Benjamin Tan Wei Hao

Build a Machine Learning Platform (From Scratch)

Build a Machine Learning Platform (From Scratch) teaches you to set up and run a production-quality machine learning system using open source tools. Chapter-by-chapter, you’ll assemble a delivery pipeline for an image classifier and a recommendation system, learning best practices as you go. You’ll get hands-on experience with the most important parts of the machine learning workflow, including orchestrating pipelines; model training, inference, and serving; and monitoring and explainability. Soon, you’ll be deploying models that are fast to production and easy to maintain and scale.

Building an ML Pipeline with Kubeflow

Putting machine learning into production can often be a complex task. The Kubeflow platform helps streamline this process with simple and scalable ML workflow deployment. In this liveProject, you’ll put Kubeflow into action to help your team roll out their new license plate recognition deep learning system.

You’ll help data scientist colleagues by standardizing their working environment, and automating away many tedious and error-prone tasks. Your challenges will include restructuring a complex deep learning project to make it Kubeflow-friendly, and developing reusable components that can be transferred to other machine learning pipelines.

The Little Elixir & OTP Guidebook

The Little Elixir & OTP Guidebook gets you started writing applications with Elixir and OTP. You'll begin with the immediately comfortable Elixir language syntax, along with just enough functional programming to use it effectively. Then, you'll dive straight into several lighthearted examples that teach you to take advantage of the incredible functionality built into the OTP library.