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Building Deploying and Scaling LLM Powered Applications

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Building Deploying and Scaling LLM Powered Applications, available at $49.99, has an average rating of 4.6, with 19 lectures, based on 17 reviews, and has 189 subscribers.

You will learn about You will Learn to Build a Complete Scalable Software Application Which Is Powered By a Large Language Model And Deploy It At Scale on Amazon Web Services You Will learn to Integrate Your Application's LLM Powered Backend with Streamlit UI Frontend You Will First Learn To Locally Test Your Application , Then Package It Using Docker And Finally Learn The Best Practices For Using Streamlit Inside Docker You Will Learn a Template & Best Practices to Inject your OpenAI's API Keys Into Your Containerized Application At Run Time You Will Learn To Address Vulnerabilities In Your Containerized Application And Best Practices To Resolve Them You Will Learn to Design Your System's Architecture Based On The Components And Design Choices In Your Application You Will Learn the Differences Between Horizontal Scaling and Vertical Scaling You Will Learn in Depth to Apply Serverless Deployment and Learn To apply Load Balancers and Auto Scaling To Your Application You Will Be Able To Apply Your Learnings To Build Deploy & Scale other LLM Powered Langchain Applications This course is ideal for individuals who are The focus of this course is to Introduce you to Machine Learning Engineering. This course would enable learners to build deploy and scale an end to end software application which in the backend is powered by a Large Language Model to generate results based on user given input. The main goal of this course is to teach a framework which users can repeat and apply to build other software applications which use LLMs from OpenAI, inject API Keys at run time to avoid key leakage and deploy their respective applications at Scale on Amazon Web Services. or This is an intermediate level course and is intended for Developers interested in Developing Deploying and Scaling LLM Powered Applications. The Target Audience for this course are : Software Engineers, Data Scientists, ML Engineers and AI Engineers. However these are definitely not hard requirements, and who want to learn building, testing and deploying software applications at scale are equally welcome. It is particularly useful for The focus of this course is to Introduce you to Machine Learning Engineering. This course would enable learners to build deploy and scale an end to end software application which in the backend is powered by a Large Language Model to generate results based on user given input. The main goal of this course is to teach a framework which users can repeat and apply to build other software applications which use LLMs from OpenAI, inject API Keys at run time to avoid key leakage and deploy their respective applications at Scale on Amazon Web Services. or This is an intermediate level course and is intended for Developers interested in Developing Deploying and Scaling LLM Powered Applications. The Target Audience for this course are : Software Engineers, Data Scientists, ML Engineers and AI Engineers. However these are definitely not hard requirements, and who want to learn building, testing and deploying software applications at scale are equally welcome.

Enroll now: Building Deploying and Scaling LLM Powered Applications

Summary

Title: Building Deploying and Scaling LLM Powered Applications

Price: $49.99

Average Rating: 4.6

Number of Lectures: 19

Number of Published Lectures: 19

Number of Curriculum Items: 19

Number of Published Curriculum Objects: 19

Original Price: $49.99

Quality Status: approved

Status: Live

What You Will Learn

  • You will Learn to Build a Complete Scalable Software Application Which Is Powered By a Large Language Model And Deploy It At Scale on Amazon Web Services
  • You Will learn to Integrate Your Application's LLM Powered Backend with Streamlit UI Frontend
  • You Will First Learn To Locally Test Your Application , Then Package It Using Docker And Finally Learn The Best Practices For Using Streamlit Inside Docker
  • You Will Learn a Template & Best Practices to Inject your OpenAI's API Keys Into Your Containerized Application At Run Time
  • You Will Learn To Address Vulnerabilities In Your Containerized Application And Best Practices To Resolve Them
  • You Will Learn to Design Your System's Architecture Based On The Components And Design Choices In Your Application
  • You Will Learn the Differences Between Horizontal Scaling and Vertical Scaling
  • You Will Learn in Depth to Apply Serverless Deployment and Learn To apply Load Balancers and Auto Scaling To Your Application
  • You Will Be Able To Apply Your Learnings To Build Deploy & Scale other LLM Powered Langchain Applications

Who Should Attend

  • The focus of this course is to Introduce you to Machine Learning Engineering. This course would enable learners to build deploy and scale an end to end software application which in the backend is powered by a Large Language Model to generate results based on user given input. The main goal of this course is to teach a framework which users can repeat and apply to build other software applications which use LLMs from OpenAI, inject API Keys at run time to avoid key leakage and deploy their respective applications at Scale on Amazon Web Services.
  • This is an intermediate level course and is intended for Developers interested in Developing Deploying and Scaling LLM Powered Applications. The Target Audience for this course are : Software Engineers, Data Scientists, ML Engineers and AI Engineers. However these are definitely not hard requirements, and who want to learn building, testing and deploying software applications at scale are equally welcome.

Target Audiences

  • The focus of this course is to Introduce you to Machine Learning Engineering. This course would enable learners to build deploy and scale an end to end software application which in the backend is powered by a Large Language Model to generate results based on user given input. The main goal of this course is to teach a framework which users can repeat and apply to build other software applications which use LLMs from OpenAI, inject API Keys at run time to avoid key leakage and deploy their respective applications at Scale on Amazon Web Services.
  • This is an intermediate level course and is intended for Developers interested in Developing Deploying and Scaling LLM Powered Applications. The Target Audience for this course are : Software Engineers, Data Scientists, ML Engineers and AI Engineers. However these are definitely not hard requirements, and who want to learn building, testing and deploying software applications at scale are equally welcome.

Are you ready to dive deep into the world of Machine Learning Engineering and build powerful software applications? Our Machine Learning Engineering course is designed to equip you with the skills and knowledge to harness the full potential of Langchain, integrate the OpenAI API, deploy applications on AWS Elastic Container Service, and efficiently manage scaling using Load Balancers and Auto Scaling Groups.

In this hands-on course, you’ll learn how to create robust ML applications from the ground up. We’ll start by mastering Langchain, a cutting-edge language model, and demonstrate how to seamlessly inject your OpenAI API key into the prediction pipeline at runtime. You’ll gain proficiency in designing and developing ML applications that can understand, process, and generate human-like text.

As you progress, we’ll explore the fundamental concepts of Horizontal Scaling and Vertical Scaling, providing a clear understanding of when and how to implement each strategy. You’ll then discover how to scale your ML application with ease by deploying Application Load Balancers and Auto Scaling Groups on AWS, ensuring high availability and fault tolerance.

By the end of this course, you’ll be well-versed in building ML-driven software applications, deploying them on AWS, and scaling them to meet the demands of your users. Join us on this exciting journey into the world of Machine Learning Engineering and become a skilled practitioner in this rapidly evolving field.

Course Curriculum

Chapter 1: Introduction and Software Installation

Lecture 1: Introduction

Lecture 2: Installing Development Software

Chapter 2: Building And Testing Prediction Pipeline

Lecture 1: Building A Prediction Pipeline

Lecture 2: Testing Prediction Pipeline

Chapter 3: Setting Up AWS CLI and Secrets Manager

Lecture 1: Installing AWS CLI

Lecture 2: Setting Up Secrets Manager

Chapter 4: Integrating Secrets Manager With Prediction Pipeline

Lecture 1: Injecting API Key From Secrets Manager into Application

Lecture 2: Feeding API Key Directly Into Prediction Pipeline

Chapter 5: Building and Testing Application Frontend

Lecture 1: Building Front End Of Application

Lecture 2: Testing Front End Of Application

Chapter 6: Packaging And Storing Application in Elastic Container Registry

Lecture 1: Writing Docker File

Lecture 2: Packaging Our Application and Storing on Elastic Container Registry

Lecture 3: Testing Our Packaged Application

Chapter 7: Adding Permissions For Successful Deployment

Lecture 1: Modifying Account Permissions For Deployment

Chapter 8: Deploying and Testing Application on Elastic Container Service

Lecture 1: Deploying Our Application on ECS

Lecture 2: Testing Our Application on ECS

Chapter 9: Scaling Our Service with Application Load Balancers and Auto Scaling Groups

Lecture 1: Difference between Horizontal Scaling vs Vertical Scaling

Lecture 2: Building Scalable Service – Adding Load Balancer and Auto Scaling

Lecture 3: Exposing & Testing Our Service

Instructors

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  • 2 stars: 0 votes
  • 3 stars: 3 votes
  • 4 stars: 3 votes
  • 5 stars: 11 votes

Frequently Asked Questions


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