Natural Language Processing (NLP) with Python spaCy Training Course

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

This instructor-led, live training (available online or on-site) targets advanced AI platform engineers, DevOps professionals for 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.
• Enhance reliability through mechanisms such as retries, timeouts, idempotency, and checkpoint-based recovery.
• Effectively 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 costs.

Format of the Course

• Interactive lecture and discussion sessions.
• Extensive 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 the details.

Devstral is an open-source framework designed to create and run coding agents that can interact with codebases, developer tools, and APIs, thereby enhancing engineering productivity.

This instructor-led, live training (available online or on-site) is targeted 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 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.

This instructor-led, live training in Taiwan (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 Taiwan (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 the Mistral and Devstral models.
• Apply fine-tuning techniques to enhance performance for specific domains.
• Implement versioning, monitoring, and lifecycle management practices.
• Ensure secure, compliant, and responsible use of open-source models.

Format of the Course

• Interactive lectures and discussions.
• Practical 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 Taiwan (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 that maintain persistent state and offer control over execution.

This instructor-led, live training (available both online and onsite) 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 comply with regulatory and audit requirements.
• Incorporate financial data standards and ontologies into the graph state and tooling.
• Implement reliability, safety, and human-in-the-loop controls for critical processes.
• Deploy, monitor, and optimize LangGraph systems to meet performance, cost, and SLA requirements.

Format of the Course

• Interactive lectures and discussions.
• Ample 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 to build 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 want 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 tools, and maintain memory.
• Incorporate retrieval and external APIs into graph workflows.
• Test, debug, and evaluate LangGraph applications for reliability and safety.

Format of the Course

• Interactive lectures and facilitated discussions.
• 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 facilitates stateful, multi-actor workflows driven by LLMs, offering precise control over execution paths and state persistence. In the healthcare sector, these features are essential for ensuring compliance, interoperability, and the development of decision-support systems that align with medical workflows.

This instructor-led, live training (available online or on-site) is designed for intermediate to advanced professionals who aim 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:

• Create 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.
• Implement 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 lectures and discussions.
• Hands-on exercises with real-world case studies.
• Practical 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 constructing stateful, multi-actor LLM applications using composable graphs that maintain persistent state and offer precise control over execution.

This instructor-led, live training (available both online and onsite) targets intermediate to advanced professionals who aim to design, implement, and operate LangGraph-based legal solutions with the necessary compliance, traceability, and governance controls.

By the end 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 with observability and cost controls.

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 workflows with LLMs, supporting branching, tool integration, memory management, and controlled execution.

This instructor-led, live training (available online or on-site) is targeted at intermediate engineers and product teams who want to merge LangGraph’s graph logic with LLM agent loops 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:

• Design graph-based workflows that coordinate LLM agents, tools, and memory effectively.
• Implement conditional routing, retries, and fallbacks to ensure robust execution.
• Integrate retrieval mechanisms, APIs, and structured outputs into agent loops.
• Evaluate, monitor, and enhance the reliability and safety of agent behavior.

Format of the Course

• Interactive lectures and facilitated discussions.
• 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 designed to facilitate conditional, multi-step workflows involving language models and tools. It is particularly useful for automating and personalizing content pipelines.

This instructor-led, live training (available both online and on-site) is tailored for intermediate-level marketers, content strategists, and automation developers who are interested in implementing dynamic, branching email campaigns and content generation pipelines using LangGraph.

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

• Create 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 throughout multi-step campaigns.
• Assess, 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

• For a customized training session 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. It supports Role-Based Access Control (RBAC), Single Sign-On (SSO), connectors, and integrations with enterprise applications.

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 want to deploy, configure, and govern Le Chat Enterprise in their 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 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.

This instructor-led, live training in Taiwan (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.