Deploying AI Agents in Production Environments Training Course
Bringing AI agents into production settings is an essential milestone for translating models into scalable, reliable, and high-performing real-world solutions.
This instructor-led session, available online or in-person, is tailored for advanced professionals seeking to refine their expertise in deploying and overseeing AI agents within production landscapes.
Upon completion, attendees will be equipped to:
- Architect and build AI deployment pipelines that scale effectively.
- Leverage Docker and Kubernetes to encapsulate and coordinate AI agents.
- Track and enhance the performance of AI agents in live environments.
- Establish CI/CD workflows tailored for AI agent releases.
- Adhere to security protocols and data governance standards.
Course Delivery Style
- Engaging lectures paired with open discussions.
- Abundant opportunities for practice and skill reinforcement.
- Practical application within a live-lab setup.
Tailored Training Options
- For bespoke training requirements related to this course, please reach out to schedule a custom arrangement.
Course Outline
Foundations of AI Deployment
- An overview of the AI deployment lifecycle
- Common hurdles when moving AI agents to production
- Core focus areas: scalability, reliability, and long-term maintenance
Containerization and Orchestration Strategies
- Basics of Docker and the principles of containerization
- Applying Kubernetes for coordinating AI agents
- Best practices for administering container-based AI applications
Serving AI Models
- An introduction to model serving frameworks, such as TensorFlow Serving and TorchServe
- Creating REST APIs to facilitate AI agent inference
- Managing both batch and real-time prediction workloads
CI/CD for AI Agents
- Configuring CI/CD pipelines specifically for AI deployments
- Streamlining the testing and validation of AI models
- Executing rolling updates and maintaining version control
Monitoring and Optimization
- Deploying monitoring solutions to track AI agent performance
- Identifying model drift and determining retraining requirements
- Enhancing resource efficiency and system scalability
Security and Governance
- Complying with data privacy laws and regulations
- Protecting AI deployment pipelines and associated APIs
- Implementing audit trails and logging for AI applications
Practical Exercises
- Encapsulating an AI agent using Docker
- Releasing an AI agent via Kubernetes
- Establishing monitoring for AI performance and resource consumption
Recap and Future Directions
Requirements
- Strong command of Python programming
- A solid grasp of machine learning workflows
- Experience with containerization platforms such as Docker
- Familiarity with DevOps methodologies (suggested)
Target Audience
- MLOps engineers
- DevOps specialists
Need help picking the right course?
southafrica@nobleprog.co.za or +27 (0)10 005 5793
Deploying AI Agents in Production Environments Training Course - Enquiry
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