Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Foundations of AI Inference with Docker
- Analyzing the specific demands of AI inference workloads
- Evaluating the strategic benefits of containerized inference
- Reviewing common deployment scenarios and operational constraints
Developing AI Inference Containers
- Choosing the appropriate base images and supporting frameworks
- Efficiently packaging pretrained models for distribution
- Structuring inference logic specifically for containerized execution
Hardening Containerized AI Services
- Reducing the potential attack surface within containers
- Implementing robust strategies for managing secrets and sensitive data
- Designing secure networking and API exposure protocols
Techniques for Portable Deployment
- Optimizing image size and structure to maximize portability
- Safeguarding predictable runtime environments across different platforms
- Handling dependency management seamlessly across various systems
Local Deployment and Validation
- Executing inference services locally using Docker
- Debugging and troubleshooting inference containers effectively
- Conducting rigorous performance and reliability testing
Deployment to Servers and Cloud VMs
- Adapting container configurations for remote infrastructure
- Setting up secure access controls for server environments
- Implementing inference APIs on cloud-based virtual machines
Leveraging Docker Compose for Multi-Service AI Architectures
- Orchestrating inference services alongside necessary supporting components
- Managing environment variables and configuration files systematically
- Scaling microservices efficiently using Docker Compose
Monitoring and Maintaining AI Inference Services
- Adopting effective logging and observability practices
- Identifying and diagnosing failures within inference pipelines
- Managing model updates and version control in production settings
Conclusion and Future Pathways
Requirements
- A foundational grasp of basic machine learning principles
- Practical experience with Python or backend development environments
- Basic familiarity with core containerization concepts
Target Audience
- Software Developers
- Backend Engineers
- Teams responsible for deploying and maintaining AI services
14 Hours
Testimonials (3)
multi-tiered, structured course programme.
Bert Paelinckx - Cube SoftwareSolutions
Course - Introduction to Docker
How trainer deliver knowledge so effectively
Vu Thoai Le - Reply Polska sp. z o. o.
Course - Certified Kubernetes Administrator (CKA) - exam preparation
The knowledge and exchanges with Augustin