How to Properly Deploy ML Models as Flask APIs on Amazon ECS
With the wild success of ChatGPT it is becoming apparent just how much AI technology will impact our lives. However, unless those amazing ML models are made available for everyone to use and deployed properly to address high user demand, they will fail to create any positive impact on the world. Hence, why it is so important to be able to not only develop AI solutions, but also know how to deploy them properly. Not to mention that this skillset will make you vastly more valuable on the marketplace and open career opportunities to take on the lucrative roles of ML Engineering.
In this post we're going to deploy an XGBoost model as a Flask API using the Gunicorn application server on Amazon Elastic Container Service. The model will recommend a **** Dachshund or a German Shepherd puppy based on how big someone's home is.
