In my last post I had written about deploying models on AWS. So, I though it would only be fitting to write one for GCP, for all the GCP lovers out there.
GCP has a service called the AI Platform which, as the name suggest, is responsible for training and hosting ML/AI models.
Just like the last post, this post, through a PoC, describes -
How to add a trained model to a Google Cloud bucket Host the saved model on the AI Platform Create a Service Account to use the model hosted on AI Platform externally Make a Streamlit app to make a UI to access the hosted model All the code can be found in my Github repository.
Quite a while back, I had written a post in which I described how to package your Machine Learning models using Docker and deploy them using Flask.
This post, through a PoC, describes -
How to package your model using Docker (similar as last post) How to push the Docker container to Amazon ECR Add a Lambda Function for your model Make a REST API using Amazon API Gateway to access your model Automate the whole process using Github Actions, so that any updates to the model can take effect immediately Make a Streamlit app to make a UI to access the REST API (for the model deployed on AWS) All the code can be found in my Github repository.
After my last post on deploying Machine Learning and Deep Learning models using FastAPI and Docker, I wanted to explore a bit more on deploying deep learning models. My last post discussed a server-side method for deploying the model. This post will discuss client side frameworks and techniques to deploy those models such that they work directly on the client side.
In this tutorial I will be giving an overview of three frameworks, Tensorflow.
The goal of this blog post is to make an API to get predictions from a pre-trained ML model and how we can do that in a fast manner using FastAPI and also be able to ship it using Docker.
This method does not scale well as it does not support caching and cannot handle much load. However, this can be a good instructional post on how you can deploy those models and use them for small low-scale projects, say a hackathon.