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 execution control.

This instructor-led live training, available online or 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.

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

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

Format of the Course

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

Course Customization Options

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

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

Upon completion of this training, participants will be equipped to construct an IoT platform encompassing the deployment and management of a backend, gateway, and devices on the AWS infrastructure.

This instructor-led, live training in Bulgaria (onsite or remote) is designed for developers who want 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, 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 various 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.

Devstral and Mistral are open-source AI technologies engineered for flexible deployment, fine-tuning, and scalable integration.

This instructor-led live training, available online or onsite, targets intermediate to advanced ML engineers, platform teams, and research engineers seeking to self-host, fine-tune, and govern Mistral and Devstral models within production environments.

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

• Set up and configure self-hosted environments for Mistral and Devstral models.
• Apply fine-tuning techniques to enhance domain-specific performance.
• Implement versioning, monitoring, and lifecycle governance mechanisms.
• Ensure security, compliance, and responsible usage of open-source models.

Course Format

• Interactive lectures and discussions.
• Hands-on exercises focused on self-hosting and fine-tuning.
• Live-lab implementation of governance and monitoring pipelines.

Customization Options

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

This instructor-led, live training in Bulgaria (online or onsite) targets advanced 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 Bulgaria (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.

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 Bulgaria (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 a clear understanding of IoT architecture and its essential functions.
• Exploring the concepts of connected "Things" and sensors, understanding how the Internet of Things operates, and aligning business needs with appropriate IoT solutions.
• Receiving a comprehensive breakdown of IoT software layers, including hardware, firmware, middleware, cloud infrastructure, and mobile interfaces.
• Examining critical IoT capabilities such as fleet management, data visualization, SaaS-based facility and data visualization, alert mechanisms, device onboarding, and geo-fencing.
• Mastering the fundamentals of device-to-cloud communication using the MQTT protocol.
• Learning how to connect IoT devices to AWS via MQTT, utilizing AWS IoT Core.
• Integrating AWS IoT Core with AWS Lambda for computational tasks and Amazon DynamoDB for efficient data storage.
• Establishing connectivity between a Raspberry Pi and AWS IoT Core to enable seamless data exchange.
• Participating in a practical lab session: constructing a smart device using a Raspberry Pi paired with AWS IoT Core.
• Visualizing sensor data and managing communication through a web interface.

LangGraph serves as a framework for constructing stateful, multi-agent LLM applications that operate as composable graphs with persistent state and precise control over execution.

This instructor-led, live training (available online or onsite) targets intermediate to advanced professionals aiming to design, implement, and manage LangGraph-based finance solutions that adhere to proper 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 states and tooling.
• Implementing reliability, safety, and human-in-the-loop controls for critical processes.
• Deploying, monitoring, and optimizing LangGraph systems to ensure performance, cost efficiency, and SLA compliance.

Course Format

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

Course Customization Options

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

LangGraph is a framework designed for constructing graph-structured Large Language Model (LLM) applications that facilitate planning, branching, tool integration, memory management, and controllable 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 build reliable, multi-step LLM workflows using LangGraph.

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

• Explain core LangGraph concepts, including nodes, edges, and state, and understand when to apply them.
• Create prompt chains that support branching, tool invocation, and memory retention.
• Integrate retrieval mechanisms and external APIs into graph-based workflows.
• Test, debug, and evaluate LangGraph applications to ensure reliability and safety.

Course Format

• Interactive lectures accompanied by facilitated discussions.
• Guided labs and code walkthroughs conducted in a sandbox environment.
• Scenario-based exercises focusing on design, testing, and evaluation.

Course Customization Options

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

LangGraph facilitates 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, enabling interoperability, and developing decision-support systems that seamlessly align with clinical workflows.

This instructor-led live training (available online or onsite) targets intermediate to advanced professionals aiming to design, implement, and manage LangGraph-based healthcare solutions while navigating 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 key considerations.
• 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 make arrangements.

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

This instructor-led training, available either online or on-site, targets intermediate to advanced professionals aiming to design, implement, and manage LangGraph-based legal solutions while ensuring necessary compliance, traceability, and governance controls.

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

• Create legal-specific LangGraph workflows that maintain auditability and compliance.
• Incorporate legal ontologies and document standards into graph states and processing.
• Implement guardrails, human-in-the-loop approvals, and traceable decision pathways.
• Deploy, monitor, and maintain LangGraph services in production environments with effective observability and cost management.

Course Format

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

Customization Options

• For tailored training on this subject, please contact us to arrange your specific requirements.

LangGraph serves as a framework for assembling graph-structured LLM workflows that facilitate branching, tool utilization, memory management, and controlled execution.

This instructor-led, live training (available online or onsite) targets intermediate engineers and product teams aiming to merge LangGraph’s graph logic with LLM agent loops to create dynamic, context-aware applications, such as customer support agents, decision trees, and information retrieval systems.

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

• Designing graph-based workflows that coordinate LLM agents, tools, and memory.
• Implementing conditional routing, retries, and fallback mechanisms for robust execution.
• Integrating retrieval, APIs, and structured outputs into agent loops.
• Evaluating, monitoring, and enhancing agent behavior to ensure reliability and safety.

Course Format

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

Customization Options

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

LangGraph is a graph-based orchestration framework designed to facilitate conditional, multi-step workflows involving Large Language Models (LLMs) and various tools. It is particularly well-suited 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 looking to build dynamic, branching email campaigns and content generation pipelines using LangGraph.

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

• Create graph-structured workflows for emails and content that incorporate conditional logic.
• Connect LLMs, APIs, and data sources to enable automated personalization.
• Manage state, memory, and context throughout multi-step campaigns.
• Assess, monitor, and optimize the performance and delivery results of workflows.

Course Format

• Interactive lectures accompanied by group discussions.
• Practical labs focused on implementing email workflows and content pipelines.
• Scenario-based exercises covering personalization, segmentation, and branching logic.

Course Customization Options

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