Large Language Models (LLMs) and Reinforcement Learning (RL) Training Course
Large Language Models (LLMs) are advanced types of neural networks designed to understand and generate human-like text based on the input they receive. Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize cumulative rewards.
This instructor-led, live training (online or onsite) is aimed at intermediate-level data scientists who wish to gain a comprehensive understanding and practical skills in both Large Language Models (LLMs) and Reinforcement Learning (RL).
By the end of this training, participants will be able to:
- Understand the components and functionality of transformer models.
- Optimize and fine-tune LLMs for specific tasks and applications.
- Understand the core principles and methodologies of reinforcement learning.
- Learn how reinforcement learning techniques can enhance the performance of LLMs.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Course Outline
Introduction to Large Language Models (LLMs)
- Overview of LLMs
- Definition and significance
- Applications in AI today
Transformer Architecture
- What is a transformer and how does it work?
- Main components and features
- Embedding and positional encoding
- Multi-head attention
- Feed-forward neural network
- Normalization and residual connections
Transformer Models
- Self-attention mechanism
- Encoder-decoder architecture
- Positional embeddings
- BERT (Bidirectional Encoder Representations from Transformers)
- GPT (Generative Pretrained Transformer)
Performance Optimization and Pitfalls
- Context length
- Mamba and state-space models
- Flash attention
- Sparse transformers
- Vision transformers
- Importance of quantization
Improving Transformers
- Retrieval augmented text generation
- Mixture of models
- Tree of thoughts
Fine-Tuning
- Theory of low-rank adaptation
- Fine-Tuning with QLora
Scaling Laws and Optimization in LLMs
- Importance of scaling laws for LLMs
- Data and model size scaling
- Computational scaling
- Parameter efficiency scaling
Optimization
- Relationship between model size, data size, compute budget, and inference requirements
- Optimizing performance and efficiency of LLMs
- Best practices and tools for training and fine-tuning LLMs
Training and Fine-Tuning LLMs
- Steps and challenges of training LLMs from scratch
- Data acquisition and maintenance
- Large-scale data, CPU, and memory requirements
- Optimization challenges
- Landscape of open-source LLMs
Fundamentals of Reinforcement Learning (RL)
- Introduction to Reinforcement Learning
- Learning through positive reinforcement
- Definition and core concepts
- Markov Decision Process (MDP)
- Dynamic programming
- Monte Carlo methods
- Temporal Difference Learning
Deep Reinforcement Learning
- Deep Q-Networks (DQN)
- Proximal Policy Optimization (PPO)
- Elements of Reinforcement Learning
Integration of LLMs and Reinforcement Learning
- Combining LLMs with Reinforcement Learning
- How RL is used in LLMs
- Reinforcement Learning with Human Feedback (RLHF)
- Alternatives to RLHF
Case Studies and Applications
- Real-world applications
- Success stories and challenges
Advanced Topics
- Advanced techniques
- Advanced optimization methods
- Cutting-edge research and developments
Summary and Next Steps
Requirements
- Basic understanding of Machine Learning
Audience
- Data scientists
- Software engineers
Open Training Courses require 5+ participants.
Large Language Models (LLMs) and Reinforcement Learning (RL) Training Course - Booking
Large Language Models (LLMs) and Reinforcement Learning (RL) Training Course - Enquiry
Large Language Models (LLMs) and Reinforcement Learning (RL) - Consultancy Enquiry
Provisional Upcoming Courses (Require 5+ participants)
Related Courses
Advanced LangGraph: Optimization, Debugging, and Monitoring Complex Graphs
35 HoursLangGraph is a framework for building stateful, multi-actor LLM applications as composable graphs with persistent state and control over execution.
This instructor-led, live training (online or onsite) is aimed at advanced-level AI platform engineers, DevOps for AI, and ML architects who wish to optimize, debug, monitor, and operate production-grade LangGraph systems.
By the end of this training, participants will be able to:
- Design and optimize complex LangGraph topologies for speed, cost, and scalability.
- Engineer reliability with retries, timeouts, idempotency, and checkpoint-based recovery.
- Debug and trace graph executions, inspect state, and systematically reproduce production issues.
- Instrument graphs with logs, metrics, and traces, deploy to production, and monitor SLAs and costs.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Building Coding Agents with Devstral: From Agent Design to Tooling
14 HoursDevstral is an open-source framework designed for building and running coding agents that can interact with codebases, developer tools, and APIs to enhance engineering productivity.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level ML engineers, developer-tooling teams, and SREs who wish to design, implement, and optimize coding agents using Devstral.
By the end of this training, participants will be able to:
- Set up and configure Devstral for coding agent development.
- Design agentic workflows for codebase exploration and modification.
- Integrate coding agents with developer tools and APIs.
- Implement best practices for secure and efficient agent deployment.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Open-Source Model Ops: Self-Hosting, Fine-Tuning and Governance with Devstral & Mistral Models
14 HoursDevstral and Mistral models are open-source AI technologies designed for flexible deployment, fine-tuning, and scalable integration.
This instructor-led, live training (online or onsite) is aimed at intermediate–level to advanced–level ML engineers, platform teams, and research engineers who wish to self-host, fine-tune, and govern Mistral and Devstral models in production environments.
By the end of this training, participants will be able to:
- Set up and configure self-hosted environments for Mistral and Devstral models.
- Apply fine-tuning techniques for domain-specific performance.
- Implement versioning, monitoring, and lifecycle governance.
- Ensure security, compliance, and responsible usage of open-source models.
Format of the Course
- Interactive lecture and discussion.
- Hands-on exercises in self-hosting and fine-tuning.
- Live-lab implementation of governance and monitoring pipelines.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
LangGraph Applications in Finance
35 HoursLangGraph Foundations: Graph-Based LLM Prompting and Chaining
14 HoursLangGraph is a framework for building graph-structured LLM applications that support planning, branching, tool use, memory, and controllable execution.
This instructor-led, live training (online or onsite) is aimed at beginner-level developers, prompt engineers, and data practitioners who wish to design and build reliable, multi-step LLM workflows using LangGraph.
By the end of this training, participants will be able to:
- Explain core LangGraph concepts (nodes, edges, state) and when to use them.
- Build prompt chains that branch, call tools, and maintain memory.
- Integrate retrieval and external APIs into graph workflows.
- Test, debug, and evaluate LangGraph apps for reliability and safety.
Format of the Course
- Interactive lecture and facilitated discussion.
- Guided labs and code walkthroughs in a sandbox environment.
- Scenario-based exercises on design, testing, and evaluation.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
LangGraph in Healthcare: Workflow Orchestration for Regulated Environments
35 HoursLangGraph for Legal Applications
35 HoursBuilding Dynamic Workflows with LangGraph and LLM Agents
14 HoursLangGraph for Marketing Automation
14 HoursLangGraph is a graph-based orchestration framework that enables conditional, multi-step LLM and tool workflows, ideal for automating and personalizing content pipelines.
This instructor-led, live training (online or onsite) is aimed at intermediate-level marketers, content strategists, and automation developers who wish to implement dynamic, branching email campaigns and content generation pipelines using LangGraph.
By the end of this training, participants will be able to:
- Design graph-structured content and email workflows with conditional logic.
- Integrate LLMs, APIs, and data sources for automated personalization.
- Manage state, memory, and context across multi-step campaigns.
- Evaluate, monitor, and optimize workflow performance and delivery outcomes.
Format of the Course
- Interactive lectures and group discussions.
- Hands-on labs implementing email workflows and content pipelines.
- Scenario-based exercises on personalization, segmentation, and branching logic.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Le Chat Enterprise: Private ChatOps, Integrations & Admin Controls
14 HoursCost-Effective LLM Architectures: Mistral at Scale (Performance / Cost Engineering)
14 HoursProductizing Conversational Assistants with Mistral Connectors & Integrations
14 HoursMistral AI is an open AI platform that enables teams to build and integrate conversational assistants into enterprise and customer-facing workflows.
This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level product managers, full-stack developers, and integration engineers who wish to design, integrate, and productize conversational assistants using Mistral connectors and integrations.
By the end of this training, participants will be able to:
- Integrate Mistral conversational models with enterprise and SaaS connectors.
- Implement retrieval-augmented generation (RAG) for grounded responses.
- Design UX patterns for internal and external chat assistants.
- Deploy assistants into product workflows for real-world use cases.
Format of the Course
- Interactive lecture and discussion.
- Hands-on integration exercises.
- Live-lab development of conversational assistants.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Enterprise-Grade Deployments with Mistral Medium 3
14 HoursMistral Medium 3 is a high-performance, multimodal large language model designed for production-grade deployment across enterprise environments.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level AI/ML engineers, platform architects, and MLOps teams who wish to deploy, optimize, and secure Mistral Medium 3 for enterprise use cases.
By the end of this training, participants will be able to:
- Deploy Mistral Medium 3 using API and self-hosted options.
- Optimize inference performance and costs.
- Implement multimodal use cases with Mistral Medium 3.
- Apply security and compliance best practices for enterprise environments.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Mistral for Responsible AI: Privacy, Data Residency & Enterprise Controls
14 HoursMistral AI is an open and enterprise-ready AI platform that provides features for secure, compliant, and responsible AI deployment.
This instructor-led, live training (online or onsite) is aimed at intermediate-level compliance leads, security architects, and legal/ops stakeholders who wish to implement responsible AI practices with Mistral by leveraging privacy, data residency, and enterprise control mechanisms.
By the end of this training, participants will be able to:
- Implement privacy-preserving techniques in Mistral deployments.
- Apply data residency strategies to meet regulatory requirements.
- Set up enterprise-grade controls such as RBAC, SSO, and audit logs.
- Evaluate vendor and deployment options for compliance alignment.
Format of the Course
- Interactive lecture and discussion.
- Compliance-focused case studies and exercises.
- Hands-on implementation of enterprise AI controls.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
