CANN SDK for Computer Vision and NLP Pipelines Training Course
The CANN SDK (Compute Architecture for Neural Networks) offers robust deployment and optimization tools for real-time AI applications in computer vision and NLP, particularly on Huawei Ascend hardware.
This instructor-led live training (available online or onsite) targets intermediate-level AI practitioners looking to build, deploy, and optimize vision and language models using the CANN SDK for production environments.
Upon completing 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 live pipelines.
- Enhance inference performance for tasks such as detection, classification, and sentiment analysis.
- Develop real-time CV/NLP pipelines for both edge and cloud-based deployment scenarios.
Course Format
- Interactive lectures and demonstrations.
- Hands-on lab sessions covering model deployment and performance profiling.
- Live pipeline design using real-world CV and NLP use cases.
Customization Options
- To request customized training for this course, please contact us to make arrangements.
Course Outline
Introduction to CV/NLP Deployment with CANN
- AI model lifecycle from training to deployment
- Key performance considerations for real-time CV and NLP
- Overview of CANN SDK tools and their role in model integration
Preparing CV and NLP Models
- Exporting models from PyTorch, TensorFlow, and MindSpore
- Handling model inputs/outputs for image and text tasks
- Using ATC to convert models to OM format
Deploying Inference Pipelines with AscendCL
- Running CV/NLP inference using the AscendCL API
- Preprocessing pipelines: image resizing, tokenization, normalization
- Postprocessing: bounding boxes, classification scores, text output
Performance Optimization Techniques
- Profiling CV and NLP models using CANN tools
- Reducing latency with mixed-precision and batch tuning
- Managing memory and compute 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
- Familiarity with deep learning for computer vision or NLP
- Experience with Python and AI frameworks such as TensorFlow, PyTorch, or MindSpore
- Basic understanding of model deployment or inference workflows
Audience
- Computer vision and NLP practitioners using Huawei’s Ascend platform
- Data scientists and AI engineers developing real-time perception models
- Developers integrating CANN pipelines in manufacturing, surveillance, or media analytics
Need help picking the right course?
southafrica@nobleprog.co.za or +27 (0)10 005 5793
CANN SDK for Computer Vision and NLP Pipelines Training Course - Enquiry
Related Courses
Advanced LangGraph: Optimization, Debugging, and Monitoring Complex Graphs
35 HoursLangGraph serves as a robust framework designed for constructing stateful, multi-agent LLM applications through composable graphs, ensuring persistent state and precise control over execution flows.
This instructor-led live training, available both online and 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.
Upon completing this training, participants will be equipped to:
- Design and optimize complex LangGraph topologies to enhance speed, reduce costs, and improve scalability.
- Ensure system reliability through the implementation of retries, timeouts, idempotency, and checkpoint-based recovery mechanisms.
- Debug and trace graph executions, inspect states, and systematically reproduce issues encountered in production environments.
- Instrument graphs with logs, metrics, and traces, deploy them to production, and actively monitor SLAs and associated costs.
Course Format
- Interactive lectures and group discussions.
- Extensive exercises and practical practice sessions.
- Hands-on implementation within a live laboratory environment.
Customization Options
- For inquiries regarding customized training for this course, please contact us to make arrangements.
Building Coding Agents with Devstral: From Agent Design to Tooling
14 HoursDevstral is an open-source framework engineered to build and operate coding agents capable of interacting with codebases, developer utilities, 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 equipped to:
- Set up and configure Devstral for coding agent development.
- Design agentic workflows for exploring and modifying codebases.
- Integrate coding agents with developer tools and APIs.
- Apply best practices for secure and efficient agent deployment.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical application.
- Hands-on implementation within a live laboratory environment.
Course Customization Options
- For customized training requests, please contact us to arrange.
Open-Source Model Ops: Self-Hosting, Fine-Tuning and Governance with Devstral & Mistral Models
14 HoursDevstral and Mistral are open-source artificial intelligence technologies engineered for flexible deployment, custom fine-tuning, and scalable integration.
This instructor-led live training, available either online or on-site, is designed for intermediate to advanced machine learning engineers, platform teams, and research engineers who aim to self-host, fine-tune, and govern Mistral and Devstral models within production environments.
Upon completion of this training, participants will be able to:
- Establish and configure self-hosted environments tailored for Mistral and Devstral models.
- Apply fine-tuning techniques to enhance performance in specific domains.
- Implement version control, monitoring, and lifecycle governance mechanisms.
- Ensure security, regulatory compliance, and responsible utilization of open-source models.
Course Format
- Interactive lectures and discussions.
- Practical exercises focused on self-hosting and fine-tuning.
- Live laboratory sessions for implementing governance and monitoring pipelines.
Customization Options
- To request customized training for this course, please contact us to make arrangements.
AI Facial Recognition Development for Law Enforcement
21 HoursThis instructor-led, live training in Kenya (online or on-site) is targeted 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 conclusion 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: Introduction to Scientific Image Processing
21 HoursFiji is a powerful open-source image processing suite that bundles ImageJ (a program designed for scientific multidimensional images) along with a comprehensive range 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 build 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 updating of a Fiji installation at 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
Course Format
- 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 session in Kenya (online or onsite) is designed for beginner to intermediate 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 creating stateful, multi-agent LLM applications through composable graphs that maintain persistent state and offer precise control over execution flow.
This instructor-led live training, available either online or on-site, targets intermediate to advanced professionals looking to design, implement, and manage LangGraph-based financial solutions with robust governance, observability, and compliance measures.
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 for critical processes.
- Deploy, monitor, and optimize LangGraph systems to ensure optimal performance, cost-efficiency, and adherence to SLAs.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical application.
- Hands-on implementation within a live laboratory environment.
Customization Options
- For customized training requests for this course, please contact us to arrange details.
LangGraph Foundations: Graph-Based LLM Prompting and Chaining
14 HoursLangGraph is a framework designed for constructing graph-structured LLM applications that facilitate planning, branching, tool use, memory management, and controllable execution.
This instructor-led, live training (available online or onsite) targets beginner-level developers, prompt engineers, and data practitioners who aim to design and build reliable, multi-step LLM workflows using LangGraph.
By the end of this training, participants will be able to:
- Explain core LangGraph concepts (nodes, edges, state) and when to use them.
- Build prompt chains that branch, call tools, and maintain memory.
- Integrate retrieval and external APIs into graph workflows.
- Test, debug, and evaluate LangGraph apps for reliability and safety.
Format of the Course
- Interactive lecture and facilitated discussion.
- Guided labs and code walkthroughs in a sandbox environment.
- Scenario-based exercises on design, testing, and evaluation.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
LangGraph in Healthcare: Workflow Orchestration for Regulated Environments
35 HoursLangGraph facilitates stateful, multi-actor workflows driven by LLMs, offering precise control over execution paths and state persistence. In the healthcare sector, these capabilities are essential for ensuring compliance, enhancing interoperability, and developing decision-support systems that align with clinical workflows.
This instructor-led training, available both online and onsite, targets intermediate to advanced professionals seeking to design, implement, and manage LangGraph-based healthcare solutions while addressing regulatory, ethical, and operational challenges.
Upon completion of this training, participants will be equipped to:
- Design healthcare-specific LangGraph workflows with a focus on compliance and auditability.
- 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 arrange.
LangGraph for Legal Applications
35 HoursLangGraph is a framework for building stateful, multi-actor LLM applications as composable graphs with persistent state and precise control over execution.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish to design, implement, and operate 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 preserve 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 with observability and cost controls.
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.
Building Dynamic Workflows with LangGraph and LLM Agents
14 HoursLangGraph serves as a framework for assembling graph-structured Large Language Model (LLM) workflows that enable branching, tool utilization, memory management, and controlled execution.
This instructor-led, live training session (available online or onsite) targets intermediate-level engineers and product teams aiming to merge LangGraph’s graph logic with LLM agent loops to develop dynamic, context-aware applications. Examples include customer support agents, decision trees, and information retrieval systems.
Upon completing 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 to ensure robust execution.
- Integrate retrieval systems, APIs, and structured outputs into agent loops.
- Evaluate, monitor, and strengthen agent behavior to guarantee reliability and safety.
Course Format
- Interactive lectures and facilitated discussions.
- Guided labs and code walkthroughs within 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 for Marketing Automation
14 HoursLangGraph serves as a graph-based orchestration framework designed to facilitate conditional, multi-step workflows involving Large Language Models (LLMs) and tools, making it ideal 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. The course focuses on implementing dynamic, branching email campaigns and content generation pipelines using LangGraph.
Upon completing this training, participants will be equipped to:
- Create graph-structured workflows for content and email, incorporating conditional logic.
- Seamlessly integrate LLMs, APIs, and data sources to enable automated personalization.
- Effectively manage state, memory, and context throughout multi-step campaigns.
- Evaluate, monitor, and optimize the performance and delivery outcomes of their workflows.
Course Format
- Engaging interactive lectures coupled with group discussions.
- Practical hands-on labs focused on implementing email workflows and content pipelines.
- Scenario-based exercises covering personalization, segmentation, and branching logic.
Course Customization Options
- For those interested in tailored training for this course, please get in touch with us to make arrangements.
Le Chat Enterprise: Private ChatOps, Integrations & Admin Controls
14 HoursLe Chat Enterprise is a secure ChatOps platform that delivers governed, customizable, and private conversational AI capabilities for organizations. It supports Role-Based Access Control (RBAC), Single Sign-On (SSO), connectors, and integrations with enterprise applications.
This instructor-led training, available online or on-site, is designed for product managers, IT leads, solution engineers, and security/compliance teams at an intermediate level. The course focuses on deploying, configuring, and governing Le Chat Enterprise within enterprise settings.
Upon completion, participants will be equipped to:
- Deploy and configure Le Chat Enterprise securely.
- Implement RBAC, SSO, and compliance-driven controls.
- Connect Le Chat with enterprise applications and data repositories.
- Develop and execute governance and administrative playbooks for ChatOps.
Training Format
- Engaging lectures and group discussions.
- Extensive exercises and practical practice.
- Hands-on implementation in a live laboratory environment.
Customization Options
- For tailored training solutions, please get in touch with us to arrange.
Python and Deep Learning with OpenCV 4
14 HoursThis instructor-led, live training in Kenya (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 Kenya (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.
