MLOps

Unlock the full potential of machine learning with MLOps - Operationalizing Machine Learning at Scale, a hands-on course designed for data scientists, ML engineers, and DevOps professionals who want to bridge the gap between model development and production deployment. MLOps (Machine Learning Operations) combines the principles of software engineering, DevOps, and data science to streamline the lifecycle of ML models-from experimentation and training to deployment, monitoring, and governance. This course dives into the tools, workflows, and best practices needed to build scalable, reproducible, and reliable ML systems. Learners will explore CI/CD pipelines for ML, model versioning, automated testing, containerization, and cloud-native deployment using platforms like MLflow, Kubeflow, Docker, and AWS SageMaker. Whether you're working in a startup or enterprise environment, this course equips you to deliver machine learning solutions that are production-ready, maintainable, and aligned with business goals.

MLOps is a set of practices and tools that aim to streamline the process of deploying, managing, and scaling machine learning models in production environments.

MLOps addresses challenges such as model versioning, automation of training pipelines, continuous integration and delivery (CI/CD) of models, and real-time monitoring of model performance.

This training offers a comprehensive pathway from the basics of MLOps to advanced topics, with a focus on practical experience and real-world applications. The program is designed to give students the knowledge and hands-on experience needed to succeed in the rapidly evolving field of MLOps. You shall learn all the tools, techniques and best practices, to get the job done.

Target Audience:-
-Machine learning engineers and data scientists ready to move models from notebooks to production
-DevOps professionals looking to specialize in ML infrastructure and automation
-Technical leads and architects designing scalable ML systems for enterprise applications
-Graduate students and researchers building deployable ML prototypes
-AI practitioners working in regulated industries

Learning Outcomes:-
-Understand and implement MLOps pipelines in production
-Automate model training, testing, and deployment workflows
-Deploy scalable, robust, and production-grade machine learning models
-Manage model performance and implement monitoring and retraining solutions
-Utilize industry-standard tools to build and manage complex MLOps pipelines

Course Format:-
✔ The course shall be delivered through a combination of lectures, interactive discussions & case studies
✔ Participants are exposed to practical exercises and new-age projects, where they learn by doing
✔ Participants shall have access to online resources, including reading materials, videos & business simulations
✔ Students shall receive all the study material
✔ Guest speakers from the industry may be invited to share insights and experiences
✔ Regular assessments and quizzes will be conducted to reinforce learning
✔ This is a Classroom only training
✔ Corporates: We understand your specific needs and goals. Contact us for customizations to this training

Trainers:-
✔ Equipped with multidisciplinary backgrounds
✔ Experts from the field of Maths, Financial Markets, AIML, Data Science & Management
✔ Each with over 25+ years of International experience working in EU / US / Australia
✔ All our trainers are Highly Qualified and Certified, in their respective subject areas