MLOps and Optimizing ML Operations: Learning from Those Who Have Been There
Webinar Speaker: Fern Halper, TDWI VP Research, Senior Research Director for Advanced Analytics
Date: Thursday, October 7, 2021
Time: 9:00 a.m. PT, 12:00 p.m. ET
As organizations grow in analytics maturity, they often look to expand their analytics arsenal to include machine learning models. Machine learning—where systems analyze data in order to learn from it and improve their own performance with minimal human intervention—is becoming part of the analytics fabric of many organizations as its competitive value becomes understood. According to TDWI research, machine learning is already mainstream and demand is growing.
Although organizations spend a lot of time thinking about building machine learning models, they often spend less time thinking about how they are going to deploy and manage these models in production. This includes issues such as data engineering and model registration, versioning, training, deployment, monitoring, and governance. It includes organizational issues, as well, given that data scientists don’t want to be responsible for model management (nor can they be as models begin to scale). This is where ML Operations (MLOps) comes in.
What does it take to succeed with MLOps? Join TDWI’s VP of Research Fern Halper as she hosts a roundtable with Otis Elevator, Standard Chartered Bank, and representatives from LTI to discuss the MLOps journey and best practices for success.
- Business benefits of MLOps
- When MLOps becomes necessary
- Technologies needed for optimizing MLOps
- Organizational strategies for MLOps
Product Head - LTI Mosaic AI
Larsen & Toubro Infotech (LTI)
Shivanand Pawar is product head for Mosaic AI, a proprietary AI platform by LTI. With 10+ years of experience, he is a hands-on expert in data science, big data, Kubernetes, and application development. Shiva is actively involved in consulting with clients across various domains in adopting big data and machine learning and operationalizing AI/ML at scale. He brings in immense knowledge around the challenges of machine learning adoption and how a systematic approach simplifies the journey and assures ROI.
Head, IFRS9 and Enterprise Risk Management Technology
Standard Chartered Bank
Recognized as Digital thought leader carrying 15 years of experience with innovative mindset driving the culture of diversity & inclusion and contributing to the company’s vision & mission.
Ankita heads the IFRS9 and enterprise risk management technology domain for the bank and is known for driving innovation and digital transformation, maximizing operational excellence, and delivering financial performances to identify, evaluate, mitigate, and monitor the company's enterprise risk.
Before her current role, Ankita worked in similar capacity at various other technology domains within Standard Chartered such as GRC (governance, risk, and compliance) and liquidity. She has been an avid contributor to the diversity and inclusion index for the organization and runs various initiatives supporting the company's D&I agenda.
After studying information technology at UP Technical University in India, Ankita worked with Fortune 500 companies which opened new avenues for her in terms of professional growth and identifying her interests in enabling next-generation technology solutions and enablement.
She is currently based in Singapore.
Associate Director, Connected Digital Apps
Sanjith is the development lead and digital product owner of several pillars of Otis ONE, OTIS’ IoT-connected elevator program. He is also a data architect and serves as an advisor to the Otis ONE architects group. He has been involved with machine learning and MLOps for the duration of his time at OTIS. He filed for his first patent recently in the area of IoT data simulation and validation.
Fern Halper, Ph.D.