MLOps - Scalable ML Operations - Associate

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Program Fee
49,500 (Incl. GST)

Certified by IITM Pravartak & TCS

Program Description

Learners in this programme will acquire and master the basics of MLOps skills with the help of industry and academic experts through live lectures and various hands-on tools required for industry use cases. This programme will be a combination of practical learning (hands-on and experiential learning paradigms) and theoretical learning (live lectures). MLOps focuses on automating the entire workflow of building an ML model, including data collection, training, testing, deployment, monitoring, and retraining. This ensures a consistent and efficient process.

Key Highlights

Knowledge of Spark Architecture and Programming

Understanding the ML project lifecycle and Architecture

Principles of Containerization and DataOps

Automate ML Workflow, Pipelines and Deployment

Understanding of Generative AI Architectures and Models

110 hours of overall experiential learning

Modular Assignments, Quizzes, Campus Immersion (Optional)

Discussion room community

Interact with IITM Pravartak SMEs & Industry Experts

Curated datasets, tutorials, coding assignments & notes

NPT assessment & excellent career opportunities

2 days* Campus Immersion (*Tentative)

Learning Format

Online

Duration

12 Weeks

Certified by

IITM Pravartak Technologies Foundation
Technology Innovation Hub (TIH) of IIT Madras and TCS

Education Qualification

Freshers/individuals pursuing Bachelor's/Master's degree, who aspire to a career in the field of AI-ML
Junior and mid-career professionals, looking for accelerated career growth and a salary increase.

Suggested Prerequisites

Understanding of the fundamentals of AI and ML with basic programming and mathematical knowledge
Recommended to complete 'Introduction to Python Programming, Statistics Fundamentals and Introduction to Scala' before attending the live lectures.

Lead Faculty

Madhusudhanan ("Madhu") Baskaran

IIT Madras Pravartak Academic Expert

Dr. Madhu is a Chief Data and AI Strategist with a PhD in AI/ML, and brings over 32 years of cross-functional experience in bridging innovation and execution - right from discovery stage, R&D to product launches across AI, Data, and Engineering domains. He leads AI transformation initiatives across sectors with a focus on Generative AI, Document Automation, Domain - Specific LLMs, and Responsible AI systems. He advises enterprises and governments on building intelligent systems rooted in explainability, modularity, and practical scalability.
An Adjunct Professor at IIT Madras, Principal AIML Consultant at IITM Pravartak, Consortion Member at BharatGen and Chief Data & AI Strategist at Centre for Human Centric Artificial Intelligence, he is passionate about shaping AI talent and mentoring startups and senior tech leaders on Deep-Tech Innovation.
His technical portfolio includes: AI/ML disciplines: NLP, Deep Learning, Signal Processing, Medical Image Analysis, Speech Processing, Computer Vision, Edge AI, Generative AI, MLOps and Drone Data Intelligence Platforms & tools: Python, C++, Unity, 3D Slicer, Flask, TensorFlow, PyTorch, OpenCV, AWS, SQLAlchemy, ITK/VTK, PowerBI, Tableau, and more He has consistently worked at the intersection of Academic Depth, Entrepreneurial Spirit, and Social Responsibility and also heading the Innovation Cell & Tamil Nadu operations of the Nisvartha Foundation-supporting higher education for underprivileged students.
He has designed and delivered AI solutions for leading institutions, including: Supreme Court of India - AI-based judicial analysis and document intelligence Comptroller & Auditor General (CAG) - Auditable, transparent AI systems for governance Indian Army Groups - Tactical AI applications, surveillance data processing Large Media Houses - Real-time AI speech recognition and video intelligence Reserve Bank Information Technology (ReBIT) - AI strategy advisory and digital roadmap Top Indian Banks - NLP-driven automation and risk analytics Ministry of Rural Development - AI models for decentralized rural impact Heritage AI Projects - AI-led cultural modeling at Ayodhya and Hampi Kompact.AI - Technical validation and evaluation of AI inferencing platform

Learning Modules

Development Fundamentals

Self-paced Learning*: Python programming: Programming essentials, Data Types, Control Structures, Functions, Modules, OO Concepts, Regex using Python
Self-paced Learning*: Data Operations and Analysis: Numpy, Data Visualization: Matplotlib, Seaborn, Pandas: Data cleaning, munging/wrangling, manipulation, EDA, Working with different data sources and structures, HPO Tuning
Self-paced Learning*: Statistics: Descriptive statistics and Sampling techniques
Self-paced Learning*: Programming using Scala: Programming fundamentals using Scala2, Concepts of Parallel Programming using Scala.
Essentials of Apache Spark: Spark Architecture, Data Frame basics, DataFrame transformation and execution, DataFrame joining, Implementation using PySpark/Scala Spark
Ingesting data into Spark, Spark SQL, Spark Data and Stream processing, Implementation using PySpark/ScalaSpark

Basics of ML/DL Operations

Predictive Modeling: Regression and Classification Algorithms, Supervised and Unsupervised Algorithms, Performance Measures and Metrics
Convolutional Neural Networks, Gradient Descent Algorithm, Pretrained CNNs: Feature Extraction and Fine Tuning, Sequence Models - RNN, Word Embeddings
Implementation of ML algorithms using Python-centric Libraries.

Introduction to Generative AI

Introduction to Generative AI
Variational autoencoders (VAEs)
Transformer Architecture
Encoder only Models - BERT
Decoder only Models - Llama, ChatGPT
Encoder and Decoder model - T5 and BERT
LLMOps Lifecycle

Introduction to MLOps and Architecture

MLOps Vs. DevOps
ML Project Life Cycle
ML Production Infrastructure
MLOps Architecture
MLOps on Cloud
MLOps Data Processing Life Cycle with Data Storage

Understanding DataOps

Review of Operating System Concepts
Basic Principles of Containerisation
Concepts of Version Management System and CI/CD in Data Pipelines
Introduction to Docker
Dockerfiles, Images, and Containers
Docker Networking
Docker Compose
Introduction to Kafka and its Implementation
Introduction to Apache Airflow
Implementation of Pipeline using Ray