Learning plan cover

AI infra/RL infra/LLM infra

LEARNING PLAN

AI infra/RL infra/LLM infra

AI infrastructure is the backbone that enables machine learning models to operate at scale in production environments. This learning plan is ideal for ML engineers, DevOps professionals, and software engineers looking to bridge the gap between theoretical AI concepts and practical, deployable systems that can handle real-world demands.

ByCommunity User
2 h 28 m
12 coursesUpdated 73 days ago
Hands-on Machine Learning With Scikit-learn And TensorflowAll About LLMs: The AI Revolution ExplainedKubernetes Patterns12 sources

How This Learning Plan Was Made

This plan was crafted by BeFreed’s proprietary AI to help you learn AI infra/RL infra/LLM infra with ease. It is curated from in-depth research on the topic and structured around the most effective learning journeys proven by BeFreed users.

Each episode delivers bite-sized, high-impact lessons distilled from world-class sources — bestselling books, research papers, and expert insights. Together, they form a sophisticated yet accessible path to mastering AI infra/RL infra/LLM infra.

What You'll Learn

  • Build and scale robust machine learning infrastructure for production environments
  • Deploy and optimize large language models in real-world applications
  • Design and implement reinforcement learning systems for self-improving AI agents
  • Master infrastructure scaling techniques for handling high-demand AI workloads
  • Develop secure, maintainable AI systems that meet enterprise requirements

Master the fundamental concepts, tools, and architectures that underpin modern AI systems, establishing a solid knowledge base for building and scaling machine learning operations.

Hands-On Machine Learning with Scikit-Learn and TensorFlow cover
Hands-On Machine Learning with Scikit-Learn and TensorFlow

Step-by-step guide to machine learning with Python

8 m
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Guide to Large Language Models (LLMs): Capabilities and ApplicationsA Survey of Large Language Models: Evolution, Architectures, Adaptation, Benchmarking, Applications, Challenges, and Societal ImplicationsLarge language models: an overview of foundational architectures, recent trends, and a new taxonomysource 4
6 sources
All About LLMs: The AI Revolution Explained

Lena and Eli break down everything about Large Language Models - from how ChatGPT predicts words to transformer architecture, real-world applications, and why understanding LLMs is essential for the future.

10 m
Kubernetes Patterns cover
Kubernetes Patterns

Proven design patterns for cloud-native Kubernetes applications

9 m

Dive deep into the inner workings of large language models, their architecture, and practical implementation strategies for deploying them in production environments.

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source 1source 2source 3source 4
6 sources
LLM Fundamentals: Attention Is All You Need

Deep dive into how ChatGPT and large language models actually work, from the revolutionary attention mechanism to probabilistic text generation. Perfect for understanding the core concepts behind modern AI.

9 m
What Is ChatGPT Doing ... and Why Does It Work? cover
What Is ChatGPT Doing ... and Why Does It Work?

In-depth analysis of ChatGPT's AI mechanisms and effectiveness

9 m
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Understanding LLMs: A Comprehensive Overview from Training to InferenceMathematical Foundations of Large Language Models - MediumTransformer Architecture Explained With Self-Attention MechanismLLM Transformer Model Visually Explained
6 sources
The Mathematical Magic Behind Large Language Models

Explore how LLMs transform raw text into intelligent responses through tokenization, transformer architecture, and neural networks. From training on massive datasets to generating coherent output.

10 m

Explore reinforcement learning infrastructure, implementation strategies, and practical applications for building self-improving AI agents and systems.

Keras Reinforcement Learning Projects cover
Keras Reinforcement Learning Projects

Build self-learning AI agents with 9 reinforcement learning projects using Keras.

9 m
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Keras Reinforcement Learning ProjectsPython CookbookHands-On Machine Learning with Scikit-Learn and TensorFlowChatGPT For Dummies
8 sources
RLHF in 24 Hours: The Express Guide

Discover how to grasp the core concepts of Reinforcement Learning from Human Feedback in just one day, from understanding its three-layer foundation to building a practical toolkit for this revolutionary AI approach.

11 m
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What Is ChatGPT Doing ... and Why Does It Work?Make Your Own Neural NetworkChatGPT For DummiesArtificial Intelligence and Generative AI for Beginners
13 sources
Building AI Agents: Beyond Chatbots

Discover how LLMs have evolved from text generators to action-taking AI agents. Learn the neural architecture behind these systems and how to build your own agents that can understand goals and execute complex tasks autonomously.

32 m

Master the techniques and best practices for building robust, scalable, and secure AI infrastructure that can handle real-world demands in production environments.

Designing Data-Intensive Applications cover
Designing Data-Intensive Applications

Comprehensive guide to building scalable data systems

9 m
Building Secure and Reliable Systems cover
Building Secure and Reliable Systems

Google experts share best practices for designing, implementing, and maintaining secure and reliable systems at scale.

9 m
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source 1RAG Architecture: Complete Guide to Retrieval-Augmented ...Reference Architecture : Retrieval Augmented Generation (RAG)Optimizing Chunking, Embedding, and Vectorization for Retrieval ...
6 sources
RAG & Vector Databases: Cloud Architecture Deep Dive

Master RAG implementation from chunking strategies to Kubernetes deployment patterns. Explore vector database selection, optimization techniques, and production-ready cloud architectures for enterprise AI systems.

23 m

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