Natural Language Processing (NLP) with Python spaCy Training Course

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

This instructor-led live training, available online or onsite, is designed for advanced AI platform engineers, DevOps professionals specializing in AI, 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 reliability by implementing 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.

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 version of this course, please contact us to make arrangements.

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

This instructor-led, live training session, available either online or onsite, targets intermediate to advanced ML engineers, developer-tooling teams, and SREs who aim to design, implement, and optimize coding agents using Devstral.

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

• Configure and set up Devstral for coding agent development.
• Design agentic workflows tailored for codebase exploration and modification.
• Seamlessly integrate coding agents with developer tools and APIs.
• Apply best practices to ensure secure and efficient agent deployment.

Course Format

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

Customization Options

• To request a customized training curriculum for this course, please contact us for arrangement.

This instructor-led live training in Argentina (available online or onsite) targets data scientists and software engineers who intend to utilize Dask alongside the Python ecosystem to construct, scale, and analyze large datasets.

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

• Configure the environment necessary to begin developing big data solutions using Dask and Python.
• Investigate the features, libraries, tools, and APIs provided by Dask.
• Comprehend how Dask enhances parallel computing capabilities in Python.
• Acquire the skills to scale the Python ecosystem, including NumPy, SciPy, and Pandas, using Dask.
• Optimize the Dask environment to ensure high performance when managing large datasets.

This instructor-led, live training in Argentina (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.

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

This instructor-led, live training—available online or onsite—is designed 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 completing 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.

Course Customization Options

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

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

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

• Set up the required development environment integrating FastAPI, React, and MongoDB.
• Understand the fundamental 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 Argentina (online or onsite) is designed for developers who want to use Python with FastAPI to build, test, and deploy RESTful APIs more easily and quickly.

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-agent Large Language Model applications through composable graphs that maintain persistent state and provide granular control over execution flows.

This instructor-led live training, available both online and on-site, targets intermediate to advanced professionals aiming to design, implement, and manage LangGraph-based solutions within the finance sector, ensuring robust governance, observability, and regulatory compliance.

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

• Developing finance-specific LangGraph workflows that align with regulatory standards and audit requirements.
• Integrating financial data standards and ontologies into graph states and associated tooling.
• Establishing reliability, safety mechanisms, and human-in-the-loop controls for critical operational processes.
• Deploying, monitoring, and optimizing LangGraph systems to enhance performance, manage costs, and meet Service Level Agreements (SLAs).

Course Format

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

Customization Options

• For customized training arrangements, please reach out to our team.

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

This instructor-led, live training (available online or onsite) 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:

• Explain core LangGraph concepts (nodes, edges, state) and when to use them.
• Build prompt chains that branch, call tools, and maintain memory.
• Integrate retrieval and external APIs into graph workflows.
• Test, debug, and evaluate LangGraph apps 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 on design, testing, and evaluation.

Course Customization Options

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

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, enabling interoperability, and developing decision-support systems that align with medical processes.

This instructor-led training, available online or onsite, targets intermediate to advanced 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 that prioritize 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.

Course Format

• Interactive lectures and discussions.
• Hands-on exercises featuring real-world case studies.
• Implementation practice 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 constructing stateful, multi-actor LLM applications through composable graphs that offer persistent state and precise control over execution.

This instructor-led, live training (available online or onsite) is targeted at intermediate to advanced professionals seeking to design, implement, and operate LangGraph-based legal solutions that meet necessary compliance, traceability, and governance standards.

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

• Design LangGraph workflows specific to legal needs that maintain auditability and compliance.
• Integrate legal ontologies and document standards into graph state and processing workflows.
• 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.
• Extensive 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 composing graph-structured workflows involving Large Language Models (LLMs), enabling capabilities such as branching, tool utilization, memory management, and controlled execution.

This instructor-led live training, available both online and on-site, targets intermediate-level engineers and product teams aiming to integrate LangGraph’s graph logic with LLM agent loops. The goal is to create dynamic, context-aware applications, including customer support agents, decision trees, and information retrieval systems.

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

• Design graph-based workflows that effectively coordinate LLM agents, tools, and memory.
• Implement conditional routing, retry mechanisms, and fallback strategies to ensure robust execution.
• Integrate retrieval processes, APIs, and structured outputs into agent loops.
• Evaluate, monitor, and reinforce agent behavior to guarantee reliability and safety.

Course Format

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

Customization Options for the Course

• For requests regarding 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.

Le Chat Enterprise offers a secure, customizable, and governed conversational AI solution tailored for organizations, featuring support for RBAC, SSO, connectors, and enterprise app integrations.

This instructor-led, live training (online or onsite) targets intermediate-level product managers, IT leads, solution engineers, and security/compliance teams aiming 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 Argentina (available online or onsite) is designed for data scientists and developers who wish to use Modin to build and implement parallel computations with Pandas for faster data analysis.

Upon completing this training, participants will be able to:

• Configure the necessary environment to begin developing Pandas workflows at scale using Modin.
• Gain a clear understanding of Modin’s features, architecture, and advantages.
• Identify the key differences between Modin, Dask, and Ray.
• Execute Pandas operations more efficiently with the help of Modin.
• Implement the full range of Pandas API functions.