Course Code: dfinetunedmp
Duration: 21 hours
Prerequisites:
  • 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
Overview:

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.
Course Outline:

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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