AI Model Deployment and Serving
Deploy, Scale, and Manage AI Models for Real-Time Predictions
Early access to e-LMS included
About This Course
This program focuses on the end-to-end process of AI model deployment, exploring cloud-based platforms, containerization, and model serving frameworks like TensorFlow Serving, Flask, and Kubernetes. Participants will gain hands-on experience in deploying models and managing them post-deployment for real-time or batch predictions.
Aim
To provide advanced knowledge on deploying and serving machine learning models in production environments. This program covers best practices for ensuring scalability, real-time inference, and continuous model management.
Program Objectives
- Learn the complete process of deploying AI models in production.
- Implement scalable and secure model serving systems.
- Understand the differences between batch and real-time inference.
- Explore cloud and containerization solutions for AI models.
- Manage post-deployment challenges like model drift and retraining.
Program Structure
Module 1: Introduction to AI Model Deployment
- Overview of Model Deployment and Serving
- Challenges in Deploying Machine Learning Models
- Differences Between Model Development and Deployment
- Key Concepts: Latency, Scalability, and Monitoring
Module 2: Model Serving Architectures
- Overview of Model Serving Architectures
- Batch vs. Real-Time Serving
- REST APIs for Model Deployment
- Microservices Architecture for AI Models
Module 3: Deploying Models on Cloud Platforms
- Cloud-Based Model Deployment (AWS, Google Cloud, Azure)
- Introduction to MLaaS (Machine Learning as a Service)
- Deploying Models with Docker and Kubernetes
- Case Study: Deploying a Model on AWS SageMaker
Module 4: Continuous Integration and Continuous Deployment (CI/CD) for ML
- Understanding CI/CD Pipelines for Machine Learning
- Automating Model Deployment Workflows
- Integrating CI/CD with Model Retraining
- Tools: Jenkins, GitHub Actions, and CircleCI
Module 5: Model Monitoring and Maintenance
- Monitoring Model Performance in Production
- Drift Detection: Data Drift and Concept Drift
- Automated Model Retraining and Updates
- Logging, Metrics, and Alerts for Model Health
Module 6: Model Optimization for Serving
- Model Compression Techniques (Quantization, Pruning)
- Optimizing Models for Edge Devices
- Reducing Latency with Batch Inference
- Tools for Model Optimization (TensorRT, ONNX)
Module 7: Security and Privacy in Model Deployment
- Securing Deployed Models: Authentication, Encryption
- Handling Sensitive Data in Model Serving
- GDPR and Data Privacy Concerns in AI
- Case Studies in Secure Model Deployment
Module 8: Final Project
- Design and Deploy a Machine Learning Model for Real-Time Serving
- Focus Areas: Cloud Deployment, CI/CD Pipeline, or Monitoring
- Present the Deployment Strategy, Challenges, and Solutions
- Evaluation Based on Practical Implementation and Scalability
Who Should Enrol?
Data scientists, AI engineers, DevOps professionals, and cloud engineers focused on model deployment and serving.
Program Outcomes
- Mastery of deploying AI models using Docker and Kubernetes.
- Ability to implement real-time and batch inference systems.
- Skills to monitor and maintain deployed models in production.
- Experience in handling model drift and retraining in production.
Fee Structure
Discounted: ₹8,499 | $112
We accept 20+ global currencies. View list →
What You’ll Gain
- Full access to e-LMS
- Real-world dry lab projects
- 1:1 project guidance
- Publication opportunity
- Self-assessment & final exam
- e-Certificate & e-Marksheet
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