AI on Amazon Web Services (AWS) Training Course

LangGraph is a framework designed for constructing stateful, multi-actor LLM applications using composable graphs that maintain persistent state and offer control over execution.

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

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

• Design and optimize complex LangGraph topologies for improved speed, cost efficiency, and scalability.
• Ensure reliability through techniques such as retries, timeouts, idempotency, and checkpoint-based recovery.
• Effectively debug and trace graph executions, inspect state, and systematically reproduce issues encountered in production environments.
• Instrument graphs with logs, metrics, and traces, deploy them to production, and monitor SLAs and costs.

Format of the Course

• Interactive lectures and discussions.
• Ample exercises and hands-on practice.
• Practical 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 Uzbekistan (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 Uzbekistan (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 Uzbekistan (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.

This instructor-led, live training in Uzbekistan (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 Uzbekistan (online or onsite) is aimed at 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 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:

• Introduction to the fundamental architecture and functions of the Internet of Things (IoT)
• Understanding the components such as "Things," "Sensors," and their integration with the internet, along with how they map to business functions in IoT
• Overview of essential IoT software components, including hardware, firmware, middleware, cloud services, and mobile applications
• Key IoT functionalities like fleet management, data visualization, Software-as-a-Service (SaaS) based fleet management and data visualization, alerts and alarms, sensor onboarding, device onboarding, and geo-fencing
• Basics of IoT device communication with the cloud using the MQTT protocol
• Connecting IoT devices to AWS using MQTT through AWS IoT Core
• Integrating AWS IoT Core with AWS Lambda for computation and data storage
• Connecting a Raspberry PI to AWS IoT Core and demonstrating simple data communication
• Handling alerts and events in the IoT ecosystem
• Sensor calibration techniques

Summary:

• Introduction to the fundamental architecture and functions of IoT (Internet of Things)
• Understanding the roles of "things," sensors, the internet, and how they align with business functions in IoT
• Overview of all essential IoT software components, including hardware, firmware, middleware, cloud services, and mobile applications
• Key IoT functionalities such as fleet management, data visualization, SaaS-based fleet management and data visualization, alerts and alarms, sensor onboarding, device onboarding, and geo-fencing
• Basics of IoT device communication with the cloud using MQTT (Message Queuing Telemetry Transport)
• Connecting IoT devices to AWS (Amazon Web Services) using MQTT through AWS IoT Core
• Linking AWS IoT Core with AWS Lambda functions for computation and data storage utilizing DynamoDB
• Integrating Raspberry PI with AWS IoT Core for simple data communication
• Practical hands-on experience with Raspberry PI and AWS IoT Core to create a smart device
• Data visualization and communication with a web interface for sensor data

LangGraph is a framework designed for constructing stateful, multi-actor LLM applications through composable graphs that maintain persistent state and provide control over execution.

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

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

• Create finance-specific LangGraph workflows that align with regulatory and audit requirements.
• Incorporate 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 ensure performance, cost efficiency, and service level agreements (SLAs).

Format of the Course

• Interactive lectures and discussions.
• Plenty of exercises and practical sessions.
• 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 is a framework designed for creating graph-structured LLM applications that support planning, branching, tool integration, memory management, and controlled execution.

This instructor-led, live training (available online or on-site) is tailored for beginner-level developers, prompt engineers, and data professionals who aim to design and build reliable, multi-step LLM workflows using LangGraph.

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

• Understand key LangGraph concepts such as nodes, edges, and state, and know when to apply them.
• Create prompt chains that can branch out, call external tools, and maintain a memory state.
• Incorporate retrieval mechanisms and external APIs into graph-based workflows.
• Test, debug, and assess LangGraph applications for reliability and safety.

Format of the Course

• Interactive lectures and facilitated discussions.
• Guided labs and code walkthroughs in 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 features are essential for compliance, interoperability, and developing decision-support systems that align with medical processes.

This instructor-led, live training (conducted online or on-site) is designed for intermediate to advanced professionals who aim to design, implement, and manage LangGraph-based healthcare solutions while addressing regulatory, ethical, and operational challenges.

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

• Create healthcare-specific LangGraph workflows with a focus on compliance and auditability.
• Integrate LangGraph applications with medical ontologies and standards such as FHIR, SNOMED CT, and ICD.
• Implement 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 lectures and discussions.
• Practical exercises with real-world case studies.
• Hands-on implementation practice in a live-lab environment.

Course Customization Options

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

LangGraph is a framework designed for constructing stateful, multi-actor LLM applications through composable graphs that maintain persistent state and offer precise control over execution.

This instructor-led, live training (conducted online or on-site) targets intermediate to advanced professionals who aim to design, implement, and manage 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 ensure auditability and compliance.
• Integrate legal ontologies and document standards into the graph state and processing.
• Implement safeguards, 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 lectures and discussions.
• Numerous exercises and practice sessions.
• 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 is a framework designed to create graph-structured workflows for LLMs, enabling branching, tool usage, memory management, and controllable execution.

This instructor-led, live training (available online or on-site) is targeted at intermediate-level engineers and product teams who want 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 effectively coordinate LLM agents, tools, and memory.
• Implement conditional routing, retries, and fallback mechanisms for robust execution.
• Integrate retrieval processes, APIs, and structured outputs into agent loops.
• Evaluate, monitor, and enhance the reliability and safety of agent behavior.

Format of the Course

• 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 for this course, please contact us to arrange.

LangGraph is a graph-based orchestration framework that facilitates conditional, multi-step workflows involving LLMs and tools, making it ideal for automating and personalizing content pipelines.

This instructor-led, live training (available online or on-site) is designed for intermediate-level marketers, content strategists, and automation developers who want 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 incorporating conditional logic.
• Integrate LLMs, APIs, and data sources for automated personalization.
• Manage state, memory, and context throughout multi-step campaigns.
• Evaluate, monitor, and optimize the performance and delivery outcomes of workflows.

Format of the Course

• Interactive lectures and group discussions.
• Hands-on labs for implementing email workflows and content pipelines.
• Scenario-based exercises focusing on personalization, segmentation, and branching logic.

Course Customization Options

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