What you'll 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.

Requirements
Basic Understanding of Artificial Intelligence: Familiarity with fundamental concepts of artificial intelligence, including machine learning and deep learning, will provide a strong foundation for the course.
Programming Proficiency: Proficiency in at least one programming language such as Python is highly recommended, as many practical exercises and projects will involve coding.
Knowledge of Command Line Interface (CLI): Basic understanding of working with the command line interface will be beneficial for executing commands and managing applications throughout the course.
Familiarity with Git: Understanding the basics of version control systems and Git operations will be helpful for managing project repositories and collaborating with peers.
Cloud Computing Basics: A basic understanding of cloud computing concepts, particularly with platforms like Google Cloud Platform (GCP), will be beneficial for deploying applications and working with Kubernetes.
While not mandatory, having these prerequisites will enhance the learning experience and ensure students can fully engage with the course materials and practical exercises. Additionally, a strong willingness to learn and explore new technologies is essential for success in the LLMOps Masterclass 2024.

Description
Unlock the potential of Generative AI with our comprehensive course, "LLMOps - Generative AI - MLOps - AIOps Masterclass 2025" 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.
Who this course is 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.
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.

Homepage:

What you'll 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.

Requirements
Basic Understanding of Artificial Intelligence: Familiarity with fundamental concepts of artificial intelligence, including machine learning and deep learning, will provide a strong foundation for the course.
Programming Proficiency: Proficiency in at least one programming language such as Python is highly recommended, as many practical exercises and projects will involve coding.
Knowledge of Command Line Interface (CLI): Basic understanding of working with the command line interface will be beneficial for executing commands and managing applications throughout the course.
Familiarity with Git: Understanding the basics of version control systems and Git operations will be helpful for managing project repositories and collaborating with peers.
Cloud Computing Basics: A basic understanding of cloud computing concepts, particularly with platforms like Google Cloud Platform (GCP), will be beneficial for deploying applications and working with Kubernetes.
While not mandatory, having these prerequisites will enhance the learning experience and ensure students can fully engage with the course materials and practical exercises. Additionally, a strong willingness to learn and explore new technologies is essential for success in the LLMOps Masterclass 2024.

Description
Unlock the potential of Generative AI with our comprehensive course, "LLMOps - Generative AI - MLOps - AIOps Masterclass 2025" 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.
Who this course is 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.
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.

Homepage: