Text Summarization with Python Training Course

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

This instructor-led live training, available online or onsite, targets advanced-level AI platform engineers, DevOps for AI professionals, and ML architects who aim to optimize, debug, monitor, and operate 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 ensure scalability.
• Engineer system reliability through retries, timeouts, idempotency, and checkpoint-based recovery mechanisms.
• Debug and trace graph executions, inspect state, and systematically reproduce production issues.
• Instrument graphs with logs, metrics, and traces, deploy them to production, and monitor SLAs and associated costs.

Course Format

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

Course Customization Options

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

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 Slovakia (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 Slovakia (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 represent open-source AI technologies engineered for flexible deployment, fine-tuning capabilities, and scalable integration.

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

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

• Establish and configure self-hosted environments for Mistral and Devstral models.
• Utilize fine-tuning techniques to optimize performance for specific domains.
• Implement versioning, monitoring, and lifecycle governance protocols.
• 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 arrange a customized training session for this course, please contact us directly.

This instructor-led, live training, offered online or onsite, is designed for developers aiming to utilize 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 required development environment integrating FastAPI, React, and MongoDB.
• Understand the core concepts, features, and benefits of the FARM stack.
• Learn how to construct REST APIs with FastAPI.
• Learn how to design interactive applications with React.
• Develop, test, and deploy applications (both front end and back end) using the FARM stack.

This instructor-led, live training in Slovakia (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.

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

This instructor-led, live training session, available both online and onsite, targets intermediate to advanced professionals seeking to design, implement, and operate finance solutions based on LangGraph, ensuring proper governance, observability, and compliance.

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 tooling.
• Implement reliability, safety mechanisms, and human-in-the-loop controls for critical processes.
• Deploy, monitor, and optimize LangGraph systems to enhance performance, manage costs, and meet SLAs.

Course Format

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

Customization Options

• For customized training tailored to your specific needs, please contact us to arrange the details.

LangGraph serves as a framework designed for constructing LLM applications structured as graphs, supporting features such as planning, branching, tool integration, memory management, and controlled execution.

This instructor-led, live training (available online or onsite) is tailored for developers at the beginner level, 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 the core concepts of LangGraph (nodes, edges, and state) and identify appropriate use cases for each.
• Construct prompt chains that support branching, tool invocation, and memory retention.
• Integrate retrieval mechanisms and external APIs into graph-based workflows.
• Test, debug, and evaluate LangGraph applications to ensure reliability and safety.

Format of the Course

• Interactive lectures and facilitated discussions.
• Guided labs and code walkthroughs 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 make arrangements.

LangGraph facilitates 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, enhancing interoperability, and developing decision-support systems that align with medical workflows.

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

Upon completing this training, participants will be able to:

• Design healthcare-specific LangGraph workflows with compliance and auditability in mind.
• Integrate LangGraph applications with medical ontologies and standards (FHIR, SNOMED CT, 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.
• Implementation practice in a live-lab environment.

Customization Options

• To request a customized training session for this course, 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 training, available online or on-site, targets intermediate to advanced professionals seeking to design, implement, and manage LangGraph-based legal solutions with the necessary compliance, traceability, and governance controls.

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

• Design legal-specific LangGraph workflows that ensure 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.
• Numerous exercises and practical sessions.
• Hands-on implementation within a live lab environment.

Customization Options

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

LangGraph serves as a framework designed for assembling graph-structured LLM workflows that facilitate branching, tool utilization, memory management, and controlled execution.

This instructor-led, live training (available online or onsite) targets intermediate-level engineers and product teams seeking to integrate LangGraph’s graph logic with LLM agent loops to create dynamic, context-aware applications, such as customer support agents, 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 for robust execution.
• Integrating retrieval processes, APIs, and structured outputs into agent loops.
• Evaluating, monitoring, and hardening agent behavior to ensure reliability and safety.

Format of the Course

• Interactive lecture and facilitated discussion.
• Guided labs and code walkthroughs conducted in a sandbox environment.
• Scenario-based design exercises and peer reviews.

Course Customization Options

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

LangGraph is a graph-based orchestration framework that facilitates 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 designed for intermediate-level marketers, content strategists, and automation developers who want 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.

Le Chat Enterprise offers a secure, customizable, and governed conversational AI solution designed for organizations. It supports RBAC, SSO, connectors, and enterprise app integrations.

This instructor-led training (online or onsite) targets intermediate-level product managers, IT leads, solution engineers, and security/compliance teams interested in deploying, configuring, and governing Le Chat Enterprise.

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.
• 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 Slovakia (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.