AI on Amazon Web Services (AWS) Training Course

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

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

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

• Design and optimize complex LangGraph topologies to enhance speed, reduce costs, and ensure scalability.
• Engineer system reliability through retries, timeouts, idempotency, and checkpoint-based recovery mechanisms.
• Debug and trace graph executions, inspect state, and systematically reproduce production issues.
• Instrument graphs with logs, metrics, and traces, deploy them to production environments, and monitor SLAs and operational costs.

Course Format

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

Course Customization Options

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

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

Upon completion 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 Uzbekistan (online or on-site) is intended for developers who aim to install, configure, and manage AWS IoT Greengrass capabilities to build applications for diverse devices.

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

This instructor-led, live training Uzbekistan (onsite or remote) is designed for developers who wish to use AWS Lambda to build and deploy services and applications to the cloud, without worrying about provisioning the execution environment (servers, VMs, and containers, availability, scalability, storage, etc.).

By the conclusion 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 purpose-built for creating and operating 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 ML engineers, developer-tooling teams, and SREs aiming to design, implement, and optimize coding agents using Devstral.

Upon completing this training, participants will be able to:

• Configure and set up 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 sessions.
• Hands-on implementation within a live lab environment.

Customization Options

• To arrange a tailored version of this course, please contact us.

This instructor-led, live training in Uzbekistan (online or on-site) is designed for 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 Uzbekistan (online or on-site) 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.

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

• Understand the core principles of serverless architecture.
• Configure 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 improved performance and cost efficiency.

This instructor-led, live training in Uzbekistan (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:

• Fundamentals of IoT architecture and functionality
• Understanding devices, sensors, internet connectivity, and their alignment with business functions
• Core components of IoT software: hardware, firmware, middleware, cloud, and mobile applications
• Key IoT services: fleet management, data visualization, SaaS-based FM/DV, alerting, sensor and device onboarding, and geo-fencing
• Basics of device-to-cloud communication using MQTT
• Connecting IoT devices to AWS via MQTT using AWS IoT Core
• Integrating AWS IoT Core with AWS Lambda for computation and DynamoDB for data storage
• Linking Raspberry Pi with AWS IoT Core for basic data exchange
• Practical implementation with Raspberry Pi and AWS IoT Core to create a smart device
• Sensor data visualization and web interface communication

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

This instructor-led, live training session (available online or onsite) targets intermediate to advanced professionals who aim to design, implement, and manage LangGraph-based financial solutions with appropriate 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 optimize LangGraph systems to enhance performance, reduce costs, and meet SLAs.

Course Format

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

Course Customization Options

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

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

This instructor-led, live training, available online or onsite, targets beginner-level developers, prompt engineers, and data practitioners aiming to design and build reliable, multi-step LLM workflows using LangGraph.

Upon completing this training, participants will be able to:

• Explain core LangGraph concepts (nodes, edges, state) and their appropriate use cases.
• Create prompt chains that branch, invoke tools, and maintain memory.
• Integrate retrieval mechanisms and external APIs into graph workflows.
• Test, debug, and evaluate LangGraph applications for reliability and safety.

Course Format

• Interactive lectures and guided discussions.
• Hands-on labs and code walkthroughs in a sandbox environment.
• Scenario-based exercises focused on design, testing, and evaluation.

Customization Options

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

LangGraph enables 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 align with established medical workflows.

This instructor-led, live training (available online or on-site) is designed for intermediate to advanced-level 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 able to:

• Design healthcare-specific LangGraph workflows with compliance and auditability as core priorities.
• Integrate LangGraph applications with medical ontologies and standards such as FHIR, SNOMED CT, and ICD.
• Apply best practices to ensure reliability, traceability, and explainability within sensitive healthcare environments.
• Deploy, monitor, and validate LangGraph applications in production healthcare settings.

Course Format

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

Course Customization Options

• To request a customized version of this training, please contact us to arrange.

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

This instructor-led, live training (available online or on-site) is tailored for intermediate to advanced professionals aiming to design, implement, and operate LangGraph-based legal solutions, ensuring robust compliance, traceability, and governance controls.

Upon completing this training, participants will be able to:

• Design legal-specific LangGraph workflows that maintain auditability and regulatory compliance.
• Integrate legal ontologies and document standards into graph state and processing logic.
• Implement guardrails, human-in-the-loop approvals, and fully traceable decision paths.
• Deploy, monitor, and maintain LangGraph services in production environments with effective observability and cost management.

Course Format

• Interactive lectures and group discussions.
• Extensive hands-on exercises and practical sessions.
• Real-time implementation within a live-lab environment.

Course Customisation Options

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

LangGraph is a framework designed for creating graph-structured LLM workflows that support branching, tool usage, memory management, and controlled execution.

This instructor-led, live training (available online or on-site) is tailored for intermediate-level engineers and product teams who aim to integrate LangGraph's graph logic with LLM agent loops to develop 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 fallback mechanisms for robust execution.
• Integrate retrieval systems, APIs, and structured outputs into agent loops.
• Evaluate, monitor, and strengthen agent behavior to ensure reliability and safety.

Course Format

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

Course Customization Options

• To request a customized training session 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.