Introduction to Large Language Models (LLMs) Training Course
Large Language Models (LLMs) are deep neural network architectures capable of generating natural language text based on specific inputs or contexts. Trained on vast corpora of textual data from diverse domains and sources, these models effectively capture the syntactic and semantic patterns inherent in natural language. Consequently, LLMs have delivered remarkable performance across a wide array of natural language processing tasks, including but not limited to text summarization, question answering, and text generation.
This instructor-led, live training session—available either online or onsite—is designed for developers at beginner to intermediate skill levels who aim to leverage Large Language Models for various natural language applications.
Upon completion of this training, participants will be equipped to:
- Establish a development environment integrated with a popular LLM.
- Construct a foundational LLM and fine-tune it using custom datasets.
- Apply LLMs to diverse natural language tasks, such as summarizing text, answering questions, and generating content.
- Debug and evaluate LLM performance utilizing tools like TensorBoard, PyTorch Lightning, and Hugging Face Datasets.
Format of the Course
- Interactive lectures accompanied by discussions.
- Extensive exercises and practical practice sessions.
- Hands-on implementation within a live laboratory environment.
Course Customization Options
- For those interested in a customized training program, please contact us to arrange specific requirements.
Course Outline
Introduction
- Defining Large Language Models (LLMs).
- Comparing LLMs with traditional NLP models.
- Overview of LLM features and architectural design.
- Examining the challenges and limitations associated with LLMs.
Understanding LLMs
- The lifecycle of an LLM.
- Mechanisms behind how LLMs operate.
- Key components of an LLM: encoders, decoders, attention mechanisms, embeddings, and others.
Getting Started
- Setting up the Development Environment.
- Installing an LLM as a development tool, for instance, via Google Colab or Hugging Face.
Working with LLMs
- Exploring available LLM options.
- Creating and deploying an LLM.
- Fine-tuning an LLM on a custom dataset.
Text Summarization
- Grasping the concept of text summarization and its practical applications.
- Employing LLMs for both extractive and abstractive text summarization.
- Evaluating the quality of generated summaries using metrics such as ROUGE, BLEU, etc.
Question Answering
- Understanding question answering tasks and their applications.
- Utilizing LLMs for open-domain and closed-domain question answering.
- Assessing the accuracy of generated answers using metrics such as F1, EM, etc.
Text Generation
- Understanding text generation tasks and their applications.
- Implementing LLMs for both conditional and unconditional text generation.
- Controlling the style, tone, and content of generated texts through parameters such as temperature, top-k, and top-p.
Integrating LLMs with Other Frameworks and Platforms
- Integrating LLMs with PyTorch or TensorFlow.
- Leveraging LLMs with Flask or Streamlit.
- Deploying LLMs using Google Cloud or AWS.
Troubleshooting
- Identifying common errors and bugs in LLM implementations.
- Monitoring and visualizing the training process using TensorBoard.
- Simplifying training code and enhancing performance with PyTorch Lightning.
- Loading and preprocessing data efficiently using Hugging Face Datasets.
Summary and Next Steps
Requirements
- Foundational knowledge of natural language processing (NLP) and deep learning principles.
- Practical experience with Python and either PyTorch or TensorFlow.
- Basic programming proficiency.
Audience
- Software Developers
- NLP enthusiasts
- Data Scientists
Open Training Courses require 5+ participants.
Introduction to Large Language Models (LLMs) Training Course - Booking
Introduction to Large Language Models (LLMs) Training Course - Enquiry
Introduction to Large Language Models (LLMs) - Consultancy Enquiry
Upcoming Courses
Related Courses
Advanced LangGraph: Optimization, Debugging, and Monitoring Complex Graphs
35 HoursLangGraph serves as a framework designed for constructing stateful, multi-agent LLM applications through composable graphs, featuring persistent state and precise control over execution flows.
This instructor-led live training, available online or onsite, targets advanced AI platform engineers, AI-focused DevOps professionals, and ML architects who aim to optimize, debug, monitor, and manage production-grade LangGraph systems.
Upon completing this training, participants will be capable of:
- Designing and optimizing complex LangGraph topologies to enhance speed, reduce costs, and improve scalability.
- Ensuring reliability through retries, timeouts, idempotency, and checkpoint-based recovery mechanisms.
- Debugging and tracing graph executions, inspecting states, and systematically reproducing production issues.
- Instrumenting graphs with logs, metrics, and traces, deploying them to production, and monitoring SLAs and costs.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical practice.
- Hands-on implementation in a live-lab environment.
Customization Options
- To request 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 are open-source AI technologies designed for flexible deployment, fine-tuning, and scalable integration.
This instructor-led, live training (available online or onsite) targets intermediate to advanced 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 to achieve domain-specific performance.
- Implement versioning, monitoring, and lifecycle governance.
- Ensure security, compliance, and responsible usage of open-source models.
Course Format
- Interactive lectures and discussions.
- Hands-on exercises focused on self-hosting and fine-tuning.
- Live-lab implementation of governance and monitoring pipelines.
Course Customization Options
- To request customized training for this course, please contact us to arrange it.
LangGraph Applications in Finance
35 HoursLangGraph serves as a framework for constructing stateful, multi-actor LLM applications through composable graphs, enabling persistent state management and precise execution control.
This instructor-led training, available either online or onsite, targets intermediate to advanced professionals seeking to design, implement, and manage LangGraph-based financial solutions with appropriate governance, observability, and compliance standards.
Upon completion of this training, participants will be capable of:
- Designing finance-specific LangGraph workflows that align with regulatory and audit requirements.
- Integrating financial data standards and ontologies into graph states and tooling.
- Implementing reliability, safety measures, and human-in-the-loop controls for critical processes.
- Deploying, monitoring, and optimizing LangGraph systems for performance, cost efficiency, and service level agreements (SLAs).
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical application.
- Hands-on implementation within a live laboratory environment.
Customization Options
- To request a tailored version of this course, please contact us to make arrangements.
LangGraph Foundations: Graph-Based LLM Prompting and Chaining
14 HoursLangGraph is a framework designed for constructing LLM applications with a graph structure, supporting planning, branching, tool usage, memory, and controllable execution.
This instructor-led, live training (available online or on-site) is targeted at beginner-level developers, prompt engineers, and data practitioners aiming 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 facilitates stateful, multi-actor workflows driven by LLMs, offering precise control over execution paths and state persistence. In the healthcare sector, these capabilities are essential for ensuring compliance, enhancing interoperability, and developing decision-support systems that integrate seamlessly with medical workflows.
This instructor-led live training, available both online and onsite, targets intermediate to advanced-level professionals aiming to design, implement, and manage LangGraph-based healthcare solutions while navigating regulatory, ethical, and operational challenges.
Upon completion of this training, participants will be able to:
- Design healthcare-specific LangGraph workflows with a focus on compliance and auditability.
- Integrate LangGraph applications with medical ontologies and standards such as FHIR, SNOMED CT, and ICD.
- Apply best practices for reliability, traceability, and explainability in sensitive environments.
- Deploy, monitor, and validate LangGraph applications in healthcare production settings.
Format of the Course
- Interactive lecture and discussion.
- Hands-on exercises with real-world case studies.
- Implementation practice in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
LangGraph for Legal Applications
35 HoursLangGraph serves as a framework designed to construct stateful, multi-agent LLM applications through composable graphs that maintain persistent state and offer precise control over execution processes.
This instructor-led live training, available either online or on-site, targets intermediate to advanced professionals aiming to design, implement, and manage LangGraph-based legal solutions equipped with robust compliance, traceability, and governance controls.
Upon completion of this training, participants will be capable of:
- Designing legal-specific LangGraph workflows that ensure auditability and compliance.
- Integrating legal ontologies and document standards into graph state and processing mechanisms.
- Implementing guardrails, human-in-the-loop approvals, and traceable decision pathways.
- Deploying, monitoring, and maintaining LangGraph services in production environments with effective observability and cost management.
Format of the Course
- Interactive lectures and discussions.
- Extensive exercises and practical application.
- Hands-on implementation within a live laboratory environment.
Course Customization Options
- To request a customized training session for this course, please contact us to make arrangements.
Building Dynamic Workflows with LangGraph and LLM Agents
14 HoursLangGraph is a framework designed for composing graph-structured workflows involving Large Language Models (LLMs), supporting features such as branching, tool integration, memory management, and controllable execution.
This instructor-led, live training (available online or onsite) is tailored for intermediate-level engineers and product teams aiming to merge LangGraph’s graph logic with LLM agent loops to create dynamic, context-aware applications like customer support assistants, decision trees, and information retrieval systems.
Upon completing this training, participants will be capable of:
- Designing graph-based workflows that coordinate LLM agents, tools, and memory.
- Implementing conditional routing, retries, and fallback mechanisms to ensure robust execution.
- Integrating retrieval systems, APIs, and structured outputs into agent loops.
- Evaluating, monitoring, and securing agent behavior to enhance reliability and safety.
Course Format
- Interactive lectures and facilitated discussions.
- Guided labs and code walkthroughs within a sandbox environment.
- Scenario-based design exercises and peer reviews.
Customization Options
- To request customized training for this course, please contact us to arrange.
LangGraph for Marketing Automation
14 HoursLangGraph is a graph-based orchestration framework designed to facilitate conditional, multi-step workflows involving LLMs and tools, making it ideal for automating and personalizing content pipelines.
This instructor-led live training (available online or onsite) is tailored for intermediate-level marketers, content strategists, and automation developers aiming to implement dynamic, branching email campaigns and content generation pipelines using LangGraph.
Upon completion of this training, participants will be capable of:
- Designing graph-structured content and email workflows that incorporate conditional logic.
- Integrating LLMs, APIs, and data sources to enable automated personalization.
- Managing state, memory, and context across multi-step campaigns.
- Evaluating, monitoring, and optimizing workflow performance and delivery outcomes.
Course Format
- Interactive lectures and group discussions.
- Hands-on labs focused on implementing email workflows and content pipelines.
- Scenario-based exercises covering personalization, segmentation, and branching logic.
Course Customization Options
- For requests regarding customized training for this course, please contact us to arrange details.
Le Chat Enterprise: Private ChatOps, Integrations & Admin Controls
14 HoursLe Chat Enterprise offers a private ChatOps solution that delivers secure, customizable, and governed conversational AI capabilities for organizations, including support for RBAC, SSO, connectors, and enterprise app integrations.
This instructor-led, live training (online or onsite) targets intermediate-level product managers, IT leads, solution engineers, and security/compliance teams who want to deploy, configure, and govern Le Chat Enterprise in enterprise environments.
By the end of this training, participants will be able to:
- Set up and configure Le Chat Enterprise for secure deployments.
- Enable RBAC, SSO, and compliance-driven controls.
- Integrate Le Chat with enterprise applications and data stores.
- Design and implement governance and admin playbooks for ChatOps.
Format of the Course
- Interactive lecture and discussion.
- Many 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.
Cost-Effective LLM Architectures: Mistral at Scale (Performance / Cost Engineering)
14 HoursMistral is a high-performance family of large language models optimized for cost-effective production deployment at scale.
This instructor-led, live training (online or onsite) is aimed at advanced-level infrastructure engineers, cloud architects, and MLOps leads who wish to design, deploy, and optimize Mistral-based architectures for maximum throughput and minimum cost.
By the end of this training, participants will be able to:
- Implement scalable deployment patterns for Mistral Medium 3.
- Apply batching, quantization, and efficient serving strategies.
- Optimize inference costs while maintaining performance.
- Design production-ready serving topologies for enterprise workloads.
Course Structure
- Interactive lecture and discussion.
- Extensive 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.
Productizing 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 engineered for robust, production-ready deployment within enterprise settings.
This instructor-led training session, available either online or onsite, targets intermediate to advanced AI/ML engineers, platform architects, and MLOps specialists looking to deploy, optimize, and secure Mistral Medium 3 for business applications.
Upon completion of this training, participants will be equipped to:
- Deploy Mistral Medium 3 via API or by self-hosting.
- Enhance inference performance while managing costs.
- Develop multimodal applications using Mistral Medium 3.
- Apply security and compliance standards suitable for enterprise environments.
Course Format
- Interactive lectures and discussions.
- Extensive practical exercises.
- Hands-on implementation within a live lab environment.
Customization Options
- For customized training solutions, please reach out to us to arrange details.
Mistral for Responsible AI: Privacy, Data Residency & Enterprise Controls
14 HoursMistral AI offers an open and enterprise-ready AI platform equipped with features designed to facilitate secure, compliant, and responsible AI deployment.
This instructor-led training, available both online and onsite, is tailored for intermediate-level compliance leads, security architects, and legal/operations stakeholders who aim to implement responsible AI practices using Mistral by leveraging privacy, data residency, and enterprise control mechanisms.
Upon completion of this training, participants will be able to:
- Implement privacy-preserving techniques within Mistral deployments.
- Apply data residency strategies to satisfy regulatory requirements.
- Establish enterprise-grade controls, including RBAC, SSO, and audit logs.
- Evaluate vendor and deployment options to ensure 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.
Multimodal Applications with Mistral Models (Vision, OCR, & Document Understanding)
14 HoursMistral models are open-source AI technologies that now extend into multimodal workflows, supporting both language and vision tasks for enterprise and research applications.
This instructor-led, live training (online or onsite) is aimed at intermediate-level ML researchers, applied engineers, and product teams who wish to build multimodal applications with Mistral models, including OCR and document understanding pipelines.
By the end of this training, participants will be able to:
- Set up and configure Mistral models for multimodal tasks.
- Implement OCR workflows and integrate them with NLP pipelines.
- Design document understanding applications for enterprise use cases.
- Develop vision-text search and assistive UI functionalities.
Format of the Course
- Interactive lecture and discussion.
- Hands-on coding exercises.
- Live-lab implementation of multimodal pipelines.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
