Natural Language Processing (NLP) with Python spaCy Training Course

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

This instructor-led, live training (available online or on-site) is targeted at advanced-level AI platform engineers, DevOps professionals specializing in AI, and ML architects who aim to optimize, debug, monitor, and operate production-grade LangGraph systems.

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

• Design and optimize complex LangGraph topologies for improved speed, cost efficiency, and scalability.
• Implement reliability features such as retries, timeouts, idempotency, and checkpoint-based recovery.
• Debug and trace graph executions, examine state, and systematically reproduce production issues.
• Instrument graphs with logs, metrics, and traces, deploy them to production, and monitor SLAs and costs.

Format of the Course

• Interactive lecture and discussion sessions.
• Plenty of exercises and practical activities.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a 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 (available both online and onsite) is aimed at intermediate to advanced ML engineers, developer-tooling teams, and SREs who want 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 developing coding agents.
• Create agentic workflows for exploring and modifying codebases.
• 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.
• Plenty of exercises and practice sessions.
• 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 models are open-source AI technologies designed for flexible deployment, fine-tuning, and scalable integration.

This instructor-led, live training (online or onsite) is aimed at intermediate to advanced ML engineers, platform teams, and research engineers who wish to self-host, fine-tune, and manage 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 optimize performance for specific domains.
• Implement versioning, monitoring, and lifecycle management.
• Ensure the security, compliance, and responsible use 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.

This instructor-led, live training in (online or onsite) is aimed at 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 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 is a framework designed for constructing stateful, multi-actor LLM applications using composable graphs with persistent state and controlled execution.

This instructor-led, live training (available online or on-site) is targeted at intermediate to advanced professionals who aim to design, implement, and manage LangGraph-based financial solutions while ensuring proper governance, observability, and compliance.

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

• Create finance-specific LangGraph workflows that align with regulatory and audit requirements.
• Incorporate financial data standards and ontologies into graph state and tools.
• Implement reliability, safety, and human-in-the-loop controls for critical processes.
• Deploy, monitor, and optimize LangGraph systems to ensure performance, cost-effectiveness, and service level agreements (SLAs).

Format of the Course

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

Course Customization Options

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

LangGraph is a framework designed for creating graph-structured LLM applications that support planning, branching, tool integration, memory management, and controllable execution.

This instructor-led, live training (available online or on-site) is aimed at beginner-level developers, prompt engineers, and data practitioners who wish to design and build reliable, multi-step LLM workflows using LangGraph.

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

• Understand key LangGraph concepts (nodes, edges, state) and know when to apply them.
• Create prompt chains that can branch, call external tools, and maintain memory.
• Incorporate retrieval mechanisms and external APIs into graph workflows.
• Test, debug, and evaluate LangGraph applications for reliability and safety.

Format of the Course

• Interactive lecture and facilitated discussion.
• Guided labs and code walkthroughs in a sandbox environment.
• Scenario-based exercises focused on design, testing, and evaluation.

Course Customization Options

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

LangGraph enables stateful, multi-actor workflows powered by LLMs with precise control over execution paths and state persistence. In healthcare, these capabilities are essential for compliance, interoperability, and building decision-support systems that align with medical workflows.

This instructor-led, live training (online or onsite) is aimed at intermediate to advanced-level professionals who wish to design, implement, and manage LangGraph-based healthcare solutions while addressing regulatory, ethical, and operational challenges.

By the end 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 (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.

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 is a framework designed for creating stateful, multi-actor LLM applications as composable graphs that maintain persistent state and offer precise control over execution.

This instructor-led, live training (available both online and on-site) is targeted at intermediate to advanced professionals who aim to design, implement, and operate LangGraph-based legal solutions with the required compliance, traceability, and governance controls.

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

• Create legal-specific LangGraph workflows that ensure auditability and compliance.
• Integrate legal ontologies and document standards into the graph state and processing.
• Implement guardrails, human-in-the-loop approvals, and traceable decision paths.
• Deploy, monitor, and maintain LangGraph services in a production environment with observability and cost controls.

Format of the Course

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

Course Customization Options

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

LangGraph is a framework designed for creating graph-structured workflows with large language models (LLMs). It supports branching, tool integration, memory management, and controllable execution processes.

This instructor-led, live training (available online or on-site) is targeted at intermediate-level engineers and product teams who aim to integrate LangGraph’s graph logic with LLM agent loops. This will enable them to develop dynamic, context-aware applications such as customer support agents, decision trees, and information retrieval systems.

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

• Create graph-based workflows that effectively coordinate LLM agents, tools, and memory.
• Implement conditional routing, retries, and fallback mechanisms to ensure robust execution.
• Incorporate retrieval, APIs, and structured outputs into agent loops for enhanced functionality.
• Evaluate, monitor, and fortify agent behavior to ensure reliability and safety.

Format of the Course

• Interactive lectures and facilitated discussions.
• Guided labs and code walkthroughs in a secure sandbox environment.
• Scenario-based design exercises and peer reviews to enhance practical skills.

Course Customization Options

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

LangGraph is a graph-based orchestration framework that enables conditional, multi-step workflows involving language models and tools, making it ideal for automating and personalizing content pipelines.

This instructor-led, live training (available online or on-site) 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 that incorporate conditional logic.
• Integrate language models, APIs, and data sources for automated personalization.
• Manage state, memory, and context across multi-step campaigns.
• Evaluate, monitor, and optimize the performance and delivery outcomes of workflows.

Format of the Course

• Interactive lectures and group discussions.
• Hands-on labs for implementing email workflows and content pipelines.
• Scenario-based exercises focusing 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 is a private ChatOps solution that offers secure, customizable, and governed conversational AI capabilities for organizations, supporting role-based access control (RBAC), single sign-on (SSO), connectors, and enterprise application integrations.

This instructor-led, live training (available online or on-site) is designed for intermediate-level product managers, IT leaders, solution engineers, and security/compliance teams who are looking to deploy, configure, and manage Le Chat Enterprise in enterprise environments.

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

• Set up and configure Le Chat Enterprise for secure deployments.
• Implement 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.
• Extensive exercises and practice sessions.
• 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.