Description
Building Deploying and Scaling LLM Powered Applications course. This course teaches the essential skills to develop, deploy, and manage powerful applications powered by large language models (LLMs) using Langchain, the OpenAI API, and AWS services. In this course, you will learn how to build AI applications from the ground up, use Langchain for natural language processing, and integrate the OpenAI API into your prediction pipeline. It also covers important topics such as horizontal and vertical scaling, deploying applications on AWS Elastic Container Service, and managing traffic with Load Balancers and Auto Scaling Groups. By the end of the course, you will be able to build ML-powered applications, deploy them in the cloud, and optimize performance for end users. This hands-on training prepares you to tackle the challenges of machine learning engineering and developing scalable solutions.
What you will learn:
- Build a complete, scalable software application supported by a Large Language Model and deploy it at scale on Amazon Web Services.
- Integrate your application’s LLM-based backend with the Streamlit UI frontend.
- First test your application locally, then package it using Docker, and finally learn the best practices for using Streamlit inside Docker.
- A template and best practices for injecting your OpenAI API keys into your containerized application at runtime.
- Fix vulnerabilities in your containerized application and best practices for resolving them.
- Design your system architecture based on the components and design choices in your application.
- Differences between Horizontal Scaling and Vertical Scaling.
- Deep dive into Serverless deployment and use Load Balancers and Auto Scaling for your application.
- Ability to use your learnings to build, deploy, and scale other LLM-based Langchain applications.
Who is this course suitable for?
- The goal of this course is to introduce you to Machine Learning Engineering. This course will enable learners to build, deploy, and scale an end-to-end software application that is supported by a Large Language Model on the backend to produce results based on user input. The main goal of this course is to teach a framework that users can replicate and apply to build other software applications that use OpenAI LLMs, inject API keys at runtime to prevent key leaks, and deploy the corresponding applications at scale on Amazon Web Services.
- This is an intermediate level course and is intended for developers who are interested in developing, deploying, and scaling LLM-based applications. The target audience for this course is: software engineers, data scientists, ML engineers, and AI engineers. However, these are certainly not strict requirements and anyone who wants to learn how to build, test, and deploy large-scale software applications is equally welcome.
Building Deploying and Scaling LLM Powered Applications Course Details
- Publisher: Udemy
- Instructor: LLM Developer
- Training level: Beginner to advanced
- Training duration: 2 hours and 27 minutes
- Number of lessons: 19
Course headings
Prerequisites for the Building Deploying and Scaling LLM Powered Applications course
- Users of this course must know how to write code in Python, Basic Knowledge of Langchain ( though, it will be discussed in the course videos ), Basic Knowledge of AWS. Additionally basic knowledge of Docker is preferred but not required as the required information to package applications for deployment will be taught in the course
Course images
Sample course video
Installation Guide
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Subtitles: None
Quality: 720p
Download link
File(s) password: www.downloadly.ir
File size
1.4 GB