Bespoke Applied Artificial Intelligence and LLM Engineering with Python Training Course

LangGraph is a framework designed for creating stateful, multi-actor LLM applications through composable graphs that maintain persistent state and provide execution control.

This instructor-led, live training (available online or onsite) targets advanced AI platform engineers, AI DevOps specialists, and ML architects who aim to optimize, debug, monitor, and manage production-grade LangGraph systems.

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

• Design and optimize complex LangGraph topologies for enhanced speed, cost-efficiency, and scalability.
• Engineer 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 to production; and monitor SLAs and costs.

Format of the Course

• Interactive lecture and discussion.
• Extensive exercises and practical application.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

Devstral is an open-source framework engineered for the creation and execution of coding agents capable of interacting with code repositories, developer utilities, and APIs to boost engineering efficiency.

This instructor-led, live training (available online or on-site) targets intermediate to advanced ML engineers, developer-tooling teams, and Site Reliability Engineers (SREs) who aim to design, implement, and optimize coding agents using Devstral.

Upon completing this training, participants will be able to:

• Establish and configure the Devstral environment 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 application.
• Hands-on implementation in a live laboratory environment.

Customization Options

• To request tailored training for this course, please contact us to arrange the details.

This instructor-led live training, delivered Romania (online or onsite), is designed for data scientists and software engineers who aim to utilize Dask within the Python ecosystem to build, scale, and analyze large datasets.

Upon completing this training, participants will be capable of:

• Configuring the environment necessary to begin building big data processing workflows with Dask and Python.
• Exploring the features, libraries, tools, and APIs offered by Dask.
• Gaining insight into how Dask accelerates parallel computing capabilities in Python.
• Learning techniques to scale the Python ecosystem (including NumPy, SciPy, and Pandas) using Dask.
• Optimizing the Dask environment to ensure high performance when managing large datasets.

This instructor-led, live training in Romania (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.

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

This instructor-led live training (available online or onsite) is tailored for 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 completion of this training, participants will be capable of:

• Setting up and configuring self-hosted environments for Mistral and Devstral models.
• Applying fine-tuning techniques to enhance domain-specific performance.
• Implementing versioning, monitoring, and lifecycle governance strategies.
• Ensuring 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 tailored training for this course, please contact us to arrange.

This instructor-led live training, offered online or onsite, targets developers who want to use the FARM stack (FastAPI, React, and MongoDB) to build dynamic, high-performance, and scalable web applications.

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

• Set up a 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 Romania (online or onsite) is aimed at developers who wish to use FastAPI with Python to build, test, and deploy RESTful APIs easier and faster.

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 Romania (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 for constructing stateful, multi-agent LLM applications using composable graphs that maintain persistent state and provide precise control over execution flow.

This instructor-led live training, available online or on-site, targets intermediate to advanced professionals aiming to design, implement, and manage LangGraph-based financial solutions with robust governance, observability, and regulatory compliance.

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

• Design finance-specific LangGraph workflows that align with regulatory and audit requirements.
• Integrate financial data standards and ontologies into graph states and associated tools.
• Implement reliability, safety measures, and human-in-the-loop controls for critical operations.
• Deploy, monitor, and optimize LangGraph systems to ensure high performance, cost efficiency, and adherence to SLAs.

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.

LangGraph is a framework designed for constructing graph-structured Large Language Model (LLM) applications that facilitate planning, branching, tool utilization, memory management, and controlled execution.

This instructor-led, live training—available online or on-site—is tailored for beginner-level developers, prompt engineers, and data practitioners aiming to design and build reliable, multi-step LLM workflows using LangGraph.

Upon completing this training, participants will be capable of:

• Explaining fundamental LangGraph concepts (nodes, edges, state) and understanding their appropriate use cases.
• Constructing prompt chains that branch, invoke tools, and maintain context memory.
• Integrating retrieval mechanisms and external APIs into graph-based workflows.
• Testing, debugging, and evaluating LangGraph applications to ensure reliability and safety.

Course Format

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

Course Customization Options

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

LangGraph empowers stateful, multi-actor workflows driven by LLMs, offering precise control over execution paths and state persistence. For the healthcare sector, these capabilities are essential for ensuring compliance, enabling interoperability, and developing decision-support systems that seamlessly integrate with medical workflows.

This instructor-led, live training—available either online or on-site—is designed for intermediate to advanced professionals looking to design, implement, and manage LangGraph-based healthcare solutions while navigating regulatory, ethical, and operational challenges.

Upon completion of this training, participants will be capable of:

• Designing healthcare-specific LangGraph workflows that prioritize compliance and auditability.
• Integrating LangGraph applications with medical ontologies and standards (FHIR, SNOMED CT, ICD).
• Applying best practices for reliability, traceability, and explainability within sensitive environments.
• Deploying, monitoring, and validating LangGraph applications in healthcare production settings.

Format of the Course

• Interactive lectures and discussions.
• Hands-on exercises based on real-world case studies.
• Implementation practice within a live-lab environment.

Course Customization Options

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

LangGraph serves as a framework for developing stateful, multi-actor LLM applications through composable graphs that maintain persistent state and offer precise execution control.

This instructor-led live training, available online or onsite, targets intermediate to advanced professionals seeking to design, implement, and manage LangGraph-based legal solutions with robust compliance, traceability, and governance controls.

Upon completion, participants will be capable of:

• Designing legal-specific LangGraph workflows that ensure auditability and regulatory compliance.
• Integrating legal ontologies and document standards into graph state and processing logic.
• Implementing guardrails, human-in-the-loop approvals, and traceable decision paths.
• Deploying, monitoring, and maintaining LangGraph services in production environments with observability and cost management.

Course Format

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

Customization Options

• For customized training requests, please contact us to arrange.

LangGraph serves as a framework for constructing graph-structured workflows involving Large Language Models (LLMs), enabling features such as branching, tool integration, memory management, and controlled execution.

This instructor-led live training, available either online or onsite, targets intermediate engineers and product teams looking to merge LangGraph’s graph logic with LLM agent loops. The goal is to build dynamic, context-aware applications, including customer support bots, decision trees, and information retrieval systems.

Upon completing this training, participants will be capable of:

• Designing graph-based workflows that effectively 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 ensure reliability and safety.

Course Format

• Interactive lectures and guided discussions.
• Hands-on labs and code walkthroughs within a sandbox environment.
• Scenario-based design exercises and peer reviews.

Customization Options

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

LangGraph 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.

This instructor-led, live training in Romania (online or onsite) is aimed at 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:

• Set up the necessary environment to start developing Pandas workflows at scale with Modin.
• Understand the features, architecture, and advantages of Modin.
• Know the differences between Modin, Dask, and Ray.
• Perform Pandas operations faster with Modin.
• Implement the entire Pandas API and functions.