LLMOps Masterclass 2024 Generative AI MLOps AIOps

LLMOps Masterclass 2024 – Generative AI – MLOps – AIOps, available at $54.99, has an average rating of 4.23, with 87 lectures, 8 quizzes, based on 60 reviews, and has 1343 subscribers.

You will learn about Gain a deep understanding of Generative AI, including its impact on daily life and real-world applications. Explore fundamental concepts such as AI levels, types, and the difference between generative and discriminative models. Learn about Prompt Engineering, including its architecture, components, and techniques for prompt generation. Understand the technical details of Language Model (LLM), its training process, and its enterprise applications. Develop hands-on experience by building LLM applications using ChatGPT and Hugging Face Library. Master the art of packaging and deploying AI applications using technologies such as FastAPI, Docker, and Kubernetes. Implement continuous integration and continuous deployment (CI/CD) pipelines using GitHub Actions, ensuring efficient project management. Explore monitoring techniques for LLM models in production, ensuring their reliability and performance. Acquire essential LLMOps basics, including version control systems, Git setup, and CICD demonstrations. Prepare for industry standards and best practices in AI development and operations, ensuring readiness for real-world challenges. This course is ideal for individuals who are AI Enthusiasts: Individuals passionate about artificial intelligence and eager to explore advanced topics such as Generative AI and MLOps will find this course valuable in expanding their expertise. or Data Scientists and Machine Learning Engineers: Professionals working in data science and machine learning roles who seek to deepen their understanding of AI operations, including model deployment, monitoring, and optimization, will benefit from this course. or Software Engineers: Developers interested in incorporating AI technologies into their applications and understanding the operational aspects of AI model deployment and management will find this course highly relevant. or AI Researchers: Researchers aiming to enhance their understanding of practical AI deployment and operations, particularly in the context of Generative AI, will gain valuable insights from this course. or IT Professionals and DevOps Engineers: Professionals involved in IT operations and DevOps who wish to expand their skill set to include AI Ops and cloud-native technologies like Kubernetes will find this course beneficial for career advancement. or Entrepreneurs and Innovators: Individuals seeking to leverage AI technologies to innovate and develop new products and services will gain valuable knowledge and practical skills for building and deploying AI applications. It is particularly useful for AI Enthusiasts: Individuals passionate about artificial intelligence and eager to explore advanced topics such as Generative AI and MLOps will find this course valuable in expanding their expertise. or Data Scientists and Machine Learning Engineers: Professionals working in data science and machine learning roles who seek to deepen their understanding of AI operations, including model deployment, monitoring, and optimization, will benefit from this course. or Software Engineers: Developers interested in incorporating AI technologies into their applications and understanding the operational aspects of AI model deployment and management will find this course highly relevant. or AI Researchers: Researchers aiming to enhance their understanding of practical AI deployment and operations, particularly in the context of Generative AI, will gain valuable insights from this course. or IT Professionals and DevOps Engineers: Professionals involved in IT operations and DevOps who wish to expand their skill set to include AI Ops and cloud-native technologies like Kubernetes will find this course beneficial for career advancement. or Entrepreneurs and Innovators: Individuals seeking to leverage AI technologies to innovate and develop new products and services will gain valuable knowledge and practical skills for building and deploying AI applications.

Enroll now: LLMOps Masterclass 2024 – Generative AI – MLOps – AIOps

Summary

Title: LLMOps Masterclass 2024 – Generative AI – MLOps – AIOps

Price: $54.99

Average Rating: 4.23

Number of Lectures: 87

Number of Quizzes: 8

Number of Published Lectures: 87

Number of Published Quizzes: 8

Number of Curriculum Items: 98

Number of Published Curriculum Objects: 98

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Gain a deep understanding of Generative AI, including its impact on daily life and real-world applications.

Explore fundamental concepts such as AI levels, types, and the difference between generative and discriminative models.

Learn about Prompt Engineering, including its architecture, components, and techniques for prompt generation.

Understand the technical details of Language Model (LLM), its training process, and its enterprise applications.

Develop hands-on experience by building LLM applications using ChatGPT and Hugging Face Library.

Master the art of packaging and deploying AI applications using technologies such as FastAPI, Docker, and Kubernetes.

Implement continuous integration and continuous deployment (CI/CD) pipelines using GitHub Actions, ensuring efficient project management.

Explore monitoring techniques for LLM models in production, ensuring their reliability and performance.

Acquire essential LLMOps basics, including version control systems, Git setup, and CICD demonstrations.

Prepare for industry standards and best practices in AI development and operations, ensuring readiness for real-world challenges.

Who Should Attend

AI Enthusiasts: Individuals passionate about artificial intelligence and eager to explore advanced topics such as Generative AI and MLOps will find this course valuable in expanding their expertise.

Data Scientists and Machine Learning Engineers: Professionals working in data science and machine learning roles who seek to deepen their understanding of AI operations, including model deployment, monitoring, and optimization, will benefit from this course.

Software Engineers: Developers interested in incorporating AI technologies into their applications and understanding the operational aspects of AI model deployment and management will find this course highly relevant.

AI Researchers: Researchers aiming to enhance their understanding of practical AI deployment and operations, particularly in the context of Generative AI, will gain valuable insights from this course.

IT Professionals and DevOps Engineers: Professionals involved in IT operations and DevOps who wish to expand their skill set to include AI Ops and cloud-native technologies like Kubernetes will find this course beneficial for career advancement.

Entrepreneurs and Innovators: Individuals seeking to leverage AI technologies to innovate and develop new products and services will gain valuable knowledge and practical skills for building and deploying AI applications.

Target Audiences

AI Enthusiasts: Individuals passionate about artificial intelligence and eager to explore advanced topics such as Generative AI and MLOps will find this course valuable in expanding their expertise.

Data Scientists and Machine Learning Engineers: Professionals working in data science and machine learning roles who seek to deepen their understanding of AI operations, including model deployment, monitoring, and optimization, will benefit from this course.

Software Engineers: Developers interested in incorporating AI technologies into their applications and understanding the operational aspects of AI model deployment and management will find this course highly relevant.

AI Researchers: Researchers aiming to enhance their understanding of practical AI deployment and operations, particularly in the context of Generative AI, will gain valuable insights from this course.

IT Professionals and DevOps Engineers: Professionals involved in IT operations and DevOps who wish to expand their skill set to include AI Ops and cloud-native technologies like Kubernetes will find this course beneficial for career advancement.

Entrepreneurs and Innovators: Individuals seeking to leverage AI technologies to innovate and develop new products and services will gain valuable knowledge and practical skills for building and deploying AI applications.

Unlock the potential of Generative AI with our comprehensive course, “LLMOps – Generative AI – MLOps – AIOps Masterclass 2024” From understanding the fundamentals to deploying advanced applications, this course equips you with the knowledge and skills to thrive in the era of artificial intelligence.

Here’s how your learning journey look like (Section wise) :

Introduction to Course:Dive into the world of LLM Ops with “Introduction to LLM Ops with Prompt Engineering.” Gain insights into the foundations of LLM Operations and the significance of Prompt Engineering.

Navigating the Generative AI Tsunami:Explore the profound impact of Generative AI on everyday life. From understanding AI fundamentals to exploring its diverse applications, equip yourself with essential knowledge through modules such as “Impact of Generative AI in Day to Day Life” and “Real World Applications of Generative AI.”

Getting Started with Generative AI:Delve deeper into Generative AI concepts with modules covering topics like “Generative vs Discriminative Models” and “Real World Applications of Generative AI.” Get hands-on experience and unlock the potential of this transformative technology.

Prompt Engineering:Uncover the secrets behind Prompt Engineering and understand its widespread attention in the world. Learn about the architecture, components, strategies, and techniques of Prompt Generation through comprehensive modules tailored for practical implementation.

Technical Details of LLM:Gain a profound understanding of LLM and its underlying principles. Explore topics such as LLM training, enterprise applications, and the idea behind LLM through detailed modules designed to enhance your technical expertise.

Project 1 – Building LLM Application using ChatGPT:Put your knowledge into action by embarking on a project to build an LLM application using ChatGPT. From prerequisites to deployment, this project will guide you through every step of the process, ensuring hands-on learning.

Packaging the AI/ LLM Application:Learn to package and deploy AI applications efficiently with modules covering FastAPI, Docker, and more. Master the art of containerization and streamline your deployment process with industry-standard practices.

Deploying the Container Application with Kubernetes:Discover the power of Kubernetes in deploying and orchestrating containerized applications. From installation to scaling, learn the ins and outs of Kubernetes deployment and enhance your proficiency in container management.

Github Actions:Explore the capabilities of GitHub Actions in automating workflows and enhancing collaboration. From introduction to implementation, master the art of configuring workflows tailored to your specific use cases.

Setting Up Kubernetes on Google Cloud:Unlock the potential of Google Cloud Platform for Kubernetes deployment. From setting up your account to testing deployment files, gain practical insights into running applications on GKE clusters.

Implement CI/CD with Github Actions – GKE: Optimize your development pipeline with continuous integration and continuous deployment. Learn to configure GitHub Secrets, adhere to industry standards, and streamline your deployment process for seamless project management.

Introducing Hugging Face Library: Discover the versatility of the Hugging Face Library in building AI applications. From text classification to finetuning models, explore the vast possibilities offered by this powerful toolkit.

Project 2 – Building Generative AI App using Hugging Face:Put your Hugging Face skills to the test with a project focused on building a Generative AI application. From understanding text generation pipelines to setting up CI/CD pipelines, elevate your expertise in AI development.

Monitoring of LLM Models in Production: Ensure the reliability and performance of LLM models in production with monitoring techniques. Explore platforms like WhyLabs and Langkit to gain insights into monitoring and optimizing LLM applications.

LLMOps Basics:Master the basics of LLM Ops with modules covering version control systems, Git setup, and CICD demonstrations. Strengthen your foundation in LLM Ops and prepare yourself for advanced concepts.

Embark on your journey to mastering LLM Ops and stay ahead in the ever-evolving landscape of artificial intelligence. Join us today and unlock a world of endless possibilities.

Course Curriculum

Chapter 1: Introduction to Course

Lecture 1: Introduction to LLM Ops with Prompt Engineering

Lecture 2: Source Code and Slides Access

Lecture 3: Connect with Instructor

Chapter 2: Navigating the Generative AI Tsunami

Lecture 1: Impact of Generative AI in Day to Day Life

Lecture 2: What is AI ?

Lecture 3: Levels of AI

Lecture 4: Types of AI

Chapter 3: Getting Started with Generative AI

Lecture 1: What is Generative AI

Lecture 2: Generative vs Discriminative Models

Lecture 3: Real World Applications of Generative AI

Chapter 4: Prompt Engineering

Lecture 1: Why World has the widespread attention ?

Lecture 2: Introduction to Prompt Engineering

Lecture 3: Architecture and Component of a Prompt

Lecture 4: Strategies and Techniques of Prompt Generation

Chapter 5: Technical Details of LLM

Lecture 1: What is LLM , and what is the idea of LLM ?

Lecture 2: How LLM is Trained ?

Lecture 3: How LLM is used in Enterprise

Chapter 6: Project 1 – Building LLM Application using ChatGPT

Lecture 1: Source code

Lecture 2: Pre-Requisites

Lecture 3: Open AI Platform Quick Intro

Lecture 4: Create Open AI Assistant using Assistants API – Platform

Lecture 5: Create AI Assistant with Python

Chapter 7: Packaging the AI/ LLM Application

Lecture 1: Introduction to FastAPI

Lecture 2: Packaging the AI Application

Lecture 3: Test with Postman

Lecture 4: Create Requirements.txt

Lecture 5: Introduction to Docker

Lecture 6: Docker Installation

Lecture 7: Docker Quickstart

Lecture 8: Build Docker Image for Project 1

Chapter 8: Deploying the Container Application with Kubernetes

Lecture 1: Introducing Kubernetes

Lecture 2: Architecture of Kubernetes

Lecture 3: Installing Kubernetes

Lecture 4: Running the Application on Kubernetes

Lecture 5: Create Service Definition for Kubernetes

Lecture 6: Kubernetes Deployment and Deployment Controller

Lecture 7: Scaling the Application

Lecture 8: Performing the Rolling Update

Lecture 9: Config Maps

Lecture 10: Hands On – Config Maps

Lecture 11: Kubernetes Secrets

Lecture 12: Summary of Kubernetes Learning

Lecture 13: Implementing the Kubernetes Orchestration for our containers

Chapter 9: Github Actions

Lecture 1: Introduction to GitHub Actions

Lecture 2: Quick Demo on github actions YAML file

Lecture 3: Understanding github Actions YAML file

Lecture 4: Create github Actions from Scratch

Lecture 5: Configure Workflow based on use case

Chapter 10: Setting Up Kubernetes on Google Cloud

Lecture 1: Create Google Cloud Account

Lecture 2: Setting up the Google CLI

Lecture 3: Create Kubernetes cluster with GKE

Lecture 4: Testing the Deployment file with Kubernetes Config file on GKE Cluster

Lecture 5: Quick Word on Running in MacOS

Chapter 11: Implement CI CD with Github Actions – GKE

Lecture 1: Setting Up the Github Secrets

Lecture 2: Adjust the Application by following the Industry Standards

Lecture 3: Commit and Test the Kubernetes Deployment

Lecture 4: Project Cleanup

Chapter 12: Introducing Hugging Face Library

Lecture 1: Agenda of the Section

Lecture 2: Introduction to Hugging Face Library

Lecture 3: Working with Hugging Face Library Pipeline

Lecture 4: Text Classification with HuggingFace Transformers – Data Loading

Lecture 5: Tokenization using HuggingFace

Lecture 6: Tokenization on Dataset

Lecture 7: Text Classification with Feature Extraction

Lecture 8: Finetuning on Transformers

Lecture 9: Journey Forward with Generator Models

Chapter 13: Project 2 – Building Generative AI App using Hugging Face

Lecture 1: Understanding Text Generation Pipeline

Lecture 2: Application Code for Hugging Face GPT Assistant

Lecture 3: CI CD Setup for Project 2

Lecture 4: Test of CI CD Pipeline for Project 2

Lecture 5: Update Application and Test the Flow

Chapter 14: Monitoring of LLM Models in Production

Lecture 1: Introduction to WhyLabs & LLM Monitoring

Lecture 2: LLM and Gen AI Applications

Lecture 3: Introduction to Langkit and WhyLabs Platform

Lecture 4: Hands On – Monitoring LLM in Production

Instructors

Manifold AI Learning ?
Learn the Future – Data Science, Machine Learning & AI

Rating Distribution

1 stars: 4 votes

2 stars: 1 votes

3 stars: 9 votes

4 stars: 21 votes

5 stars: 25 votes

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