DEV Community

Cover image for ๐Ÿ“š Announcing My New Book: Building an LLMOps Pipeline Using Hugging Face๐Ÿ“š
Prashant Lakhera
Prashant Lakhera

Posted on

1

๐Ÿ“š Announcing My New Book: Building an LLMOps Pipeline Using Hugging Face๐Ÿ“š

๐Ÿ“šhttps://pratimuniyal.gumroad.com/l/BuildinganLLMOpsPipelineUsingHuggingFace

Thank you, everyone, for your invaluable feedback and support for my previous two books:
๐Ÿ“š AWS for System Administrators: https://www.amazon.com/AWS-System-Administrators-automate-infrastructure/dp/1800201532
๐Ÿ“š Cracking the DevOps Interview: https://pratimuniyal.gumroad.com/l/cracking-the-devops-interview

I'm thrilled to announce the release of my new book, Building an LLMOps Pipeline Using Hugging Face. In a world where Generative AI and large language models (LLMs) are transforming our daily lives, many of us use tools like ChatGPT regularly; this book serves as a comprehensive guide to mastering LLMOps within the Hugging Face ecosystem.

Starting with the fundamentals of machine learning and deep learning, the book introduces key concepts, types of learning, and various machine learning models. It is designed to provide readers with both the knowledge and practical skills needed to leverage the power of LLMOps effectively.
You can find the book link here.
๐Ÿ“šhttps://pratimuniyal.gumroad.com/l/BuildinganLLMOpsPipelineUsingHuggingFace

Book Breakdown
1๏ธโƒฃ Chapter 1: Foundations of Machine Learning and Deep Learning
Introduction to the fundamental concepts, types, and applications of machine learning and deep learning.
2๏ธโƒฃ Chapter 2: Exploring Hugging Face: Tools, Features, and Integration for Machine Learning
Overview of Hugging Face's tools and resources for implementing and fine-tuning machine learning models.
3๏ธโƒฃ Chapter 3: Mastering Transformers and Datasets with Hugging Face
Exploration of transformer models and datasets in Hugging Face, focusing on their architecture and practical use.
4๏ธโƒฃ Chapter 4: Text Generation and Classification with Hugging Face Transformers
Guide to using Hugging Face's Transformers library for text generation, classification, and summarization tasks.
5๏ธโƒฃ Chapter 5: Creating Pipelines for Image, Audio, and Video Processing with Hugging Face
Instruction on building pipelines for image, audio, and video processing using Hugging Face tools.
6๏ธโƒฃ Chapter 6: Document and Visual Question Answering in Multi-Modal Tasks with Hugging Face
Discussion on setting up document and visual question answering pipelines for multi-modal tasks.
7๏ธโƒฃChapter 7: Fine-Tuning a Pre-Trained Model for Sentiment Analysis with Hugging Face
Step-by-step guide to fine-tuning a pre-trained model for improved sentiment analysis performance.
8๏ธโƒฃChapter 8: Project: MLOps, AIOps, LLMOps, and Deploying an Application with GitHub Actions and AWS EKS
Practical guide to setting up a CI/CD pipeline for deploying a machine learning application using GitHub Actions and AWS EKS.
9๏ธโƒฃ Chapter 9: Next Steps with Hugging Face: Contributing, Collaborating, and Continuous Learning
Encouragement and guidance for contributing to the Hugging Face community and continuing the learning journey.

I'm looking forward to your feedback ๐Ÿ™๐Ÿ™๐Ÿ™

API Trace View

Struggling with slow API calls? ๐Ÿ•’

Dan Mindru walks through how he used Sentry's new Trace View feature to shave off 22.3 seconds from an API call.

Get a practical walkthrough of how to identify bottlenecks, split tasks into multiple parallel tasks, identify slow AI model calls, and more.

Read more โ†’

Top comments (0)

A Workflow Copilot. Tailored to You.

Pieces.app image

Our desktop app, with its intelligent copilot, streamlines coding by generating snippets, extracting code from screenshots, and accelerating problem-solving.

Read the docs

๐Ÿ‘‹ Kindness is contagious

Please leave a โค๏ธ or a friendly comment on this post if you found it helpful!

Okay