Learn about Apache Airflow and how to use it to develop, orchestrate and maintain machine learning and data pipelines
A tutorial on how to get started with Tensorflow Extended and how to design and execute a Deep Learning pipeline
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
An article course on how to write and deploy deep learning systems in production. python code optimization, cloud hosting and system design