- Strong understanding of machine learning workflows
- Experience with fine-tuning ML models
- Familiarity with DevOps or MLOps principles
Audience
- DevOps engineers
- MLOps practitioners
- AI deployment specialists
Deploying Fine-Tuned Models in Production focuses on the practical strategies and tools required to integrate fine-tuned models into scalable, real-world applications. Participants will learn to optimize models for performance, ensure monitoring and security, and address challenges in production environments.
This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to deploy fine-tuned models reliably and efficiently.
By the end of this training, participants will be able to:
- Understand the challenges of deploying fine-tuned models into production.
- Containerize and deploy models using tools like Docker and Kubernetes.
- Implement monitoring and logging for deployed models.
- Optimize models for latency and scalability in real-world scenarios.
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.
Introduction to Production Deployment
- Key challenges in deploying fine-tuned models
- Differences between development and production environments
- Tools and platforms for model deployment
Preparing Models for Deployment
- Exporting models in standard formats (ONNX, TensorFlow SavedModel, etc.)
- Optimizing models for latency and throughput
- Testing models on edge cases and real-world data
Containerization for Model Deployment
- Introduction to Docker
- Creating Docker images for ML models
- Best practices for container security and efficiency
Scaling Deployments with Kubernetes
- Introduction to Kubernetes for AI workloads
- Setting up Kubernetes clusters for model hosting
- Load balancing and horizontal scaling
Model Monitoring and Maintenance
- Implementing monitoring with Prometheus and Grafana
- Automated logging for error tracking and performance
- Retraining pipelines for model drift and updates
Ensuring Security in Production
- Securing APIs for model inference
- Authentication and authorization mechanisms
- Addressing data privacy concerns
Case Studies and Hands-On Labs
- Deploying a sentiment analysis model
- Scaling a machine translation service
- Implementing monitoring for image classification models
Summary and Next Steps
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