Bespoke Applied Artificial Intelligence and LLM Engineering with Python Training Course

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

This instructor-led live training, available online or onsite, targets advanced AI platform engineers, AI DevOps specialists, and ML architects who aim to optimize, debug, monitor, and manage production-grade LangGraph systems.

Upon completing this training, participants will be able to:

• Design and optimize complex LangGraph topologies for enhanced speed, cost-efficiency, and scalability.
• Ensure 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 costs.

Course Format

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

Course Customization Options

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

Devstral serves as an open-source framework engineered for the creation and execution of coding agents capable of engaging with codebases, developer utilities, and APIs to elevate engineering efficiency.

This instructor-led, live training session (available online or onsite) targets intermediate to advanced ML engineers, developer-tooling teams, and SREs aiming to design, implement, and refine coding agents utilizing Devstral.

Upon completing this training, participants will possess the ability to:

• Establish and configure 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 for secure and efficient agent deployment.

Course Format

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

Customization Options

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

This instructor-led, live training in Taiwan (online or onsite) is designed for 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 begin 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 (including Numpy, SciPy, and Pandas) using Dask.
• Optimize the Dask environment to maintain high performance when 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 engineered for flexible deployment, fine-tuning, and scalable integration.

This instructor-led, live training (available online or onsite) is tailored for intermediate to advanced-level ML engineers, platform teams, and research engineers who wish to self-host, fine-tune, and govern Mistral and Devstral models in 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 optimize performance for specific domains.
• 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.

This instructor-led live training (online or onsite) is designed for 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 tailored for developers who wish to utilize FastAPI with Python to build, test, and deploy RESTful APIs with greater ease and speed.

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

• Configure the necessary development environment for creating APIs with Python and FastAPI.
• Develop APIs more quickly and effortlessly using the FastAPI library.
• Learn to create data models and schemas based on Pydantic and OpenAPI standards.
• Integrate APIs with databases using SQLAlchemy.
• Implement security measures and authentication within APIs using FastAPI tools.
• Construct container images and deploy web APIs to cloud servers.

This instructor-led, live training in Taiwan (online or onsite) is aimed at beginner-level to intermediate-level researchers and laboratory professionals who wish to process and analyze images related to histological tissues, blood cells, algae, and other biological samples.

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

• Navigate the Fiji interface and utilize ImageJ’s core functions.
• Preprocess and enhance scientific images for better analysis.
• Analyze images quantitatively, including cell counting and area measurement.
• Automate repetitive tasks using macros and plugins.
• Customize workflows for specific image analysis needs in biological research.

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

This instructor-led, live training (available online or on-site) is designed for intermediate to advanced professionals seeking to design, implement, and operate LangGraph-based financial solutions with robust governance, observability, and compliance.

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

• Designing finance-specific LangGraph workflows that align with regulatory and audit requirements.
• Integrating financial data standards and ontologies into graph state and tooling.
• Implementing reliability, safety, and human-in-the-loop controls for critical processes.
• Deploying, monitoring, and optimizing LangGraph systems to meet performance, cost, and SLA objectives.

Course Format

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

Course Customization Options

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

LangGraph serves as a framework for developing graph-structured LLM applications, enabling capabilities such as planning, branching, tool usage, memory management, and controllable execution.

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

Upon completion 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-agent workflows driven by LLMs, offering precise control over execution paths and state persistence. In the healthcare sector, these capabilities are vital for ensuring compliance, interoperability, and the development of decision-support systems that align with clinical workflows.

This instructor-led live training, available online or onsite, targets intermediate to advanced professionals looking 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 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 utilizing real-world case studies.
• Implementation practice within a live laboratory environment.

Course Customization Options

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

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, live training (available online or onsite) targets intermediate to advanced professionals seeking to design, implement, and operate LangGraph-based legal solutions with robust compliance, traceability, and governance controls.

Upon completing this training, participants will be able to:

• Design legal-specific LangGraph workflows that preserve 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.

Course Format

• Interactive lecture and discussion.
• Extensive exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

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

LangGraph serves as a framework for composing graph-structured workflows involving Large Language Models (LLMs), enabling features such as branching, tool utilization, memory management, and controlled execution.

This instructor-led live training (available online or onsite) is designed for intermediate-level engineers and product teams looking to integrate LangGraph's graph logic with LLM agent loops to create dynamic, context-aware applications, including customer support agents, decision trees, and information retrieval systems.

Upon completion of 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 to ensure robust execution.
• Integrating retrieval processes, APIs, and structured outputs into agent loops.
• Evaluating, monitoring, and enhancing agent behavior to ensure reliability and safety.

Course Format

• Interactive lectures and facilitated discussions.
• Guided labs and code walkthroughs within a sandbox environment.
• Scenario-based design exercises and peer reviews.

Customization Options

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

LangGraph is a graph-based orchestration framework designed to facilitate conditional, multi-step workflows involving large language models (LLMs) and tools, making it an excellent choice for automating and personalizing content pipelines.

This instructor-led live training, available either online or onsite, targets intermediate-level marketers, content strategists, and automation developers who aim to implement dynamic, branching email campaigns and content generation pipelines using LangGraph.

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

• Design graph-structured content and email workflows incorporating conditional logic.
• Integrate LLMs, APIs, and data sources to enable automated personalization.
• Manage state, memory, and context across multi-step campaigns.
• Evaluate, monitor, and optimize workflow performance and delivery outcomes.

Course Format

• Interactive lectures accompanied by group discussions.
• Hands-on labs focused on implementing email workflows and content pipelines.
• Scenario-based exercises covering personalization, segmentation, and branching logic.

Course Customization Options

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

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.