A tutorial on how to get started with Tensorflow Extended and how to design and execute a Deep Learning pipeline
A side-by-side comparison of JAX, Tensorflow and Pytorch while developing and training a Variational Autoencoder from scratch
How to develop and train a Transformer with JAX, Haiku and Optax. Learn by example how to code Deep Learning models in JAX
An introduction to JAX, its best features alongside with code snippets for you to get started
What is Kubernetes? What are the basic principles behind it? Why it might be the best option to deploy Machine Learning applications? What features it provides to help us maintain and scale our infrastructure? How to set up a simple Kubernetes cluster in Google cloud?
Follow along with a small AI startup on its journey to scale from 1 to millions of users. Learn what's a typical process to handle steady growth in the userbase, and what tools and techniques one can incorporate. All from a machine learning perspective
Learn how to containerize a deep learning model using Docker. Start with the basic concepts behind containers, package a Tensorflow application with Docker and combine multiple images using Docker compose
Serving a Tensorflow model to users with Flask, uWSGI as a web server and Nginx as a reverse proxy. Why we need both uWSGI and Flask, why we need Nginx on top of uWSGI and how everything is connected together?
How to expose a deep learning model, built with Tensorflow, as an API using Flask. Learn how to build a web application to serve the model to the users and how to send requests to it with an HTTP client.
How to train your data in multiple GPUs or machines using distributed methods such as mirrored strategy, parameter-server and central storage.
How to create a VM instance in Google cloud, transfer a deep learning model and run a training job using external data from cloud storage
Building a custom training loop in Tensorflow and Python with checkpoints and Tensorboards visualizations
How to optimize the data processing pipeline using batching, prefetching, streaming, caching and iterators
How to develop high performance input pipelines in Tensorflow using the ETL pattern and functional programming
A guide on how to debug machine learning code and how to use logs to catch errors in production (including a set of useful Tensorflow functions to make your debugging life easier)
Explore unit testing in tensorflow code using tf.test(), mocking and patching objects, code coverage and different examples of test cases in machine learning applications
A deep learning python project template, object oriented techniques such as abstraction, inheritance and static methods, type hints and docstrings
An article course on how to write and deploy deep learning systems in production. python code optimization, cloud hosting and system design
How to buld a Convolutional neural network library using C++ and OpenCL
How to buld a neural network library using C++ and OpenCL