MLOps Essentials Certification Course Online
The MLOps Essentials programme by TimesPro is a comprehensive self-paced online course designed to equip learners with the knowledge and practical skills needed to implement Machine Learning Operations effectively. The programme covers the complete MLOps lifecycle, from foundational machine learning concepts and data preprocessing to model development, deployment, monitoring, and maintenance. Learners gain hands-on exposure to industry-standard tools and technologies including Docker, Kubernetes, Git, and cloud platforms such as AWS, Azure, and GCP, along with practical demonstrations of experiment management, data drift detection, hyperparameter optimisation, and CI/CD pipelines for ML models. By the end of the programme, participants will have a thorough understanding of MLOps principles and best practices, enabling them to deploy and manage machine learning models efficiently in real-world production environments. The course is ideal for data scientists, ML engineers, software developers, and technology professionals looking to bridge the gap between model development and production-grade deployment.
₹ 12,299
Overview
The MLOps Essentials programme by TimesPro is a self-paced online course that provides a thorough grounding in Machine Learning Operations, covering every stage of the MLOps lifecycle from data handling to production monitoring. As organisations increasingly rely on machine learning models to drive business decisions, the ability to deploy, manage, and maintain these models reliably in production has become a critical skill. This programme addresses that need directly, offering learners a structured path from fundamental ML concepts through to advanced deployment pipelines, experiment management, and continuous integration and delivery for ML systems. The curriculum is rich with practical demonstrations covering tools such as Git, Docker, Kubernetes, and leading cloud platforms including AWS, Azure, and GCP. Topics such as data drift detection, model interpretability, hyperparameter optimisation, and model serving patterns ensure learners are equipped to handle real-world MLOps challenges. The programme is self-paced and accessible online, making it suitable for working professionals who need flexibility in their learning schedule. Learners are assessed through quizzes and assignments throughout the course and receive a certificate upon successful completion.
Why Technology & Analytics?
The MLOps Essentials programme stands out as a highly practical and comprehensive introduction to one of the most in-demand skill sets in data science and engineering today. MLOps as a discipline bridges the gap between data science experimentation and reliable, scalable production deployment of ML models, and professionals with these skills are increasingly sought after across industries. Unlike courses that focus purely on theory, this programme integrates hands-on demonstrations of real tools and workflows including Docker containerisation, CI/CD pipeline setup with GitHub Actions, experiment tracking, data drift detection, and model serving patterns, giving learners directly applicable skills. The course is self-paced, removing barriers for working professionals who cannot commit to fixed class schedules. TimesPro's backing by Bennett, Coleman and Co. Ltd. and its partnerships with premier institutions add credibility and trust to the certification. For anyone looking to transition into MLOps roles or strengthen their existing ML engineering capabilities, this programme offers a well-structured, affordable, and career-relevant learning pathway.
What does this course have to offer?
Key Highlights
Self-paced online programme with flexible learning schedule
Covers complete MLOps lifecycle from data handling to model monitoring
Hands-on demos with Docker, Git, Kubernetes, and GitHub Actions
CI/CD pipeline design for machine learning systems
Experiment management and data drift detection modules
Hyperparameter optimisation and model interpretability coverage
Cloud platform exposure including AWS, Azure, and GCP
Model deployment pipeline and serving patterns
Certificate of Completion from TimesPro upon successful assessment
Who is this programme for?
Data scientists looking to move models into production environments
Machine learning engineers seeking MLOps skills and best practices
Software developers transitioning into ML engineering roles
Technology professionals working in AI/ML-driven organisations
Anyone seeking to understand and apply end-to-end ML deployment workflows
Minimum Eligibility
Basic understanding of machine learning concepts recommended
Familiarity with Python or a programming language is helpful
Suitable for data scientists, ML engineers, and software developers
No formal prerequisites specified; open to technology professionals
Interest in deploying and managing machine learning models in production
Not sure whether you qualify for this programme?
Admission Overview
The MLOps Essentials programme follows an open enrolment model with no formal admission process. Learners can enrol directly through the TimesPro platform after paying the course fee. While no formal prerequisites are specified, a basic familiarity with machine learning concepts and programming is recommended to get the most value from the curriculum. Once enrolled, learners access the complete course content through the TimesPro portal and can progress through all modules at their own pace. Assessment is conducted through quizzes and assignments embedded within the course, and a final evaluation tests overall comprehension of MLOps principles and tools. Successful completions result in a Certificate of Completion issued by TimesPro, recognising the learner's demonstrated understanding of MLOps practices.
Selection process
How to apply?
Curriculum
The MLOps Essentials curriculum is organised into a single comprehensive module covering the full spectrum of MLOps knowledge and practice. The programme begins with a recap of core machine learning concepts before transitioning into MLOps principles, components, and how MLOps compares with traditional DevOps practices. Data-focused topics include data types, labelling, feature engineering, feature stores, data engineering workflows, and feature management, with dedicated demonstrations for each area. The curriculum then moves into model development topics including experiment management, data drift detection, hyperparameter optimisation, model interpretability, and model evaluation. The deployment section covers deployment pipelines, CI/CT/CD pipeline architecture, pipeline steps, model serving patterns, and containerisation using Git and Docker. The final sections address model monitoring, collaboration and communication in MLOps teams, and practical demonstrations using GitHub Actions and Streamlit-based monitoring tools.
There are 1 semesters in this course
The programme is structured as one comprehensive module titled ML Ops, which systematically covers the end-to-end MLOps workflow. It opens with a recap of machine learning fundamentals and an introduction to MLOps, establishing the context for why MLOps is critical in modern data science. The module then explores the core components of MLOps and draws key distinctions between DevOps and MLOps approaches. Data management is addressed through topics on data types, labelling techniques, feature engineering, feature stores, and data engineering practices, each accompanied by practical demonstrations. The curriculum progresses to experiment management, data drift detection, and hyperparameter optimisation, with hands-on demos reinforcing each concept. Model interpretability is explored both conceptually and through code environment demonstrations. The deployment phase covers deployment pipelines, CI/CT/CD pipeline design, pipeline steps, and model serving patterns, with Git and Docker demonstrated in practice. The monitoring section introduces model monitoring techniques and includes a practical monitoring demonstration. The module concludes with CI/CT/CD pipeline demonstrations using GitHub Actions, experiment management demos, and guidance on collaboration and communication practices within MLOps teams.
ML Ops
Programme Length
Self-paced programme with no fixed duration. Learners can complete the course at their own pace around their existing commitments.
Whom you will learn from?
Learn from top industry experts who bring real-world experience and deep knowledge to every lesson. The instructors are dedicated to help you achieve your goals with practical insights and hands-on guidance.
Tuition Fee
The MLOps Essentials programme is priced at Rs. 12,299 plus applicable GST, payable as a one-time fee at the time of enrolment. The fee covers complete access to all course content, practical demonstrations, quizzes, assignments, and the Certificate of Completion. There are no additional or hidden charges. Payments must be made exclusively through the official TimesPro platform. For any queries related to payment, learners can contact TimesPro.
Fee Structure
Payment options
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About the University
TimesPro, established in 2013, is a Higher EdTech platform by The Times Group focused on professional education in India. The institution offers diverse learning programs across early career courses, executive education, and enterprise solutions. Their programs span banking, finance, technology, analytics, and marketing sectors through partnerships with premier institutions like IIMs, IITs, and global universities
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TimesPro provides extensive career support through dedicated placement assistance and industry connections. The institution focuses on developing industry-ready professionals through practical training, skill development, and career guidance. Their placement cell offers comprehensive support including interview preparation, profile enhancement, and direct connections with leading employers. The career services team works closely with over 3,500 corporate partners to ensure strong placement outcomes and career growth opportunities for students. Students receive personalized mentoring, soft skills training, and industry-specific preparation to enhance their employability.
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placement rate
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₹ 12,299
Frequently asked Questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.