud Services for Machine
30:12 - SageMaker Benefits
33:19 - SageMaker Workshop
34:11 - Recommended Resources