Learn to scale your machine learning workflows at will. — I work at Yodo1, a leading mobile game platform company where we value streamlined efficiency and achieve it through automation. In this post, I am going to introduce two tools I have used on the job that can help you build efficient and scalable machine learning pipelines. 1. Papermill We all love…

Autoencoder in action — What is an anomaly? Lexico defines it as: something that deviates from what is standard, normal, or expected. This isn’t a great definition because it implies a perfect observer. In the real world, an anomaly is a phenomenon that happens all the time either due to the imperfect observational capabilities…

Learn how to ship in this 5-minute read on TensorFlow Serving In a previous series, I covered the nuts and bolts of developing machine learning algorithms with TensorFlow. Now it’s time to demonstrate how to release our models into production. In this post, you’ll learn: Why we use TensorFlow Serving; …

Why TensorBoard? — In my previous three posts, I covered tf.keras and other features of TensorFlow 1.0 and 2.0 such as computational graphs, Eager execution, and AutoGraph. With this post, I’ll introduce you to the super useful tool, TensorBoard! After reading, you should understand: Why TensorBoard is useful; How TensorBoard works, and what…

why tf.keras? one ring to rule them all! — In the first two parts of this series, I covered Computation Graphs, plus Eager Execution and AutoGraph. However, as powerful as Graphs and Eager Execution may be, they simply aren’t that pleasant to use. Whether you’re a seasoned researcher or simply a newcomer, the complexity of the TensorFlow ecosystem will…

In my previous post, I covered computational graphs with TensorFlow 1.0. As we’ve learned, these graphs are stable and performant but the non-imperative way of dealing with them puts an extra cognitive load on developers. With the arrival of TensorFlow 2.0, there was a paradigm shift. In this post, I’ll…

TensorFlow, a machine learning library created by Google, is not known for being easy to use. In response, TensorFlow 2.0 addressed a lot of the pain points with eager mode and AutoGraph features. Thing is, while these additions solve a lot of problems, they also complicate the existing programming model. …

An Introduction to the Anatomy and Core Concepts of the Python Execution Model — Python has become a de facto programming language for machine learning. However, if you are jumping into Tensorflow, PyTorch, or any other Python machine learning framework without previous Python experience, you might be overwhelmed by its complexity. (P.S. I’ll be writing about the Tensorflow programming model soon. …

The best way to learn is by doing! I’m excited to go from backend engineering to AI project development — I have been developing backend applications for the last few years. After reading Hackers & Painters I was captivated by the Lisp, to this day still, am. And under the influence of Paul and Hacker News,