Artificial Intelligence with Python (Intermediate Level) Training Course

LangGraph 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.

Devstral 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.

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

• Set up the environment to start building big data processing with 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 (Numpy, SciPy, and Pandas) using Dask.
• Optimize the Dask environment to maintain high performance in handling large datasets.

This instructor-led, live training in Greece (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 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.

This instructor-led live training (online or onsite) targets developers who wish to use 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 Greece (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 Greece (online or onsite) is designed for beginner to intermediate-level researchers and laboratory professionals who wish to process and analyze images of histological tissues, blood cells, algae, and other biological samples.

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

• Navigating the Fiji interface and applying ImageJ’s core functionalities.
• Preprocessing and enhancing scientific images to improve analysis quality.
• Performing quantitative image analysis, such as cell counting and area measurement.
• Automating repetitive tasks through the use of macros and plugins.
• Tailoring workflows to meet specific image analysis requirements in biological research.

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

This instructor-led live training, available either online or onsite, is tailored for beginner-level developers, prompt engineers, and data practitioners eager to design and implement reliable, multi-step LLM workflows using LangGraph.

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

• Articulate core LangGraph concepts—such as nodes, edges, and state—and determine appropriate use cases.
• Develop prompt chains that support branching, tool invocation, and memory retention.
• Incorporate retrieval mechanisms and external APIs into graph-based workflows.
• Test, debug, and evaluate LangGraph applications to ensure reliability and safety.

Course Format

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

Customization Options

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

LangGraph 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 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.

LangGraph serves as a framework designed for constructing graph-based LLM workflows that enable branching, tool utilization, memory management, and controlled execution.

This instructor-led, live training session, available both online and onsite, targets intermediate-level engineers and product teams aiming to merge LangGraph’s graph logic with LLM agent loops to create dynamic, context-aware applications such as customer support assistants, decision trees, and information retrieval systems.

Upon completion of 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 processes, APIs, and structured outputs into agent loops.
• Evaluating, monitoring, and securing agent behavior to enhance reliability and safety.

Course Format

• Interactive lectures accompanied by facilitated discussions.
• Guided laboratory exercises and code walkthroughs within a sandbox environment.
• Scenario-based design exercises and peer reviews.

Customization Options for the Course

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

LangGraph 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.

This instructor-led, live training in Greece (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.