AI on Amazon Web Services (AWS) Training Course

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

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

Upon completing this training, participants will be equipped to:

• Design and optimize complex LangGraph topologies to enhance speed, reduce costs, and improve scalability.
• Ensure system reliability through the implementation of retries, timeouts, idempotency, and checkpoint-based recovery mechanisms.
• Debug and trace graph executions, inspect states, and systematically reproduce issues encountered in production environments.
• Instrument graphs with logs, metrics, and traces, deploy them to production, and actively monitor SLAs and associated costs.

Course Format

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

Customization Options

• For inquiries regarding customized training for this course, please contact us to make arrangements.

This instructor-led, live training in Kenya (onsite or remote) is designed for engineers looking to deploy and manage IoT devices on AWS.

Upon completion of this training, participants will be equipped to construct an IoT platform that encompasses the deployment and management of a backend, gateway, and devices within the AWS ecosystem.

This instructor-led, live training in Kenya (online or onsite) is designed for 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 Kenya (onsite or remote) targets 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 to build and operate coding agents capable of interacting with codebases, developer utilities, and APIs to boost engineering productivity.

This instructor-led live training, available online or onsite, targets intermediate to advanced ML engineers, developer-tooling teams, and SREs who aim to design, implement, and optimize 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 exploring and modifying codebases.
• 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 application.
• Hands-on implementation within a live laboratory environment.

Course Customization Options

• For customized training requests, please contact us to arrange.

This instructor-led, live training in Kenya (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 Kenya (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 session in Kenya (online or onsite) is designed for beginner to intermediate 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 a clear understanding of IoT architecture and its core operational functions.
• Exploring the concepts of "Things" and "Sensors," the Internet of Things ecosystem, and how to map business requirements to effective IoT solutions.
• Reviewing the comprehensive IoT software stack: hardware, firmware, middleware, cloud infrastructure, and mobile applications.
• Examining critical IoT functions such as fleet management, data visualization, SaaS-based Facility Management and Data Visualization, alerting systems, sensor and "thing" onboarding, and geo-fencing.
• Mastering the fundamentals of IoT device-to-cloud communication via MQTT.
• Connecting IoT devices to AWS infrastructure using MQTT and AWS IoT Core.
• Integrating AWS IoT Core with AWS Lambda for computational tasks and Amazon DynamoDB for robust data storage.
• Establishing seamless data communication between a Raspberry Pi and AWS IoT Core.
• Engaging in hands-on lab exercises to construct a smart device utilizing Raspberry Pi and AWS IoT Core.
• Visualizing sensor data and facilitating communication through web interfaces.

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

This instructor-led live training, available either online or on-site, targets intermediate to advanced professionals looking 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 states and associated tooling.
• Implement reliability, safety, and human-in-the-loop controls for critical processes.
• Deploy, monitor, and optimize LangGraph systems to ensure optimal performance, cost-efficiency, and adherence to SLAs.

Course Format

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

Customization Options

• For customized training requests for this course, please contact us to arrange details.

LangGraph is a framework designed for constructing graph-structured LLM applications that facilitate planning, branching, tool use, 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 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 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, enhancing interoperability, and developing decision-support systems that align with clinical workflows.

This instructor-led training, available both online and onsite, targets intermediate to advanced professionals seeking to design, implement, and manage LangGraph-based healthcare solutions while addressing regulatory, ethical, and operational challenges.

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

• Design healthcare-specific LangGraph workflows with a focus on compliance and auditability.
• Integrate LangGraph applications with medical ontologies and standards, including 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.

Course Format

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

Course Customization Options

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

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.
• 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.

LangGraph serves as a framework for assembling graph-structured Large Language Model (LLM) workflows that enable branching, tool utilization, memory management, and controlled 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 develop dynamic, context-aware applications. Examples include customer support agents, decision trees, and information retrieval systems.

Upon completing this training, participants will be able 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 systems, APIs, and structured outputs into agent loops.
• Evaluate, monitor, and strengthen agent behavior 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 Customization Options

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

LangGraph serves as a graph-based orchestration framework designed to facilitate conditional, multi-step workflows involving Large Language Models (LLMs) and tools, making it ideal for automating and personalizing content pipelines.

This instructor-led live training, available both online and onsite, targets intermediate-level marketers, content strategists, and automation developers. The course focuses on implementing dynamic, branching email campaigns and content generation pipelines using LangGraph.

Upon completing this training, participants will be equipped to:

• Create graph-structured workflows for content and email, incorporating conditional logic.
• Seamlessly integrate LLMs, APIs, and data sources to enable automated personalization.
• Effectively manage state, memory, and context throughout multi-step campaigns.
• Evaluate, monitor, and optimize the performance and delivery outcomes of their workflows.

Course Format

• Engaging interactive lectures coupled with group discussions.
• Practical hands-on labs focused on implementing email workflows and content pipelines.
• Scenario-based exercises covering personalization, segmentation, and branching logic.

Course Customization Options

• For those interested in tailored training for this course, please get in touch with us to make arrangements.