CANN SDK for Computer Vision and NLP Pipelines 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 (online or onsite) is aimed at beginner-level law enforcement personnel who wish to transition from manual facial sketching to using AI tools for developing facial recognition systems.

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

• Understand the fundamentals of Artificial Intelligence and Machine Learning.
• Learn the basics of digital image processing and its application in facial recognition.
• Develop skills in using AI tools and frameworks to create facial recognition models.
• Gain hands-on experience in creating, training, and testing facial recognition systems.
• Understand ethical considerations and best practices in the use of facial recognition technology.

Fiji is an open-source image processing package that combines ImageJ (an image processing program designed for scientific multidimensional images) with various plugins for advanced image analysis.

In this instructor-led, live training, participants will learn how to utilize the Fiji distribution and its underlying ImageJ program to develop an image analysis application.

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

• Utilize Fiji's advanced programming features and software components to extend the capabilities of ImageJ
• Stitch together large 3D images from overlapping tiles
• Automatically update their Fiji installation at startup using the integrated update system
• Choose from a wide range of scripting languages to create custom image analysis solutions
• Leverage Fiji's powerful libraries, such as ImgLib, for processing large bioimage datasets
• Deploy their application and collaborate with other scientists on similar projects

Format of the Course

• Interactive lecture and discussion.
• Extensive 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.

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.

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.

OpenCV (Open Source Computer Vision Library: http://opencv.org) is an open-source library licensed under the BSD license, which contains several hundred algorithms for computer vision.

Audience

This course is designed for engineers and architects who are looking to leverage OpenCV for their computer vision projects.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at software engineers who wish to program in Python with OpenCV 4 for deep learning.

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

• View, load, and classify images and videos using OpenCV 4.
• Implement deep learning in OpenCV 4 with TensorFlow and Keras.
• Run deep learning models and generate impactful reports from images and videos.

Pattern Matching is a technique employed to identify specific patterns within an image. It helps determine whether certain characteristics are present in a captured image, such as the correct label on a defective product on a factory line or the precise dimensions of a component. Unlike "Pattern Recognition," which identifies general patterns based on large collections of related samples, Pattern Matching specifies what we are looking for and confirms whether the expected pattern is present.

Format of the Course

• This course provides an introduction to the approaches, technologies, and algorithms used in the field of pattern matching as it pertains to Machine Vision.

This instructor-led, live training in Uzbekistan (online or onsite) is aimed at intermediate-level professionals who wish to use Vision Builder AI to design, implement, and optimize automated inspection systems for SMT (Surface-Mount Technology) processes.

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

• Set up and configure automated inspections using Vision Builder AI.
• Acquire and preprocess high-quality images for analysis.
• Implement logic-based decisions for defect detection and process validation.
• Generate inspection reports and optimize system performance.