Mentor Based
AI Model Deployment and Serving
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Course Overview
Program Syllabus
Module 1
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.
Module 2
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.
Module 3
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.
Module 4
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
Lead Instructor
Dr. Sarah Chen
PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.
Limited SeatsClosing Soon
AI Model Deployment and Serving
Professional Certification Program
🎥
FormatLive + Recorded
📅
Duration8 Weeks
📜
CertificationVerified
Module 5
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 6
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 7
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 8
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 9
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 10
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 11
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
Module 12
Who Should Enrol?
Data scientists, AI engineers, DevOps professionals, and cloud engineers focused on model deployment and serving.
Module 13
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.
Module 15
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
Module 16
Join Our Hall of Fame!
Take your research to the next level with NanoSchool.
Module 17
Publication Opportunity
Get published in a prestigious open-access journal.
Module 18
Centre of Excellence
Become part of an elite research community.
Module 19
Networking & Learning
Connect with global researchers and mentors.
Module 20
Global Recognition
Worth ₹20,000 / $1,000 in academic value.
Module 21
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