Course Overview
This enhanced DASA DevAIOps course delivers the complete standard DASA curriculum while adding a full day of embedded AI content not found in conventional courses. You’ll receive comprehensive preparation for the official DASA DevAIOps certification exam plus specialized knowledge for implementing AI in resource-constrained embedded systems.
Beyond the standard DASA modules on AI adoption strategies, governance frameworks, and operational efficiency, this course tackles embedded-specific AI challenges: How do you deploy neural networks on microcontrollers with 256KB of RAM? How do you implement predictive maintenance when devices have intermittent connectivity? How do you ensure AI model safety in automotive or medical devices? You’ll master practical solutions including TinyML implementation, federated learning for distributed IoT fleets, edge-cloud AI architectures, and safety-critical AI validation techniques for embedded systems.
Learning Objectives
- Master DevAIOps principles and AI adoption strategies for DevOps contexts
- Create governance frameworks for AI with embedded systems considerations
- Implement AI on resource-constrained microcontrollers using TinyML
- Design edge-cloud hybrid architectures for distributed IoT fleets
- Apply AI safety standards (ISO 26262, IEC 61508) in critical systems
- Build predictive maintenance solutions using sensor data analytics
- Integrate security and ethical AI practices in embedded DevOps
- Enhance operational efficiency through embedded AI automation
Topics Covered
Complete DASA DevAIOps curriculum:
- Introduction to DevAIOps - Understanding AI’s role in modern DevOps
- AI Adoption Strategies - Planning and implementing AI in DevOps workflows
- Governance Framework - Establishing controls and oversight for AI systems
- Data-Driven Decision Making - Leveraging AI for intelligent operational decisions
- Security and Ethical Practices - Implementing responsible AI in DevOps
- Enhancing Operational Efficiency - AI-powered automation and optimization
- Continuous Improvement and Innovation - Using AI for iterative enhancement
- Applying DevAIOps for Efficient Product Delivery - AI-enabled delivery pipelines
Embedded AI additions:
- TinyML and Edge AI - Neural networks on microcontrollers, model quantization
- Embedded AI Architecture - Edge-cloud partitioning, inference optimization
- Predictive Maintenance for IoT - Sensor data pipelines, anomaly detection
- Safety-Critical AI - Validation, verification, and certification for embedded AI
- Federated Learning - Distributed training across IoT fleets
What You Get
- Official DASA DevAIOps certification exam (voucher included)
- Comprehensive course materials and practical exercises
- Additional day of embedded AI specialization
- Practical exercises on TinyML and Edge AI concepts
- Examples from automotive, IoT, and industrial applications
- Networking opportunities with other DevOps and embedded professionals
- Certificate of completion