Master Designing, Integrating & Deploying Enterprise AI Apps

Target Audience

Machine Learning Engineers & Data Scientists

What is unique about this course & What will you learn?

Why What & How of designing, integrating & deploying Enterprise Level Data Science/AI/ML applications

How to translate requirements into scalable architectural components?

How to break a big complex problem into simple & manageable parts using microservices style architecture?

An End–to–End real–world enterprise–level machine learning solution

Asynchronous IO – Foundations & Writing I/O bound applications in python 3

NATS – A Cloud Native Computing Foundation open source project to connect distributed applications

FlatBuffers – A language–independent, compact and fast binary structured data representation language

Docker & Docker–compose – The gold standard in deploying and orchestrating applications

Why should you learn all this?

A statistical or deep learning model is not an application rather it is an important component of a solution to real–world problems. A sophisticated solution to a complex problem generally consists of multiple applications written using different languages and running on a cluster of machines.

Your role as a Data Scientist and Machine Learning engineer is not just limited to a model building or tuning its performance rather it is expected that at the very minimum you will design your applications so that they can easily integrate with other applications of a big solution as well as are easily deployable using modern DevOps methodologies.