Best practices on Machine Learning infrastructure. How to build, maintain and scale production-ready deep learning systems.

Data Processing · MLOps · Software

A complete Apache Airflow tutorial: building data pipelines with Python

Learn about Apache Airflow and how to use it to develop, orchestrate and maintain machine learning and data pipelines

MLOps · Software · Tensorflow

Tensorflow Extended (TFX) in action: build a production ready deep learning pipeline

A tutorial on how to get started with Tensorflow Extended and how to design and execute a Deep Learning pipeline

MLOps · Software

Introduction to Kubernetes with Google Cloud: Deploy your Deep Learning model effortlessly

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?

MLOps · Software

Scalability in Machine Learning: Grow your model to serve millions of users

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

MLOps · Software

How to use Docker containers and Docker Compose for Deep Learning applications

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

MLOps · Software

How to use uWSGI and Nginx to serve a Deep Learning model

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?

MLOps · Software · Tensorflow

Deploy a Deep Learning model as a web application using Flask and Tensorflow

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.

MLOps · Software · Tensorflow

Distributed Deep Learning training: Model and Data Parallelism in Tensorflow

How to train your data in multiple GPUs or machines using distributed methods such as mirrored strategy, parameter-server and central storage.

MLOps · Software

How to train a deep learning model in the cloud

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

MLOps · Software

Deep Learning in Production: Laptop set up and system design

An article course on how to write and deploy deep learning systems in production. python code optimization, cloud hosting and system design