Sovereign AI for Regulated Organizations: Controlling Data, Models and Inference Environments Training Course
Sovereign AI for Regulated Organizations: Managing Data, Models, and Inference Environments is a hands-on course designed to help regulated enterprises maintain strict control over their AI data, models, and runtime environments.
This instructor-led live training (available online or onsite) targets intermediate-level IT leaders, compliance officers, security teams, and enterprise architects who aim to apply sovereign AI principles and governance frameworks to design AI environments that safeguard sensitive information, meet localization mandates, and minimize vendor lock-in.
Upon completing this training, participants will be able to:
- Articulate the foundational principles of sovereign AI within a regulated enterprise context.
- Evaluate risks associated with data, models, and inference environments, including those related to hosting, logging, and third-party AI services.
- Establish governance controls for prompts, logs, access permissions, audit trails, and localization compliance.
- Develop a practical roadmap to reduce dependence on AI vendors while ensuring ongoing regulatory compliance.
Course Format
- Interactive lectures and discussions.
- Guided exercises and collaborative group analysis.
- Scenario-based planning sessions focused on policy formulation and architectural decisions.
Course Customization Options
- To request tailored training for this course, please contact us to make arrangements.
Course Outline
Foundations of Sovereign AI
- Understanding the meaning of sovereign AI in regulated organizations.
- Business, legal, and operational drivers.
- Key control areas: data, models, infrastructure, and operations.
Regulatory Requirements and Risk Mapping
- Data residency, privacy, and sector-specific obligations.
- Mapping sensitive data to specific AI use cases.
- Identifying risks related to cross-border data transfer, logging, and third-party exposure.
Governing Data, Prompts, and Logs
- Prompt governance and defining acceptable use boundaries.
- Logging policies for prompts, responses, and metadata.
- Practices for retention, redaction, masking, and access control.
- Exercise: reviewing an AI data flow to identify governance gaps.
Model Hosting and Inference Environment Options
- Deployment choices: public API, private cloud, on-premise, and hybrid environments.
- Key factors influencing where models should run.
- Balancing trade-offs among control, security, cost, and operational ownership.
Vendor Dependence and Portability
- Common patterns of vendor lock-in in models, tools, and platforms.
- Achieving portability through modular architecture, open interfaces, and clear contractual terms.
- Exercise: evaluating a vendor against sovereignty criteria.
Governance Model and Action Planning
- Defining roles and responsibilities across IT, security, legal, and compliance teams.
- Approval workflows for use cases, models, and operational changes.
- Expectations for auditability, monitoring, and incident response.
- Creating a practical sovereign AI roadmap and identifying next steps.
Requirements
- A foundational understanding of AI concepts, data governance, and compliance requirements.
- Familiarity with enterprise technology infrastructure, cloud services, security protocols, or risk management decision-making.
- No programming experience is required.
Target Audience
- IT leaders, enterprise architects, and platform managers.
- Risk, compliance, legal, and data governance professionals.
- Security teams and business leaders responsible for implementing AI in regulated environments.
Open Training Courses require 5+ participants.
Sovereign AI for Regulated Organizations: Controlling Data, Models and Inference Environments Training Course - Booking
Sovereign AI for Regulated Organizations: Controlling Data, Models and Inference Environments Training Course - Enquiry
Sovereign AI for Regulated Organizations: Controlling Data, Models and Inference Environments - Consultancy Enquiry
Provisional Upcoming Courses (Require 5+ participants)
Related Courses
Advanced LangGraph: Optimization, Debugging, and Monitoring Complex Graphs
35 HoursLangGraph is a framework designed for constructing stateful, multi-actor LLM applications as composable graphs, featuring persistent state and precise control over execution flows.
This instructor-led live training (available online or on-site) targets advanced AI platform engineers, AI DevOps professionals, and ML architects who aim to optimize, debug, monitor, and manage production-grade LangGraph systems.
Upon completing this training, participants will be able to:
- Design and optimize complex LangGraph topologies to enhance speed, reduce costs, and improve scalability.
- Ensure reliability through retries, timeouts, idempotency, and checkpoint-based recovery mechanisms.
- Debug and trace graph executions, inspect state variables, and systematically reproduce production issues.
- Instrument graphs with logs, metrics, and traces, deploy them to production, and monitor SLAs and costs effectively.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical practice.
- Hands-on implementation within a live-lab environment.
Customization Options
- To request customized training for this course, please contact us to arrange it.
Building Coding Agents with Devstral: From Agent Design to Tooling
14 HoursDevstral is an open-source framework tailored for creating and operating coding agents capable of interacting with codebases, developer tools, and APIs to boost engineering productivity.
This instructor-led live training (available online or on-site) targets intermediate to advanced ML engineers, developer-tooling teams, and SREs who want to design, implement, and optimize coding agents using Devstral.
Upon completion of this training, participants will be able to:
- Set up and configure Devstral for coding agent development.
- Design agentic workflows for exploring and modifying codebases.
- Integrate coding agents with developer tools and APIs.
- Apply best practices for secure and efficient agent deployment.
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 make arrangements.
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 (available online or onsite) targets intermediate 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 serves as a framework designed for constructing stateful, multi-agent LLM applications through composable graphs that maintain persistent state and provide granular control over execution.
This instructor-led live training, available online or on-site, targets intermediate to advanced professionals seeking to design, implement, and manage LangGraph-based financial solutions with robust governance, observability, and compliance measures.
Upon completion of this training, participants will be equipped to:
- Create finance-specific LangGraph workflows that align with regulatory and audit requirements.
- Integrate financial data standards and ontologies into graph states and associated tooling.
- Establish reliability, safety mechanisms, and human-in-the-loop controls for critical processes.
- Deploy, monitor, and optimize LangGraph systems to enhance performance, reduce costs, and meet SLAs.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical applications.
- Hands-on implementation within a live-lab environment.
Customization Options
- To request tailored training for this course, please contact us to arrange details.
LangGraph Foundations: Graph-Based LLM Prompting and Chaining
14 HoursLangGraph is a framework designed for constructing graph-structured Large Language Model (LLM) applications, enabling capabilities such as planning, branching, tool integration, memory management, and controlled execution.
This instructor-led live training, available both online and onsite, is tailored for beginner-level developers, prompt engineers, and data professionals who aim to design and implement reliable, multi-step LLM workflows using LangGraph.
Upon completing this training, participants will be able to:
- Articulate core LangGraph concepts—including nodes, edges, and state—and determine appropriate use cases for each.
- Create prompt chains that support branching logic, tool invocation, and persistent memory.
- Integrate retrieval mechanisms and external APIs into graph-based workflows.
- Test, debug, and evaluate LangGraph applications to ensure reliability and safety.
Course Format
- Interactive lectures coupled with guided discussions.
- Hands-on labs and code walkthroughs conducted in a sandbox environment.
- Scenario-based exercises focusing on design, testing, and evaluation.
Customization Options
- To arrange customized training for this course, please contact us directly.
LangGraph in Healthcare: Workflow Orchestration for Regulated Environments
35 HoursLangGraph empowers the creation of stateful, multi-actor workflows driven by Large Language Models (LLMs), offering precise control over execution paths and state persistence. In the healthcare sector, these capabilities are essential for ensuring compliance, enabling interoperability, and developing decision-support systems that seamlessly align with medical processes.
This instructor-led, live training session (available online or onsite) is designed for intermediate to advanced-level professionals looking to design, implement, and manage LangGraph-based healthcare solutions while navigating regulatory, ethical, and operational challenges.
Upon completing this training, participants will be able to:
- Design healthcare-specific LangGraph workflows that prioritize 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 within healthcare production settings.
Course Format
- Interactive lectures and discussions.
- Hands-on exercises featuring real-world case studies.
- Implementation practice in a live-lab environment.
Customization Options
- To request customized training for this course, please contact us to make arrangements.
LangGraph for Legal Applications
35 HoursLangGraph is a framework designed for constructing stateful, multi-agent LLM applications as composable graphs, featuring persistent state and precise execution control.
This instructor-led, live training (available online or onsite) targets intermediate to advanced professionals who want to design, implement, and manage legal solutions based on LangGraph, incorporating necessary compliance, traceability, and governance controls.
Upon completion of this training, participants will be able to:
- Create legal-specific LangGraph workflows that ensure auditability and compliance.
- Incorporate legal ontologies and document standards into graph state and processing.
- Implement guardrails, human-in-the-loop approvals, and traceable decision paths.
- Deploy, monitor, and maintain LangGraph services in production with observability and cost controls.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practice sessions.
- Hands-on implementation in a live-lab environment.
Customization Options
- To request a customized training for this course, please contact us to arrange.
Building Dynamic Workflows with LangGraph and LLM Agents
14 HoursLangGraph serves as a framework for assembling graph-structured workflows with Large Language Models (LLMs), enabling features such as branching, tool integration, memory management, and controlled execution.
This instructor-led live training, available online or on-site, targets intermediate engineers and product teams seeking to merge LangGraph’s graph logic with LLM agent loops. The goal is to develop dynamic, context-aware applications, including customer support agents, decision trees, and information retrieval systems.
Upon completion of this training, participants will be able to:
- Design graph-based workflows that effectively coordinate LLM agents, tools, and memory mechanisms.
- Implement conditional routing, retry logic, and fallback strategies to ensure robust execution.
- Integrate retrieval systems, APIs, and structured outputs into agent loops.
- Evaluate, monitor, and secure agent behavior to guarantee reliability and safety.
Course Format
- Interactive lectures facilitated by expert instructors, accompanied by group discussions.
- Guided laboratory sessions and code walkthroughs within a sandbox environment.
- Scenario-based design exercises coupled with peer review feedback.
Customization Options for the Course
- To arrange customized training tailored to your specific needs, please reach out to us.
LangGraph for Marketing Automation
14 HoursLangGraph serves as a graph-based orchestration framework designed to facilitate conditional, multi-step workflows involving Large Language Models (LLMs) and tools. It is particularly well-suited for automating and personalizing content pipelines.
This instructor-led training, available online or on-site, targets intermediate-level marketers, content strategists, and automation developers who aim to implement dynamic, branching email campaigns and content generation pipelines using LangGraph.
Upon completing this training, participants will be equipped to:
- Design graph-structured workflows for content and email that incorporate conditional logic.
- Integrate LLMs, APIs, and various data sources to enable automated personalization.
- Manage state, memory, and context effectively across multi-step campaigns.
- Evaluate, monitor, and optimize the performance and delivery outcomes of workflows.
Course Format
- Interactive lectures combined with group discussions.
- Hands-on labs focused on implementing email workflows and content pipelines.
- Scenario-based exercises covering personalization, segmentation, and branching logic.
Customization Options
- To arrange customized training for this course, please contact us to discuss your needs.
Le Chat Enterprise: Private ChatOps, Integrations & Admin Controls
14 HoursLe Chat Enterprise is a dedicated ChatOps platform that delivers secure, customizable, and policy-driven conversational AI capabilities for enterprises. It supports Role-Based Access Control (RBAC), Single Sign-On (SSO), connectors, and integration with enterprise applications.
This instructor-led live training (available online or onsite) is designed for intermediate-level product managers, IT leaders, solution engineers, and security/compliance teams seeking to deploy, configure, and govern Le Chat Enterprise within corporate environments.
Upon completion of this training, participants will be equipped to:
- Deploy and configure Le Chat Enterprise for secure operations.
- Implement RBAC, SSO, and compliance-focused controls.
- Connect Le Chat with enterprise applications and data repositories.
- Develop and execute governance and administrative playbooks for ChatOps.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical application.
- Hands-on implementation in a live laboratory environment.
Customization Options
- To request tailored training for this course, please contact us to arrange specifics.
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.
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.
Productizing Conversational Assistants with Mistral Connectors & Integrations
14 HoursMistral AI serves as an open-source AI platform that empowers teams to construct and embed conversational assistants within enterprise operations and customer-facing workflows.
This instructor-led live training, available in online or onsite formats, targets product managers, full-stack developers, and integration engineers at beginner to intermediate levels. The course focuses on equipping these professionals with the skills to design, integrate, and commercialize conversational assistants utilizing Mistral connectors and integrations.
Upon completing this training, participants will be capable of:
- Integrating Mistral conversational models with enterprise and SaaS connectors.
- Implementing retrieval-augmented generation (RAG) to ensure responses are grounded in accurate data.
- Designing user experience (UX) patterns for both internal and external chat assistants.
- Deploying assistants into product workflows to address real-world use cases.
Format of the Course
- Interactive lectures and discussions.
- Hands-on integration exercises.
- Live laboratory sessions for developing conversational assistants.
Course Customization Options
- To request a customized training program for this course, please contact us to make arrangements.
Enterprise-Grade Deployments with Mistral Medium 3
14 HoursMistral Medium 3 is a high-performance, multimodal large language model built for production-ready deployment within enterprise settings.
This instructor-led live training (available online or onsite) targets intermediate to advanced AI/ML engineers, platform architects, and MLOps professionals who aim to deploy, optimize, and secure Mistral Medium 3 for business-critical applications.
Upon completion of this training, participants will be able to:
- Deploy Mistral Medium 3 via API or self-hosted solutions.
- Enhance inference performance while managing costs effectively.
- Develop multimodal use cases leveraging Mistral Medium 3.
- Apply security and compliance best practices suitable for enterprise environments.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and hands-on practice.
- Practical implementation within a live lab environment.
Customization Options
- To arrange customized training for this course, please contact us.
Mistral for Responsible AI: Privacy, Data Residency & Enterprise Controls
14 HoursMistral AI offers an open, enterprise-ready platform designed to facilitate secure, compliant, and responsible AI deployments through robust feature sets.
This instructor-led training session, available both online and onsite, is tailored for compliance leaders, security architects, and legal or operations stakeholders at an intermediate proficiency level. Participants will learn how to establish responsible AI practices within Mistral by utilizing mechanisms for privacy protection, data residency management, and enterprise control.
Upon completion of this training, participants will be equipped to:
- Deploy privacy-preserving techniques using Mistral.
- Execute data residency strategies to satisfy regulatory obligations.
- Configure enterprise-level controls, including RBAC, SSO, and audit logging.
- Assess vendor offerings and deployment models to ensure alignment with compliance standards.
Course Format
- Interactive lectures coupled with group discussions.
- Case studies and practical exercises focused on compliance.
- Practical implementation of enterprise AI control mechanisms.
Customization Options
- For organizations seeking a tailored version of this course, please reach out to us to arrange customization.
Multimodal Applications with Mistral Models (Vision, OCR, & Document Understanding)
14 HoursMistral models are open-source artificial intelligence technologies that have expanded into multimodal workflows, enabling support for both language and visual tasks in enterprise and research contexts.
This instructor-led live training (available online or onsite) is designed for intermediate-level machine learning researchers, applied engineers, and product teams looking to develop multimodal applications with Mistral models, particularly OCR and document understanding pipelines.
Upon completing this training, participants will be able to:
- Configure and set up Mistral models for multimodal tasks.
- Build OCR workflows and integrate them with NLP pipelines.
- Create document understanding applications tailored to enterprise use cases.
- Implement vision-text search and assistive user interface functionalities.
Course Format
- Interactive lectures and discussions.
- Practical coding exercises.
- Live-lab implementation of multimodal pipelines.
Customization Options
- To request a customized version of this course, please contact us to arrange.
