Master Designing, Integrating & Deploying Enterprise Ai Apps

[LeeAndro]

Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: aac, 48000 Hz
Language: English | VTT | Size: 1.58 GB | Duration: 5h 30m

How to divide complex problem into simple & manageable parts using microservices style architecture ?

What you'll learn

A complete end-to-end solution consisting of 3 distributed applications using asyncio, flatbuffers, NATS and Docker

Translate the requirements of a big and complex machine learning project into a scalable solution

Foundations, insights and practical usage of Asynchronous IO in Python

How to design high performance, low resource and future-proof data formats & protocols using Flatbuffers

Loosely coupled distributed app development using Message Bus (NATS)

Packaging, Deploying & Upgrading applications using Docker & Docker Compose

Practical code examples to support the concepts taught in this course and the fully developed final solution

Requirements

A good working knowledge of Python Language

Description

Target Audience

Machine Learning Eeers & 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 eeer 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.

Mastering how to make AI applications integrate with other applications while ensuring scalability and upgradability will offer you a competitive advantage over others.

The good news is that mastering them is not difficult at all!

How is this course taught?

My teaching style covers 3 key aspects of mastering any technology:

Intuition

Theory

Code

For any solution first I describe the overall goal, its associated challenges, and how to break down a big complex problem into manageable components. This process of simplifying the problems into components will guide you in identifying & selecting the best technology to use. I then explain the why, what & how of the selected technologies (AsyncIO, NATS, Flatbuffers, Docker) with code examples. These code examples start simple and I then iteratively add features to bring them to the level of real-world applications.

I have taken immense care in preparing the material that has great animations to help you develop intuition behind the solutions.

I have made sure that coding sessions follow an iterative development style and more importantly are clear & delightful.

All the source code from the iterative cycles as well as full end to end solution has been provided in the resources.

Who this course is for:

Machine Learning eeers

Data Scientists

Software Eeers



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