Joshua Arvin Lat
Joshua Arvin Lat is the Chief Technology Officer of NuWorks Interactive Labs. He previously served as the CTO of 3 Australian-owned companies and startups. He is also an AWS Machine Learning Hero and has spearheaded and led the Machine Learning Zero-to-Hero online international events. For the past couple of years, he has been sharing his knowledge in several international and local conferences and events to discuss practical strategies for companies and professionals. He is also the author of a Machine Learning and Machine Learning Engineering book called Amazon SageMaker Cookbook: Practical Solutions for Developers, Data Scientists, and Machine Learning Engineers using R and Python (to be released this year )
Pragmatic Machine Learning and ML Engineering in the Cloud with Amazon SageMaker
It is not an easy task to design and build systems in the cloud that involve Machine Learning and Data Science requirements. It also requires careful planning and execution to get different teams and professionals such as data scientists and members of MLOps teams to follow certain processes in order to have a sustainable and effective ML workflow. In this tutorial, I will share the different strategies and solutions on how to design, build, deploy, and maintain complex intelligent systems in AWS using Amazon SageMaker and Python. Amazon SageMaker is a fully managed machine learning service that aims to help developers, data scientists, machine learning practitioners, and MLOps teams manage machine learning experiments and workflows.
We will start by learning some of the important concepts and patterns used in production environments through some initial short laboratory exercises using Python and the SageMaker Python SDK. As we take on more complex topics, we will work on a couple of practical solutions and examples using the different features and capabilities of Amazon SageMaker to solve the different needs of data science and MLOps teams.