AI on Amazon Web Services (AWS) Training Course

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

This instructor-led live training, available both online and onsite, is tailored for advanced AI platform engineers, AI-focused DevOps professionals, and ML architects aiming to optimize, debug, monitor, and operate production-grade LangGraph systems.

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

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

Course Format

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

Course Customization Options

• For those interested in customizing this training to fit specific needs, please contact us to arrange a tailored session.

This instructor-led, live training in Brazil (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 Brazil (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 tools, 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 Site Reliability Engineers (SREs) who aim to design, implement, and optimize coding agents using Devstral.

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

• Setting up and configuring Devstral for coding agent development.
• Designing agentic workflows tailored for codebase exploration and modification.
• Integrating coding agents with various developer tools and APIs.
• Implementing best practices for secure and efficient agent deployment.

Course Format

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

Course Customization Options

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

Devstral and Mistral are open-source AI technologies designed to offer flexible deployment options, fine-tuning capabilities, and scalable integration.

This instructor-led live training, available either online or on-site, targets intermediate to advanced ML engineers, platform teams, and research engineers who want 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.
• 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 arrange it.

This instructor-led, live training in Brazil (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 Brazil (online or onsite) is designed for intermediate-level professionals seeking 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 Brazil (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 an understanding of IoT architecture and its core functionalities.
• Exploring the concepts of "Things" and "Sensors" within the Internet of Things, and aligning business functions with IoT solutions.
• Comprehensive overview of IoT software components: hardware, firmware, middleware, cloud infrastructure, and mobile applications.
• Key IoT functions: Fleet management, data visualization, SaaS-based FM and DV, alerting/alarm systems, sensor and "thing" onboarding, and geo-fencing.
• Fundamentals of IoT 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 Amazon DynamoDB for data storage.
• Connecting a Raspberry Pi to AWS IoT Core for seamless data communication.
• Hands-on lab: Building a smart device using a Raspberry Pi and AWS IoT Core.
• 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 granular control over execution flow.

This instructor-led, live training (available online or on-site) targets intermediate to advanced professionals aiming to design, implement, and manage LangGraph-based financial solutions with robust governance, observability, and compliance.

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

• Design finance-specific LangGraph workflows that align with regulatory and audit standards.
• 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 ensure optimal performance, cost-efficiency, and SLA adherence.

Course Format

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

Customization Options

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

LangGraph is a framework designed for building LLM applications with graph structures, supporting planning, branching, tool usage, 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 want to design and build reliable, multi-step LLM workflows using LangGraph.

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

• Explain core LangGraph concepts (nodes, edges, state) and understand when to apply them.
• Construct prompt chains that branch, invoke tools, and maintain state memory.
• Integrate retrieval mechanisms and external APIs into graph-based workflows.
• Test, debug, and evaluate 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 customized training for this course, please contact us to make arrangements.

LangGraph empowers stateful, multi-actor workflows driven by LLMs, offering precise control over execution paths and state persistence. These capabilities are vital in healthcare for ensuring compliance, interoperability, and developing decision-support systems that align with clinical workflows.

This instructor-led, live training (available online or onsite) is designed for intermediate to advanced professionals seeking 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:

• Design healthcare-specific LangGraph workflows with compliance and auditability in mind.
• Integrate LangGraph applications with medical ontologies and standards (FHIR, SNOMED CT, ICD).
• Apply 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 lecture and discussion.
• Hands-on exercises with real-world case studies.
• 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 creating stateful, multi-actor LLM applications by composing graphs that maintain persistent state and offer precise execution control.

This instructor-led live training, available online or onsite, targets intermediate to advanced professionals seeking to design, implement, and manage LangGraph-based legal solutions with robust compliance, traceability, and governance controls.

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

• Designing legal-specific LangGraph workflows that ensure 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.
• Extensive exercises and practice sessions.
• Hands-on implementation in a live-lab environment.

Customization Options

• For customized training on this topic, please contact us to arrange a session.

LangGraph is a framework designed for composing graph-structured LLM workflows that support branching, tool use, memory, and controllable execution.

This instructor-led, live training (online or onsite) is aimed at intermediate-level engineers and product teams who wish to combine LangGraph’s graph logic with LLM agent loops to build 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 fallbacks for robust execution.
• Integrate retrieval, APIs, and structured outputs into agent loops.
• Evaluate, monitor, and harden agent behavior for reliability and safety.

Format of the Course

• Interactive lecture and facilitated discussion.
• 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 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.