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Machine Learning Operations (MLOps)

Syllabus, study tools, lectures, and curriculum map.

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Weekly outline

Syllabus

Week topics from the course map

00W00

Topic

Incomplete

01W01

Introduction to MLOps: Overview of MLOps and its significance: Key challenges in deploying and managing ML models in production, Comparison of traditi

Incomplete

02W02

ML Pipelines & Data Management: Overview of data engineering tools and practices, Data management for ML models, ML pipeline automation. DVC overview

Incomplete

03W03

Data Management - Part 2 : Feature Stores. Motivation, role in ML and Generative AI applications, benefits for MLOps. Feast overview.

Incomplete

04W04

CI/CD for ML Models: Use of version control systems like Git for model development, automated testing & validation, model delivery strategies

Incomplete

05W05

Machine Learning Model Development: Comparison of development of small models vs large models including LLMs, tracking model training & experimentatio

Incomplete

06W06

Model Deployment and Serving: Overview of containerization and orchestration technologies and various other cloud-based deployment options, Deployment

Incomplete

07W07

Monitoring and Performance Optimization: Techniques for monitoring model performance in production, Logging and error tracking for ML systems, Perform

Incomplete

08W08

ML Security: Overview of Security considerations for ML, field of MLSecOps, tooling options.

Incomplete

09W09

ML Governance: Overview of model explainability and ethical considerations in ML deployments, tracking bias

Incomplete

010W10

MLOps for LLMs - Part 1 : Model Versioning for base models and fine-tuned variants, CI/CD specifics

Incomplete

011W11

MLOps for LLMs - Part 2 : Accuracy, Performance vs Cost tradeoffs, Observability, Security & Governance (Bias, Toxicity, Explainability)

Incomplete

012W12

Closing topics: Advanced topics of Federated learning, edge inferencing; Putting it all together

Incomplete

Study tools

Memorize first

Compact recall strip

Open notes

3 min

Experiment Tracking with MLflow and W&B

Open this first for the core logic and traps.

Dive in

3 min

MLOps Lifecycle & CRISP-ML Framework

Open this first for the core logic and traps.

Dive in

3 min

Model Serving: APIs, Containers, and Inference Optimization

Open this first for the core logic and traps.

Dive in

Practice Dock

One drill at a time, with Theo hints, teacher lanes, and a replay queue.

Use this when the theory is already clear and the goal is fast, clean retrieval under pressure. Mark weak questions, copy a revision pack, and move on.

TheoTeacherTextbookSpeedrun

Ask less. Recall more.

Open Practice Dock

SM-2 Active

Spaced Repetition

Flashcards

Spaced repetition engine for long-term retention of core concepts.

Start session

Not Available

Interactive Simulations

Visual Labs

Exam Mode

Adaptive Practice

Mock Exam

Set paper

Code Protocol

OPPE Simulator

Secure Comm-Link Terminal

Secure Comm-Link // Playlist Connected

Uplink 12ms

Syllabus Synchronization: Active

Open current cue

Syllabus Matrix Registry

Global Course Index

Open Full Frame

Master Registry

v6.4 Directory

Foundational

Diploma

BSc Degree

BS Degree

PG / MTech

BSDA5014

PG / MTech

4 Credits

Machine Learning Operations (MLOps)

This course aims to give students a comprehensive understanding of Machine Learning Operations (MLOps). MLOps is a paradigm to deploy and maintain ...

Required Dependencies:

Deployment Hub External Docs

Execution Protocol

Module 0

Topic

Module 1

Introduction to MLOps: Overview of MLOps and its significance: Key challenges in deploying and managing ML models in production, Comparison of traditi

Module 2

ML Pipelines & Data Management: Overview of data engineering tools and practices, Data management for ML models, ML pipeline automation. DVC overview

Module 3

Data Management - Part 2 : Feature Stores. Motivation, role in ML and Generative AI applications, benefits for MLOps. Feast overview.

Module 4

CI/CD for ML Models: Use of version control systems like Git for model development, automated testing & validation, model delivery strategies

Module 5

Machine Learning Model Development: Comparison of development of small models vs large models including LLMs, tracking model training & experimentatio

Module 6

Model Deployment and Serving: Overview of containerization and orchestration technologies and various other cloud-based deployment options, Deployment

Module 7

Monitoring and Performance Optimization: Techniques for monitoring model performance in production, Logging and error tracking for ML systems, Perform

Module 8

ML Security: Overview of Security considerations for ML, field of MLSecOps, tooling options.

Module 9

ML Governance: Overview of model explainability and ethical considerations in ML deployments, tracking bias

Module 10

MLOps for LLMs - Part 1 : Model Versioning for base models and fine-tuned variants, CI/CD specifics

Module 11

MLOps for LLMs - Part 2 : Accuracy, Performance vs Cost tradeoffs, Observability, Security & Governance (Bias, Toxicity, Explainability)

Module 12

Syllabus

Topic

Introduction to MLOps: Overview of MLOps and its significance: Key challenges in deploying and managing ML models in production, Comparison of traditi

ML Pipelines & Data Management: Overview of data engineering tools and practices, Data management for ML models, ML pipeline automation. DVC overview

Data Management - Part 2 : Feature Stores. Motivation, role in ML and Generative AI applications, benefits for MLOps. Feast overview.

CI/CD for ML Models: Use of version control systems like Git for model development, automated testing & validation, model delivery strategies

Machine Learning Model Development: Comparison of development of small models vs large models including LLMs, tracking model training & experimentatio

Model Deployment and Serving: Overview of containerization and orchestration technologies and various other cloud-based deployment options, Deployment

Monitoring and Performance Optimization: Techniques for monitoring model performance in production, Logging and error tracking for ML systems, Perform

ML Security: Overview of Security considerations for ML, field of MLSecOps, tooling options.

ML Governance: Overview of model explainability and ethical considerations in ML deployments, tracking bias

MLOps for LLMs - Part 1 : Model Versioning for base models and fine-tuned variants, CI/CD specifics

MLOps for LLMs - Part 2 : Accuracy, Performance vs Cost tradeoffs, Observability, Security & Governance (Bias, Toxicity, Explainability)

Closing topics: Advanced topics of Federated learning, edge inferencing; Putting it all together

Compact recall strip

One drill at a time, with Theo hints, teacher lanes, and a replay queue.

Flashcards

Visual Labs

Mock Exam

OPPE Simulator

Secure Comm-Link Terminal

Syllabus Matrix Registry

Master Registry

Foundational

Diploma

BSc Degree

BS Degree

PG / MTech

Machine Learning Operations (MLOps)

Execution Protocol

Topic

Introduction to MLOps: Overview of MLOps and its significance: Key challenges in deploying and managing ML models in production, Comparison of traditi

ML Pipelines & Data Management: Overview of data engineering tools and practices, Data management for ML models, ML pipeline automation. DVC overview

Data Management - Part 2 : Feature Stores. Motivation, role in ML and Generative AI applications, benefits for MLOps. Feast overview.

CI/CD for ML Models: Use of version control systems like Git for model development, automated testing & validation, model delivery strategies

Machine Learning Model Development: Comparison of development of small models vs large models including LLMs, tracking model training & experimentatio

Model Deployment and Serving: Overview of containerization and orchestration technologies and various other cloud-based deployment options, Deployment

Monitoring and Performance Optimization: Techniques for monitoring model performance in production, Logging and error tracking for ML systems, Perform

ML Security: Overview of Security considerations for ML, field of MLSecOps, tooling options.

ML Governance: Overview of model explainability and ethical considerations in ML deployments, tracking bias

MLOps for LLMs - Part 1 : Model Versioning for base models and fine-tuned variants, CI/CD specifics

MLOps for LLMs - Part 2 : Accuracy, Performance vs Cost tradeoffs, Observability, Security & Governance (Bias, Toxicity, Explainability)

Closing topics: Advanced topics of Federated learning, edge inferencing; Putting it all together

Video Archive

Intelligence Hub