Professional Certificate in Machine Learning Operations
Machine Learning Solution Design:
To be advised
Production, Deployment and Sustainment of Machine Learning Solutions:
To be advised
Duration: 6 Days
This Professional Certificate will equip learners with the following competencies:
- Understand Machine Learning Operations (MLOps) as a set of practices that aims to deploy and maintain Machine Learning (ML) models in production reliably and efficiently.
- Understand Machine Learning models are tested and developed in isolated experimental systems.
- Understand the MLOps steps in the ML pipeline to automate the execution of the pipeline for continuous training (CT) of the models.
- Learn how to iteratively and continuously cater to changing business needs, as the Continuous Integration / Continuous Delivery (CI/CD) practices are adopted to create and implement new model pipelines.
Job Role Readiness
This Professional Certificate will prepare learners in the following job roles to perform their responsibilities more effectively:
- Data Engineers
- Data Analysts
- Software Engineers
Who Should Attend
Any professionals involved in Machine Language lifecycle management.
- At least a polytechnic diploma
- Basic Python programming knowledge
(Click their photos to view their short biographies)
Dr Ai Xin
Dr Ai Xin is currently a Lecturer with the School of Computing at the National University of Singapore (NUS). She has many years’ experience on teaching Artificial Intelligence and Data Science courses, e.g. machine learning, deep learning, data mining and etc.
She graduated from NUS with a PhD degree on Electrical and Computer Engineering. Her research focused on Game Theoretical Modelling, Optimization Methods, Algorithm Design and Wireless Networks.
She worked in BHP Billiton Marketing Asia for eight years and gained a lot of industry experience through different functions, e.g. risk management, supply chain management, sales and marketing planning and etc.
Dr Ganesh Neelakanta Iyer
Dr Ganesh Neelakanta Iyer is a Lecturer in in the Department of Computer Science, National University of Singapore. Prior to this, he worked as a lead DevOps engineer at Salesforce.com. He had also served as an Associate Professor in the Department of Computer Science & Engineering, School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore. He has received his Bachelor’s degree in Computer Science and Engineering (University first rank) from Mahatma Gandhi University, Kerala, India in 2004 and Masters and PhD degrees from National University of Singapore in 2008 and 2012 respectively. He brings in a decade of industry experience in various companies including Sasken Communication Technologies, NXP semiconductors and Progress software. He has handled several roles in the software industry including QA Architect, Technical Support Manager, Engineering development and Technology Evangelist.
He has strong inclination towards Game Theory. He applies game theory for handling conflicts, enforcing cooperation and for multi-agent systems. His technical knowledge and experience are in various areas including Cloud/Edge/Fog Computing Paradigms (including cloud platforms, containers and Kubernetes), Computer Networks, Software Engineering practices (Agile) and Quality Analysis, Economic models (Game Theoretic principles) and current day practices on cloud-based enterprise architectures, Machine Learning and technology for traditional Indian dance (such as Kathakali) popularization. His mathematical interests include game theory, graph theory, optimization principles etc. Over the past several years he has acquired practical knowledge and experience in various cutting-edge software engineering methodologies including Agile framework and has experience formulating and implementing various software engineering principles using Agile for large and small product development teams.
Dr Iyer is active in doing practical industry-oriented research on the above topics of his interest. He also aspires to do research on technological innovations to popularize traditional classical arts such as Kathakali and Koodiyattam.
He has published two book chapters in the “Encyclopaedia for Cloud Computing” in 2016 in addition to several book chapters, journals and conference publications. Dr. Iyer has delivered several practical workshops and talks on various cutting-edge technology topics in many academic and industry events in several countries including USA, Europe, Australia and Asia. Many of these were on the contributions made by him in his industry engagement for software quality analysis with current day software engineering principles such as Agile for application development involving cloud platforms, mobile platforms and IoT based systems.
Dr Iyer is an IEEE Senior Member. He has been a reviewer of many internationals Journals including IEEE Transactions on Computers, IEEE Transactions on Parallel and Distributed Systems and several international conferences. He was also the program chair for several international conferences including ICCCI and ICIWE.
Dr Iyer has strong teaching skills and boundless passion for teaching. He was a visiting faculty at IIIT-H (International Institute of Information Technology Hyderabad), and has been teaching various subjects including “Game Theory for Computer Science”, “Computer Networks”, “Advanced Computer Networks” and “Scripting and Computer environments” for which, he has framed the syllabus, developed the materials and references, structured the grading scheme and formulated continuous assessment strategies. Further, he was a member of Board of Studies at JNTU-H (a premium university in India) where he participates in framing the syllabus for the university’s upcoming academic year.
He is also an expert in performing Kathakali, a traditional Indian dance. He has composed a story in Kathakali and he spends a considerable amount of his personal time to uplift this traditional art by organizing Kathakali performances, workshops and demonstrations and performance by himself. He has also composed a Kathakali story “Sri Mookambika Mahathmyam” which has been staged in multiple venues in India.
Total Nett Programme Fee Payable, Including GST, after additional funding from the various funding schemes
Participants must fulfill at least 75% attendance and pass all assessment components to be eligible for SSG funding.
To enquire, email firstname.lastname@example.org
To register, click Register
Machine Learning Solution Design: TGS-2022014568 (Classroom Learning) / TGS-2022014576 (Synchronous e-learning)
Production, Deployment and Sustainment of Machine Learning Solutions: TGS-2022014570 (Classroom Learning) / TGS-2022014574 (Synchronous e-learning)