AI on Amazon Web Services (AWS) Training Course

LangGraph is a framework designed for constructing stateful, multi-actor Large Language Model (LLM) applications as composable graphs, featuring persistent state and granular control over execution.

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

By the conclusion of this training, participants will be capable of:

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

Course Format

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

Course Customisation Options

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

This instructor-led, live training in South Africa (on-site or remote) is aimed at engineers who wish to deploy and manage IoT devices on AWS.

By the end of this training, participants will be able to build an IoT platform that includes the deployment and management of a backend, gateway, and devices on top of AWS.

This instructor-led, live training in South Africa (online or onsite) is targeted at developers who wish to install, configure, and manage AWS IoT Greengrass capabilities to create applications for various devices.

By the end of this training, participants will be able to use AWS IoT Greengrass to build, deploy, manage, secure, and monitor applications on intelligent devices.

This instructor-led, live training in South Africa (onsite or remote) is aimed at developers who wish to use AWS Lambda to build and deploy services and applications to the cloud, without needing to worry about provisioning the execution environment (servers, VMs and containers, availability, scalability, storage, etc.).

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

• Configure AWS Lambda to execute a function.
• Understand FaaS (Functions as a Service) and the advantages of serverless development.
• Build, upload and execute AWS Lambda functions.
• Integrate Lambda functions with different event sources.
• Package, deploy, monitor and troubleshoot Lambda based applications.

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-level to advanced-level ML engineers, developer-tooling teams, and SREs who wish 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 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.

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 South Africa (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of DevOps practices and streamline development processes using AWS Cloud9.

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

• Set up and configure AWS Cloud9 for DevOps workflows.
• Implement continuous integration and continuous delivery (CI/CD) pipelines.
• Automate testing, monitoring, and deployment processes using AWS Cloud9.
• Integrate AWS services such as Lambda, EC2, and S3 into DevOps workflows.
• Utilize source control systems like GitHub or GitLab within AWS Cloud9.

This instructor-led, live training in South Africa (online or onsite) targets intermediate-level professionals who wish to learn how to effectively build, deploy, and maintain serverless applications on AWS Cloud9 and AWS Lambda.

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

• Understand the fundamentals of serverless architecture.
• Set up AWS Cloud9 for serverless application development.
• Develop, test, and deploy serverless applications using AWS Lambda.
• Integrate AWS Lambda with other AWS services such as API Gateway and S3.
• Optimize serverless applications for performance and cost efficiency.

This instructor-led, live training in South Africa (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.

Summary:

• Gaining an understanding of IoT architecture and its fundamental functions.
• Examining the notions of 'Things' and 'Sensors', the broader context of the Internet of Things, and aligning business processes with IoT solutions.
• Providing a thorough overview of IoT software components, encompassing hardware, firmware, middleware, cloud infrastructure, and mobile applications.
• Highlighting key IoT functionalities such as fleet management, data visualization, Software-as-a-Service (SaaS) for fleet and data visualization, alerting and alarm systems, sensor and device onboarding, and geo-fencing.
• Exploring the basics of IoT device-to-cloud communication via MQTT.
• Connecting IoT devices to AWS using MQTT through AWS IoT Core.
• Integrating AWS IoT Core with AWS Lambda for computational tasks and Amazon DynamoDB for data storage.
• Linking a Raspberry Pi to AWS IoT Core to enable seamless data exchange.
• Participating in a hands-on laboratory session: constructing a smart device utilizing a Raspberry Pi and AWS IoT Core.
• Visualizing sensor data and managing communication through web interfaces.

LangGraph is a framework designed for building stateful, multi-actor LLM applications by composing them into graphs with persistent state and precise control over execution.

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

Upon completing this training, participants will be able 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 optimise LangGraph systems for performance, cost, and SLAs.

Course Format

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

Customisation Options

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

LangGraph is a framework designed for constructing graph-structured Large Language Model (LLM) applications that facilitate planning, branching, tool usage, memory management, and controllable execution.

This instructor-led, live training session (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 capable of:

• Explaining core LangGraph concepts (nodes, edges, state) and understanding when to apply them.
• Constructing prompt chains that branch, invoke tools, and maintain memory.
• Integrating retrieval mechanisms and external APIs into graph workflows.
• Testing, debugging, and evaluating LangGraph applications to ensure reliability and safety.

Format of the Course

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

Course Customization Options

• To request a customized training programme 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 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) is designed for intermediate to advanced professionals who aim to design, implement, and manage LangGraph-based healthcare solutions while navigating regulatory, ethical, and operational challenges.

Upon completing this training, participants will be equipped 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.

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 Large Language Model (LLM) applications. It utilizes composable graphs that feature persistent state and allow for precise control over execution processes.

This instructor-led live training, available either online or onsite, is tailored for intermediate to advanced professionals who aim to design, implement, and manage LangGraph-based legal solutions. The course emphasizes the importance of compliance, traceability, and governance controls.

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

• Designing legal-specific LangGraph workflows that maintain auditability and ensure compliance.
• Integrating legal ontologies and document standards into graph state and processing systems.
• Implementing guardrails, facilitating human-in-the-loop approvals, and establishing traceable decision paths.
• Deploying, monitoring, and maintaining LangGraph services in production environments with robust observability and cost controls.

Format of the Course

• Interactive lecture and discussion.
• Extensive exercises and practical practice.
• Hands-on implementation within 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 composing graph-structured LLM workflows that support branching, tool use, memory, and controllable execution.

This instructor-led, live training (online or onsite) 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 a 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 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.