Bespoke Applied Artificial Intelligence and LLM Engineering with Python Training Course

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

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

Upon completing this training, participants will be capable of:

• Designing and optimizing complex LangGraph topologies to enhance speed, reduce costs, and improve scalability.
• Building reliability through retries, timeouts, idempotency, and checkpoint-based recovery mechanisms.
• Debugging and tracing graph executions, inspecting states, and systematically reproducing production issues.
• Instrumenting graphs with logs, metrics, and traces; deploying to production; and monitoring SLAs and costs.

Course Format

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

Customization Options

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

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, available online or onsite, targets intermediate to advanced-level ML engineers, developer-tooling teams, and SREs keen on designing, implementing, and optimizing coding agents using Devstral.

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

• Set up and configure Devstral for coding agent development.
• Design agentic workflows for codebase exploration and modification.
• Integrate coding agents with developer tools and APIs.
• Implement best practices for secure and efficient agent deployment.

Course Format

• Interactive lecture and discussion.
• Ample exercises and practice opportunities.
• Hands-on implementation within a live-lab environment.

Customization Options

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

This instructor-led, live training in Malaysia (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 Malaysia (online or onsite) is designed for 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 engineered for flexible deployment, fine-tuning capabilities, and scalable integration.

This instructor-led live training, available both online and onsite, targets intermediate to advanced machine learning 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:

• Establish and configure self-hosted environments for Mistral and Devstral models.
• Apply fine-tuning techniques to enhance performance for specific domains.
• Implement versioning, monitoring, and lifecycle governance protocols.
• Ensure security, regulatory compliance, and responsible usage of open-source models.

Course Format

• Interactive lectures and discussions.
• Practical exercises focused on self-hosting and fine-tuning.
• Live laboratory sessions for implementing governance and monitoring pipelines.

Customization Options

• To arrange a customized version of this course, please get in touch with us.

This instructor-led live training, available 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 Malaysia (online or on-site) is aimed at developers who wish to use FastAPI with Python to build, test, and deploy RESTful APIs more efficiently and rapidly.

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.

This instructor-led, live training in Malaysia (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 designed for constructing stateful, multi-actor LLM applications through composable graphs that maintain persistent state and offer precise control over execution.

This instructor-led live training, available either online or on-site, targets intermediate to advanced professionals seeking to design, implement, and manage LangGraph-based financial solutions with robust governance, observability, and compliance measures.

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

• Design finance-specific LangGraph workflows that align with regulatory and audit requirements.
• 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 meet performance, cost, and SLA objectives.

Format of the Course

• Interactive lecture and discussion.
• Extensive 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.

LangGraph serves as a framework for constructing graph-structured LLM applications that facilitate planning, branching, tool utilization, memory management, and controllable execution.

This instructor-led, live training (available online or onsite) is designed for beginner-level developers, prompt engineers, and data practitioners aiming 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 empowers stateful, multi-actor workflows driven by Large Language Models (LLMs), offering precise control over execution paths and state persistence. In the healthcare sector, these capabilities are essential for ensuring compliance, interoperability, and the development of decision-support systems that seamlessly align with medical workflows.

This instructor-led live training session is available both online and onsite. It is designed for intermediate to advanced-level professionals aiming to design, implement, and manage LangGraph-based healthcare solutions while effectively addressing 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 such as FHIR, SNOMED CT, and ICD.
• Apply best practices for reliability, traceability, and explainability in sensitive environments.
• Deploy, monitor, and validate LangGraph applications within 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 serves as a framework designed for constructing stateful, multi-actor LLM applications through composable 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 operate LangGraph-based legal solutions equipped with necessary compliance, traceability, and governance controls.

Upon completion 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 workflows.
• Implement guardrails, human-in-the-loop approvals, and traceable decision paths.
• Deploy, monitor, and maintain LangGraph services in production environments with robust observability and cost controls.

Course Format

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

Customization Options

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

LangGraph serves as a framework designed for composing graph-structured workflows with LLMs, enabling features such as branching, tool utilization, memory management, and controllable execution.

This instructor-led live training, available online or onsite, targets intermediate engineers and product teams aiming to merge LangGraph’s graph logic with LLM agent loops to develop dynamic, context-aware applications. Examples include 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, retries, and fallback mechanisms to ensure robust execution.
• Integrate retrieval processes, APIs, and structured outputs into agent loops.
• Evaluate, monitor, and harden agent behavior to ensure reliability and safety.

Course Format

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

Customization Options

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

LangGraph is a graph-based orchestration framework designed to enable conditional, multi-step workflows involving LLMs and tools, making it ideal for automating and personalizing content pipelines.

This instructor-led live training, available online or onsite, targets intermediate-level marketers, content strategists, and automation developers seeking 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 facilitate automated personalization.
• Manage state, memory, and context throughout multi-step campaigns.
• Evaluate, monitor, and optimize workflow performance and delivery outcomes.

Course Format

• Interactive lectures and 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 arrange a session.

This instructor-led, live training in Malaysia (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.