Text Summarization with Python Training Course

LangGraph is a framework for building stateful, multi-actor LLM applications as composable graphs with persistent state and control over execution.

This instructor-led, live training (available online or on-site) is designed for advanced-level AI platform engineers, AI-focused DevOps professionals, and ML architects who aim to optimise, debug, monitor, and operate production-grade LangGraph systems.

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

• Design and optimise complex LangGraph topologies for speed, cost-efficiency, and scalability.
• Engineer reliability through retries, timeouts, idempotency, and checkpoint-based recovery.
• Debug and trace graph executions, inspect state, and systematically reproduce production issues.
• Instrument graphs with logs, metrics, and traces, deploy to production, and monitor SLAs and costs.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and hands-on practice.
• Real-world implementation in a live-lab environment.

Course Customisation Options

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

Devstral is an open-source framework designed for building and running coding agents that can interact with codebases, developer tools, and APIs to enhance engineering productivity.

This instructor-led, live training (online or onsite) is aimed at intermediate to advanced-level ML engineers, developer-tooling teams, and SREs who wish to design, implement, and optimise coding agents using Devstral.

By the end of this training, participants will be able 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 lectures and discussions.
• Abundant exercises and practice opportunities.
• Hands-on implementation within a live-lab environment.

Course Customisation Options

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

This instructor-led, live training in New Zealand (delivered online or on-site) is aimed at data scientists and software engineers who wish to leverage Dask within the Python ecosystem to build, scale, and analyse large datasets.

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

• Set up the environment to begin building big data processing solutions 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.
• Optimise the Dask environment to maintain high performance when handling large datasets.

This instructor-led, live training in New Zealand (online or on-site) is designed for intermediate-level Python developers and data analysts seeking 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 including 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 analyse time series data.
• Visualise data using Matplotlib and other visualisation libraries.
• Debug and optimise 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 on-site) is aimed at intermediate–level 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.

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

• Set up and configure self-hosted environments for Mistral and Devstral models.
• Apply fine-tuning techniques for domain-specific performance.
• Implement versioning, monitoring, and lifecycle governance.
• Ensure security, compliance, and responsible usage of open-source models.

Course Format

• Interactive lecture and discussion.
• Hands-on exercises in self-hosting and fine-tuning.
• Live-lab implementation of governance and monitoring pipelines.

Course Customisation Options

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

This instructor-led, live training (available online or on-site) is designed for developers eager to harness the FARM (FastAPI, React, and MongoDB) stack to build dynamic, high-performance, and scalable web applications.

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

• Establish the essential development environment integrating FastAPI, React, and MongoDB.
• Grasp the core concepts, features, and advantages of the FARM stack.
• Learn how to construct REST APIs using FastAPI.
• Master the art of designing interactive applications with React.
• Develop, test, and deploy both front-end and back-end applications using the FARM stack.

This instructor-led, live training in New Zealand (online or on-site) is designed for developers who wish to leverage 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 build APIs using Python and FastAPI.
• Create APIs more quickly and easily using the FastAPI library.
• Learn how to define data models and schemas based on Pydantic and OpenAPI.
• Connect APIs to a database using SQLAlchemy.
• Implement security and authentication mechanisms in APIs using FastAPI tools.
• Build container images and deploy web APIs to a cloud server.

LangGraph is a framework for building stateful, multi-actor LLM applications as composable graphs with persistent state and control over execution.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish to design, implement, and operate LangGraph-based finance solutions with proper governance, observability, and compliance.

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

• Design finance-specific LangGraph workflows aligned to 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 optimise LangGraph systems for performance, cost, and SLAs.

Format of the Course

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

Course Customisation Options

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

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

This instructor-led, live training (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.

Course Format

• Interactive lecture and facilitated discussion.
• Guided labs and code walkthroughs in a sandbox environment.
• Scenario-based exercises on design, testing, and evaluation.

Course Customisation Options

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

LangGraph enables stateful, multi-actor workflows powered by large language models (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 seamlessly with clinical workflows.

This instructor-led, live training (available online or on-site) is designed for intermediate to advanced-level professionals who wish to design, implement, and manage LangGraph-based healthcare solutions while navigating 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 as core considerations.
• Integrate LangGraph applications with medical ontologies and standards such as FHIR, SNOMED CT, and ICD.
• Apply best practices for reliability, traceability, and explainability within sensitive healthcare environments.
• Deploy, monitor, and validate LangGraph applications in real-world healthcare production settings.

Course Format

• Interactive lectures and group discussions.
• Hands-on exercises featuring real-world case studies.
• Practical implementation in a live-lab environment.

Course Customisation Options

• To request a customised version of this training, please contact us to arrange a tailored session.

LangGraph is a framework for building stateful, multi-actor LLM applications as composable graphs with persistent state and precise control over execution.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish 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 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.

Format of the Course

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

Course Customisation Options

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

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

This instructor-led, live training (online or on-site) is aimed at 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 behaviour 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 Customisation Options

• To request customised 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 personalising content pipelines.

This instructor-led, live training (online or on-site) 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 personalisation.
• Manage state, memory, and context across multi-step campaigns.
• Evaluate, monitor, and optimise 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 personalisation, segmentation, and branching logic.

Course Customisation Options

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

Le Chat Enterprise is a private ChatOps solution delivering secure, customisable, and governed conversational AI capabilities for organisations, with support for RBAC, SSO, connectors, and enterprise application integrations.

This instructor-led, live training (available online or on-site) is designed for intermediate-level product managers, IT leads, solution engineers, and security and compliance teams who wish to deploy, configure, and govern Le Chat Enterprise within 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.

Course Format

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

Course Customisation Options

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

This instructor-led, live training in New Zealand (available online or on-site) is tailored for data scientists and developers who wish to utilise Modin to build and implement parallel computations with Pandas, enabling faster data analysis.

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

• Set up the necessary environment to begin developing scalable Pandas workflows with Modin.
• Understand the features, architecture, and advantages of Modin.
• Identify the key differences between Modin, Dask, and Ray.
• Execute Pandas operations more efficiently using Modin.
• Implement the full Pandas API and its associated functions.