Natural Language Processing (NLP) with Python spaCy Training Course

LangGraph serves as a framework for constructing stateful, multi-agent LLM applications by utilizing composable graphs that maintain persistent state and precise control over execution flow.

This instructor-led live training, available both online and onsite, is tailored for advanced AI platform engineers, AI-focused DevOps professionals, and ML architects aiming to optimize, debug, monitor, and operate production-grade LangGraph systems.

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

• Design and optimize complex LangGraph topologies to enhance speed, manage costs, and ensure scalability.
• Ensure system 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 environments; and monitor SLAs and costs effectively.

Course Format

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

Course Customization Options

• For those interested in customizing this training to fit specific needs, please contact us to arrange a tailored session.

Devstral is an open-source framework engineered for the creation and execution of coding agents capable of interacting with codebases, developer tools, and APIs to boost engineering productivity.

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

• Setting up and configuring Devstral for coding agent development.
• Designing agentic workflows tailored for codebase exploration and modification.
• Integrating coding agents with various developer tools and APIs.
• Implementing best practices for secure and efficient agent deployment.

Course Format

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

Course Customization Options

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

This instructor-led live training in Brazil (online or on-site) is designed for data scientists and software engineers who aim to utilize Dask with the Python ecosystem to construct, scale, and analyze large datasets.

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

• Configure 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 Brazil (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 to offer flexible deployment options, fine-tuning capabilities, and scalable integration.

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

Upon completion 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 enhance 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.

Customization Options

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

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

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

• Set up the requisite development environment for FastAPI, React, and MongoDB.
• Understand the fundamental concepts, features, and benefits of the FARM stack.
• Learn to create REST APIs using FastAPI.
• Acquire skills in designing interactive applications with React.
• Develop, test, and deploy applications (both frontend and backend) using the FARM stack.

This instructor-led live training in Brazil (online or onsite) is designed for developers who want to use FastAPI with Python to build, test, and deploy RESTful APIs more quickly and easily.

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 as composable graphs, featuring persistent state and granular 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 compliance.

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

• Design finance-specific LangGraph workflows that align with regulatory and audit standards.
• Integrate financial data standards and ontologies into graph state and tooling.
• Implement reliability, safety, and human-in-the-loop controls for critical processes.
• Deploy, monitor, and optimize LangGraph systems to ensure optimal performance, cost-efficiency, and SLA adherence.

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 a session.

LangGraph is a framework designed for developing LLM applications structured as graphs, featuring capabilities for planning, branching, tool usage, memory management, and controllable execution.

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

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

• Explain core LangGraph concepts (nodes, edges, state) and their appropriate use cases.
• Construct prompt chains that branch, invoke tools, and maintain memory.
• Integrate retrieval mechanisms and external APIs into graph-based workflows.
• Test, debug, and evaluate LangGraph applications to ensure reliability and safety.

Course Format

• Interactive lectures and facilitated discussions.
• Guided labs and code walkthroughs conducted in 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 it.

LangGraph empowers stateful, multi-actor workflows driven by LLMs, offering precise control over execution paths and state persistence. These capabilities are vital in healthcare for ensuring compliance, 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 seeking 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 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.

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 by composing 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 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.
• Implementing guardrails, human-in-the-loop approvals, and traceable decision paths.
• Deploying, monitoring, and maintaining LangGraph services in production environments with observability and cost controls.

Course Format

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

Customization Options

• For customized training on this topic, please contact us to arrange a session.

LangGraph is a framework designed for composing graph-structured LLM workflows that support branching, tool use, memory, and controllable execution.

This instructor-led, live training (online or onsite) targets intermediate-level engineers and product teams who wish to combine LangGraph’s graph logic with LLM agent loops to build 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:

• Design graph-based workflows that coordinate LLM agents, tools, and memory.
• Implement conditional routing, retries, and fallbacks for robust execution.
• Integrate retrieval, APIs, and structured outputs into agent loops.
• Evaluate, monitor, and harden agent behavior for reliability and safety.

Format of the Course

• Interactive lecture and facilitated discussion.
• Guided labs and code walkthroughs 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 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.

Le Chat Enterprise is a private ChatOps solution that provides secure, customizable, and governed conversational AI capabilities for organizations, with support for RBAC, SSO, connectors, and enterprise app integrations.

This instructor-led, live training (online or onsite) is aimed at intermediate-level product managers, IT leads, solution engineers, and security/compliance teams who wish to deploy, configure, and govern 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.
• 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 Brazil (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.