AI on Amazon Web Services (AWS) Training Course

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

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

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

• Design and optimize complex LangGraph topologies for speed, cost-efficiency, and scalability.
• Enhance 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 practical 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 Botswana (onsite 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 Botswana (online or onsite) is aimed 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 Botswana (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 engineered for the creation and execution of coding agents capable of interacting with codebases, developer utilities, and APIs to boost engineering productivity.

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 optimise coding agents using Devstral.

Upon completion 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.
• Extensive exercises and practical sessions.
• Hands-on implementation within a live-lab environment.

Course Customisation Options

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

This instructor-led, live training in Botswana (online or onsite) is designed for advanced-level professionals seeking to deepen their understanding of DevOps practices and streamline development processes using AWS Cloud9.

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

• Setting up and configuring AWS Cloud9 for DevOps workflows.
• Implementing continuous integration and continuous delivery (CI/CD) pipelines.
• Automating testing, monitoring, and deployment processes using AWS Cloud9.
• Integrating AWS services such as Lambda, EC2, and S3 into DevOps workflows.
• Utilising source control systems like GitHub or GitLab within AWS Cloud9.

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

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

• Grasp the core principles of serverless architecture.
• Configure AWS Cloud9 for serverless application development.
• Develop, test, and deploy serverless applications leveraging AWS Lambda.
• Integrate AWS Lambda with other AWS services, such as API Gateway and S3.
• Optimize serverless applications for both performance and cost-efficiency.

This instructor-led live training in Botswana (online or onsite) is aimed at beginner to intermediate researchers and laboratory professionals who wish to process and analyse 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:

• Grasping the architecture and fundamental operations of IoT systems.
• Examining the notions of "Things" and "Sensors," the essence of the Internet of Things, and aligning business processes with IoT solutions.
• A thorough examination of IoT software layers: hardware, firmware, middleware, cloud infrastructure, and mobile applications.
• Core IoT capabilities: fleet management, data visualisation, SaaS-based facility management and data visualisation, alert and alarm systems, sensor and device onboarding, and geo-fencing.
• The basics of device-to-cloud communication via MQTT.
• Linking IoT devices to AWS through MQTT using AWS IoT Core.
• Integrating AWS IoT Core with AWS Lambda for processing and Amazon DynamoDB for data storage.
• Establishing a connection between a Raspberry Pi and AWS IoT Core for seamless data exchange.
• Practical lab session: Creating a smart device using a Raspberry Pi and AWS IoT Core.
• Visualising sensor data and managing web interface communication.

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

This instructor-led, live training—available both online and onsite—is designed for intermediate to advanced professionals aiming to design, implement, and manage LangGraph-based finance solutions with appropriate governance, observability, and compliance standards.

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 targets.

Course Format

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

Customization Options

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

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

This instructor-led, live training (available online or onsite) is tailored for 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 determining when to apply them.
• Constructing prompt chains that branch, invoke tools, and retain memory.
• Integrating retrieval mechanisms and external APIs into graph workflows.
• Testing, debugging, and evaluating LangGraph applications for reliability and safety.

Format of the Course

• Interactive lectures and facilitated discussions.
• Guided labs and code walkthroughs within a sandbox environment.
• Scenario-based exercises focusing 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 capabilities are essential for ensuring compliance, achieving interoperability, and developing decision-support systems that align with clinical workflows.

This instructor-led training, available online or onsite, is designed for intermediate to advanced professionals seeking to design, implement, and manage LangGraph-based healthcare solutions while navigating regulatory, ethical, and operational challenges.

Upon completing this training, participants will be able to:

• Design healthcare-specific LangGraph workflows that prioritise 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 lectures and discussions.
• Hands-on exercises using real-world case studies.
• Implementation practice in a live laboratory environment.

Course Customization Options

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

LangGraph serves as a framework for developing stateful, multi-actor LLM applications through composable graphs that feature 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 equipped with the necessary compliance, traceability, and governance controls.

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

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

Course Format

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

Course Customization Options

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

LangGraph serves as a framework designed for composing graph-structured LLM workflows that facilitate branching, tool utilisation, memory management, and controllable execution.

This instructor-led, live training session (available online or onsite) targets intermediate-level engineers and product teams aiming to merge LangGraph’s graph logic with LLM agent loops to construct dynamic, context-aware applications, such as 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 fallbacks to ensure robust execution.
• Integrate retrieval mechanisms, APIs, and structured outputs into agent loops.
• Evaluate, monitor, and harden agent behaviour to guarantee 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.

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 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.