Machine Learning Operations (MLOps) Specialization

Unleash the potential of data with the fusion of Data Science and Machine Learning, revolutionizing industries and driving innovation by SCAI

Skills You Will Gain

ML Project Lifecycle

Data Pipeline

Model Quantization

Model Serving



This course includes

Syllabus Overview

AI for Everyone

AI is not only for engineers. If you want your organization to become better at using AI, this is the course to tell everyone – especially your non-technical colleagues – to take.

  • Course Overview
    • Introduction and objectives of the course.
  • Introduction to Model Serving
    • Basic concepts and importance of serving models in production.
  • Best Practices in Model Deployment
    • Exploring best practices for deploying and managing models efficiently, including Docker use.
  • Model Serving Infrastructure Overview
    • Detailed look at infrastructure requirements for effective model serving.
  • Deployment Options for Model Serving
    • Various deployment strategies and their trade-offs.
  • Improving Prediction Latency and Reducing Resource Costs
    • Techniques to optimize prediction latency and manage resources effectively.
  • TensorFlow Serving Introduction
    • Exploring TensorFlow Serving for model deployment.
  • Installing TensorFlow Serving
    • Step-by-step installation and setup of TensorFlow Serving.
  • TensorFlow Serving with Docker
    • Using Docker to serve TensorFlow models.
  • Serve a Model with TensorFlow Serving
    • Practical lab on deploying a model using TensorFlow Serving.
  • Scaling Model Serving Infrastructure
    • Techniques for scaling model serving using Kubernetes and KubeFlow.
  • Online Inference Techniques
    • Best practices for managing online inference effectively.
  • Batch Inference Scenarios
    • Understanding when and how to apply batch inference.
  • Batch Processing with ETL.
    • Techniques for implementing ETL processes in batch scenarios.
  • Data Preprocessing for Model Serving
    • The role and techniques of data preprocessing at scale.
  • Principal Component Analysis (PCA)
    • Using PCA for dimensionality reduction to enhance data processing efficiency.
  • Monitoring ML Models
    • Techniques to monitor and maintain ML model performance in production.
  • Logging for ML Monitoring
    • Implementing robust logging mechanisms to track model performance.
  • GDPR Compliance for ML Systems
    • Implementing GDPR-compliant ML systems.
  • Ethical Considerations in AI
    • Discussing ethical considerations and responsibilities in deploying AI systems.


  • Model Quantization
    • Techniques and benefits of model quantization to reduce model size and improve performance.
  • Autoscaling and Load Balancing
    • Applying autoscaling and load balancing to manage deployment efficiently.
  • Advanced Deployment Strategies
    • Exploring complex deployment strategies for large-scale applications.
  • Experiment Tracking Tools
    • Overview of tools for tracking and managing ML experiments.
  • Workflow Automation in MLOps
    • Automating MLOps workflows to enhance productivity and reproducibility.
  • Managing Model Versions
    • Strategies for effective version control of ML models.
  • Continuous Delivery for ML Models
    • Implementing continuous delivery to streamline model updates and improvements.
  • Advanced Monitoring and Debugging
    • Deep dive into advanced monitoring and debugging techniques for ML models.
  • Performance Auditing
    • Techniques for conducting thorough performance audits.
  • Fairness and Bias Detection Techniques
    • Methods to detect and mitigate bias in AI applications.
  • Responsible AI Practices
    • Ensuring ethical practices are integrated throughout the ML lifecycle.
  • Detecting and Addressing Model Decay
    • Identifying and mitigating the decay of model accuracy over time.
  • Model Remediation Techniques
    • Strategies for remediation to maintain model performance.
  • Course Wrap-Up and Advanced Topics*
    • Course Recap
      • Reviewing key concepts and discussing ongoing learning opportunities.
    • Capstone Project and Final Assessments
      • Comprehensive project involving all aspects learned, focusing on building, deploying, and managing an ML model using MLOps principles.

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