Bespoke Applied Artificial Intelligence and LLM Engineering with Python Training Course

LangGraph serves as a framework for developing stateful, multi-agent LLM applications by composing graphs with persistent state and execution control.

This instructor-led live training, available online or onsite, is designed for advanced AI platform engineers, AI DevOps specialists, 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.
• Build reliability through retries, timeouts, idempotency, and checkpoint-based recovery mechanisms.
• Debug and trace graph executions, inspect states, and systematically reproduce production issues.
• Instrument graphs with logs, metrics, and traces; deploy to production; and monitor SLAs and costs.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practice sessions.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request customized training for this course, please contact us to arrange.

Devstral is an open-source framework purpose-built 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 onsite, targets intermediate to advanced ML engineers, developer-tooling teams, and SREs who aim 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 codebase exploration and modification.
• Integrate coding agents with developer tools and APIs.
• Implement best practices for secure and efficient agent deployment.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and hands-on practice.
• Live-lab implementation exercises.

Customization Options

• To request customized training for this course, please contact us to arrange.

This instructor-led, live training in France (online or onsite) is designed for data scientists and software engineers who wish to use Dask with the Python ecosystem to build, scale, and analyze large datasets.

By the end of this training, participants will be able to:

• Configure the environment to begin building big data processing solutions using Dask and Python.
• Explore the features, libraries, tools, and APIs available in Dask.
• Understand how Dask accelerates parallel computing in Python.
• Learn how to scale the Python ecosystem (including NumPy, SciPy, and Pandas) using Dask.
• Optimize the Dask environment to maintain high performance when handling large datasets.

This instructor-led, live training in France (online or onsite) is aimed at intermediate-level Python developers and data analysts who wish to enhance their skills in data analysis and manipulation using Pandas and NumPy.

By the end of this training, participants will be able to:

• Set up a development environment that includes Python, Pandas, and NumPy.
• Create a data analysis application using Pandas and NumPy.
• Perform advanced data wrangling, sorting, and filtering operations.
• Conduct aggregate operations and analyze time series data.
• Visualize data using Matplotlib and other visualization libraries.
• Debug and optimize their data analysis code.

The Devstral and Mistral models are open-source AI technologies engineered for flexible deployment, fine-tuning capabilities, and scalable integration.

This instructor-led live training, available either online or onsite, targets intermediate to advanced ML engineers, platform teams, and research engineers who aim to self-host, fine-tune, and govern Mistral and Devstral models within production environments.

Upon completing this training, participants will be equipped to:

• Establish and configure self-hosted environments for Mistral and Devstral models.
• Apply fine-tuning techniques to achieve domain-specific performance improvements.
• Implement versioning, monitoring, and lifecycle governance strategies.
• 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.

Customization Options

• To request customized training for this course, please contact us to arrange.

This instructor-led live training, offered online or onsite, is intended for developers aiming to leverage the FARM (FastAPI, React, and MongoDB) stack to build dynamic, high-performance, and scalable web applications.

By the end of this training, participants will be able to:

• Set up the necessary development environment that integrates FastAPI, React, and MongoDB.
• Understand the key concepts, features, and benefits of the FARM stack.
• Learn how to build REST APIs with FastAPI.
• Learn how to design interactive applications with React.
• Develop, test, and deploy applications (front end and back end) using the FARM stack.

This instructor-led live training in France (online or onsite) targets developers who wish to use FastAPI with Python to build, test, and deploy RESTful APIs more easily and quickly.

By the end of this training, participants will be able to:

• Set up the necessary development environment to develop APIs with Python and FastAPI.
• Create APIs quicker and easier using the FastAPI library.
• Learn how to create data models and schemas based on Pydantic and OpenAPI.
• Connect APIs to a database using SQLAlchemy.
• Implement security and authentication in APIs using the FastAPI tools.
• Build container images and deploy web APIs to a cloud server.

This instructor-led, live training in France (online or onsite) is aimed at beginner-level to intermediate-level researchers and laboratory professionals who wish to process and analyze images related to histological tissues, blood cells, algae, and other biological samples.

By the end of this training, participants will be able to:

• Navigate the Fiji interface and utilize ImageJ’s core functions.
• Preprocess and enhance scientific images for better analysis.
• Analyze images quantitatively, including cell counting and area measurement.
• Automate repetitive tasks using macros and plugins.
• Customize workflows for specific image analysis needs in biological research.

LangGraph serves as a framework designed for constructing stateful, multi-agent Large Language Model (LLM) applications through composable graphs, featuring persistent state and precise execution control.

This instructor-led, live training—available either online or onsite—is tailored for intermediate to advanced professionals seeking to design, implement, and manage LangGraph-based financial solutions that adhere to strict governance, observability, and compliance standards.

Upon completion of this training, participants will be equipped to:

• Design finance-specific LangGraph workflows that align with regulatory and audit requirements.
• Integrate financial data standards and ontologies into graph states and associated tooling.
• Implement reliability, safety, and human-in-the-loop controls essential for critical processes.
• Deploy, monitor, and optimize LangGraph systems to ensure optimal performance, cost-efficiency, and adherence to Service Level Agreements (SLAs).

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical applications.
• Hands-on implementation within a live laboratory environment.

Customization Options

• To request customized training for this course, please contact us to arrange your specific needs.

LangGraph is a framework designed for constructing graph-structured Large Language Model (LLM) applications, supporting capabilities such as planning, branching, tool usage, memory management, and controllable execution.

This instructor-led, live training (available online or onsite) is designed for beginner-level developers, prompt engineers, and data practitioners who aim to design and build reliable, multi-step LLM workflows using LangGraph.

Upon completion of this training, participants will be able to:

• Explain core LangGraph concepts (nodes, edges, state) and determine when to apply them.
• Construct prompt chains that branch, invoke tools, and maintain memory.
• Integrate retrieval mechanisms and external APIs into graph workflows.
• Test, debug, and evaluate LangGraph applications for reliability and safety.

Course Format

• Interactive lectures and facilitated discussions.
• Guided labs and code walkthroughs within a sandbox environment.
• Scenario-based exercises focused on design, testing, and evaluation.

Course Customization Options

• To request customized training for this course, please contact us to arrange.

LangGraph facilitates stateful, multi-actor workflows driven by Large Language Models (LLMs), offering precise control over execution paths and state persistence. These capabilities are essential in healthcare for ensuring compliance, enabling interoperability, and developing decision-support systems that align with clinical workflows.

This instructor-led, live training (available online or onsite) is designed for intermediate to advanced professionals looking to design, implement, and manage LangGraph-based healthcare solutions while addressing regulatory, ethical, and operational challenges.

Upon completion of this training, participants will be able to:

• Design LangGraph workflows tailored to healthcare, ensuring 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.

Course Format

• Interactive lectures and discussions.
• Hands-on exercises using real-world case studies.
• Practical implementation in a live-lab environment.

Customization Options

• To request a customized version of this training, please contact us to arrange.

LangGraph is a framework designed for constructing stateful, multi-actor LLM applications as composable graphs, featuring persistent state and precise execution control.

This instructor-led live training, available both online and onsite, targets intermediate to advanced professionals who aim to design, implement, and manage LangGraph-based legal solutions, ensuring the necessary compliance, traceability, and governance controls are in place.

Upon completion of this training, participants will be able to:

• Design legal-specific LangGraph workflows that maintain auditability and compliance.
• Integrate 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 environments with observability and cost controls.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practice sessions.
• Hands-on implementation within a live laboratory environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

LangGraph is a framework designed for composing graph-structured workflows powered by Large Language Models (LLMs). It supports features such as branching logic, tool integration, memory management, and controllable execution.

This instructor-led, live training session (available online or onsite) is tailored for intermediate-level engineers and product teams. Participants will learn to combine LangGraph's graph-based logic with LLM agent loops to construct dynamic, context-aware applications, including customer support agents, decision trees, and information retrieval systems.

By the conclusion of this training, participants will be able to:

• Design graph-based workflows that effectively coordinate LLM agents, tools, and memory.
• Implement conditional routing, retry mechanisms, and fallback strategies to ensure robust execution.
• Integrate data retrieval, APIs, and structured outputs into agent loops.
• Evaluate, monitor, and enhance the reliability and safety of agent behaviors.

Course Format

• Interactive lectures and facilitated discussions.
• Guided laboratory exercises and code walkthroughs within a sandbox environment.
• Scenario-based design tasks and peer review sessions.

Customization Options

• To request a customized training program for this course, please contact us to arrange.

LangGraph serves as a graph-based orchestration framework designed to facilitate conditional, multi-step workflows involving LLMs and tools, making it an excellent choice for automating and personalizing content pipelines.

This instructor-led live training, available either 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 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 achieve automated personalization.
• Managing state, memory, and context throughout 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

• To request customized training for this course, please contact us to arrange.

This instructor-led live training in France (online or on-site) is intended for data scientists and developers who wish to use Modin to build and implement parallel computations with Pandas for faster data analysis.

By the end of this training, participants will be able to:

• Configure the necessary environment to begin developing scalable Pandas workflows with Modin.
• Gain an understanding of Modin's features, architecture, and benefits.
• Distinguish between Modin, Dask, and Ray.
• Execute Pandas operations more efficiently using Modin.
• Implement the full Pandas API and its functions.