CANN SDK for Computer Vision and NLP Pipelines Training Course
The CANN SDK (Compute Architecture for Neural Networks) offers robust deployment and optimization capabilities for real-time AI applications in computer vision and natural language processing, particularly on Huawei Ascend hardware.
This instructor-led training, available both online and onsite, targets intermediate-level AI professionals seeking to build, deploy, and optimize vision and language models using the CANN SDK for production environments.
Upon completion of this training, participants will be able to:
- Deploy and optimize CV and NLP models using CANN and AscendCL.
- Leverage CANN tools to convert models and integrate them into active pipelines.
- Enhance inference performance for tasks such as detection, classification, and sentiment analysis.
- Construct real-time CV/NLP pipelines suitable for edge or cloud-based deployment scenarios.
Course Format
- Interactive lectures and live demonstrations.
- Practical labs focused on model deployment and performance profiling.
- Live pipeline design utilizing real-world CV and NLP use cases.
Customization Options
- For a customized version of this course, please reach out to us to arrange your training.
Course Outline
Introduction to CV/NLP Deployment with CANN
- The AI model lifecycle from training through to deployment.
- Key performance factors for real-time CV and NLP applications.
- Overview of CANN SDK tools and their role in model integration.
Preparing CV and NLP Models
- Exporting models from PyTorch, TensorFlow, and MindSpore.
- Managing model inputs and outputs for image and text tasks.
- Using ATC to convert models to OM format.
Deploying Inference Pipelines with AscendCL
- Executing CV/NLP inference via the AscendCL API.
- Preprocessing pipelines including image resizing, tokenization, and normalization.
- Post-processing steps involving bounding boxes, classification scores, and text outputs.
Performance Optimization Techniques
- Profiling CV and NLP models using CANN tools.
- Reducing latency through mixed-precision and batch tuning.
- Managing memory and compute resources for streaming tasks.
Computer Vision Use Cases
- Case study: Object detection for smart surveillance.
- Case study: Visual quality inspection in manufacturing.
- Building live video analytics pipelines on Ascend 310.
NLP Use Cases
- Case study: Sentiment analysis and intent detection.
- Case study: Document classification and summarization.
- Real-time NLP integration with REST APIs and messaging systems.
Summary and Next Steps
Requirements
- Understanding of deep learning applied to computer vision or NLP.
- Proficiency in Python and AI frameworks such as TensorFlow, PyTorch, or MindSpore.
- Foundational knowledge of model deployment and inference workflows.
Target Audience
- Computer vision and NLP practitioners utilizing Huawei’s Ascend platform.
- Data scientists and AI engineers creating real-time perception models.
- Developers integrating CANN pipelines for applications in manufacturing, surveillance, or media analytics.
Open Training Courses require 5+ participants.
CANN SDK for Computer Vision and NLP Pipelines Training Course - Booking
CANN SDK for Computer Vision and NLP Pipelines Training Course - Enquiry
NobleProg offers professional training programs designed specifically for companies and organizations. These trainings are not intended for individuals.
CANN SDK for Computer Vision and NLP Pipelines - Consultancy Enquiry
Upcoming Courses
Related Courses
Advanced LangGraph: Optimization, Debugging, and Monitoring Complex Graphs
35 HoursLangGraph serves as a framework for developing stateful, multi-agent LLM applications by composing graphs with persistent state and execution control.
This instructor-led live training, available online or onsite, is designed for advanced AI platform engineers, AI DevOps specialists, and ML architects who aim to optimize, debug, monitor, and manage production-grade LangGraph systems.
Upon completing this training, participants will be able to:
- Design and optimize complex LangGraph topologies to enhance speed, reduce costs, and improve scalability.
- Build reliability through retries, timeouts, idempotency, and checkpoint-based recovery mechanisms.
- Debug and trace graph executions, inspect states, and systematically reproduce production issues.
- Instrument graphs with logs, metrics, and traces; deploy to production; and monitor SLAs and costs.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practice sessions.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request customized training for this course, please contact us to arrange.
Building Coding Agents with Devstral: From Agent Design to Tooling
14 HoursDevstral 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 who aim to design, implement, and optimize coding agents using Devstral.
Upon completion 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.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and hands-on practice.
- Live-lab implementation exercises.
Customization Options
- To request customized training for this course, please contact us to arrange.
Open-Source Model Ops: Self-Hosting, Fine-Tuning and Governance with Devstral & Mistral Models
14 HoursThe Devstral and Mistral models are open-source AI technologies engineered for flexible deployment, fine-tuning capabilities, and scalable integration.
This instructor-led live training, available either online or onsite, targets intermediate to advanced ML engineers, platform teams, and research engineers who aim to self-host, fine-tune, and govern Mistral and Devstral models within production environments.
Upon completing this training, participants will be equipped to:
- Establish and configure self-hosted environments for Mistral and Devstral models.
- Apply fine-tuning techniques to achieve domain-specific performance improvements.
- Implement versioning, monitoring, and lifecycle governance strategies.
- 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.
AI Facial Recognition Development for Law Enforcement
21 HoursThis instructor-led, live training session (available online or onsite) is designed for entry-level law enforcement professionals aiming to shift from manual facial sketching to utilizing AI tools for developing facial recognition systems.
By the conclusion of this training, participants will be able to:
- Understand the core concepts of Artificial Intelligence and Machine Learning.
- Learn the fundamentals of digital image processing and how it applies to facial recognition.
- Develop competencies in using AI tools and frameworks to build facial recognition models.
- Gain practical experience in creating, training, and testing facial recognition systems.
- Comprehend the ethical considerations and best practices associated with facial recognition technology.
Fiji: Introduction to Scientific Image Processing
21 HoursFiji is a powerful open-source image processing package that bundles ImageJ (a program designed for scientific multidimensional images) along with a comprehensive suite of plugins for scientific image analysis.
In this instructor-led, live training, participants will learn how to leverage the Fiji distribution and its underlying ImageJ program to create robust image analysis applications.
By the end of this training, participants will be able to:
- Use Fiji's advanced programming features and software components to extend ImageJ capabilities
- Stitch large 3D images from overlapping tiles
- Automate the update of a Fiji installation on startup using the integrated update system
- Select from a broad selection of scripting languages to build custom image analysis solutions
- Utilize Fiji's powerful libraries, such as ImgLib, to process large bioimage datasets efficiently
- Deploy applications and collaborate effectively with other scientists on similar projects
Format of the Course also allows for the evaluation of participants.
- Interactive lecture and discussion
- Extensive exercises and practical application
- Hands-on implementation in a live-lab environment
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Fiji: Image Processing for Biotechnology and Toxicology
14 HoursThis instructor-led, live training in France (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 Applications in Finance
35 HoursLangGraph serves as a framework designed for constructing stateful, multi-agent Large Language Model (LLM) applications through composable graphs, featuring persistent state and precise execution control.
This instructor-led, live training—available either online or onsite—is tailored for intermediate to advanced professionals seeking to design, implement, and manage LangGraph-based financial solutions that adhere to strict governance, observability, and compliance standards.
Upon completion of this training, participants will be equipped to:
- Design finance-specific LangGraph workflows that align with regulatory and audit requirements.
- Integrate financial data standards and ontologies into graph states and associated tooling.
- Implement reliability, safety, and human-in-the-loop controls essential for critical processes.
- Deploy, monitor, and optimize LangGraph systems to ensure optimal performance, cost-efficiency, and adherence to Service Level Agreements (SLAs).
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical applications.
- Hands-on implementation within a live laboratory environment.
Customization Options
- To request customized training for this course, please contact us to arrange your specific needs.
LangGraph Foundations: Graph-Based LLM Prompting and Chaining
14 HoursLangGraph is a framework designed for creating graph-structured applications powered by large language models (LLMs), supporting features such as planning, branching, tool usage, memory management, and controllable execution.
This instructor-led live training, available either online or onsite, is tailored for beginner-level developers, prompt engineers, and data practitioners who aim to design and implement reliable, multi-step LLM workflows using LangGraph.
Upon completion of this training, participants will be able to:
- Articulate the core concepts of LangGraph (nodes, edges, and state) and understand their appropriate use cases.
- Construct prompt chains that branch, invoke tools, and maintain memory context.
- 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 combined with facilitated discussions.
- Guided laboratory sessions 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 make arrangements.
LangGraph in Healthcare: Workflow Orchestration for Regulated Environments
35 HoursLangGraph facilitates stateful, multi-actor workflows driven by Large Language Models (LLMs), offering precise control over execution paths and state persistence. These capabilities are essential in healthcare for ensuring compliance, enabling 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 looking 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 LangGraph workflows tailored to healthcare, ensuring compliance and auditability.
- Integrate LangGraph applications with medical ontologies and standards such as FHIR, SNOMED CT, and ICD.
- Apply best practices for reliability, traceability, and explainability in sensitive environments.
- Deploy, monitor, and validate LangGraph applications in healthcare production settings.
Course Format
- Interactive lectures and discussions.
- Hands-on exercises using real-world case studies.
- Practical implementation in a live-lab environment.
Customization Options
- To request a customized version of this training, please contact us to arrange.
LangGraph for Legal Applications
35 HoursLangGraph is a framework designed for constructing stateful, multi-actor LLM applications as composable graphs, featuring persistent state and precise execution control.
This instructor-led live training, available both online and onsite, targets intermediate to advanced professionals who aim to design, implement, and manage LangGraph-based legal solutions, ensuring the necessary compliance, traceability, and governance controls are in place.
Upon completion of this training, participants will be able to:
- Design legal-specific LangGraph workflows that maintain auditability and compliance.
- Integrate legal ontologies and document standards into graph state and processing.
- Implement guardrails, human-in-the-loop approvals, and traceable decision paths.
- Deploy, monitor, and maintain LangGraph services in production environments with observability and cost controls.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practice sessions.
- Hands-on implementation within a live laboratory environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
Building Dynamic Workflows with LangGraph and LLM Agents
14 HoursLangGraph is a framework designed for composing graph-structured LLM workflows that support branching, tool use, memory, and controllable execution.
This instructor-led, live training (available online or onsite) targets intermediate-level engineers and product teams who aim 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.
Course Format
- 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 customized training for this course, please contact us to arrange.
LangGraph for Marketing Automation
14 HoursLangGraph serves as a graph-based orchestration framework designed to facilitate conditional, multi-step workflows involving LLMs and tools, making it an excellent choice for automating and personalizing content pipelines.
This instructor-led live training, available either online or on-site, targets intermediate-level marketers, content strategists, and automation developers who aim to implement dynamic, branching email campaigns and content generation pipelines using LangGraph.
Upon completion of this training, participants will be capable of:
- Designing graph-structured content and email workflows that incorporate conditional logic.
- Integrating LLMs, APIs, and data sources to achieve automated personalization.
- Managing state, memory, and context throughout multi-step campaigns.
- Evaluating, monitoring, and optimizing workflow performance and delivery outcomes.
Course Format
- Interactive lectures and group discussions.
- Hands-on labs focused on implementing email workflows and content pipelines.
- Scenario-based exercises covering personalization, segmentation, and branching logic.
Course Customization Options
- To request customized training for this course, please contact us to arrange.
Le Chat Enterprise: Private ChatOps, Integrations & Admin Controls
14 HoursLe Chat Enterprise delivers a private ChatOps solution equipped with secure, customizable, and governed conversational AI capabilities tailored for organizations. It supports RBAC, SSO, connectors, and enterprise app integrations.
\rThis instructor-led, live training (online or onsite) is aimed at intermediate-level product managers, IT leads, solution engineers, and security/compliance teams who wish to deploy, configure, and govern Le Chat Enterprise in enterprise environments.
By the end of this training, participants will be able to:
- Set up and configure Le Chat Enterprise for secure deployments.
- Enable RBAC, SSO, and compliance-driven controls.
- Integrate Le Chat with enterprise applications and data stores.
- Design and implement governance and admin playbooks for ChatOps.
Format of the Course also allows for the evaluation of participants.
- 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.
Python and Deep Learning with OpenCV 4
14 HoursThis instructor-led, live training in France (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.
Vision Builder for Automated Inspection
35 HoursThis instructor-led, live training in France (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.
