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About the Session:
HackerTrail will be hosting the next instalment of our Tech 101 series on Thursday, February 10th, from 8.00 to 9.00 PM (GMT +8).
Organizations are expanding into a wider range of artificial intelligence (AI) and machine learning (ML) use cases, with a particular focus on process automation and customer experience, as they both have direct lines to tangible ROI, generating short-term savings that boost the bottom line in the former and driving top-line growth through capturing new customers and retaining existing customers in the latter.
The average number of data scientists employed has increased 76% year-on-year, yet with only 22% of companies using machine learning able to deploy ML models successfully, the questions of what makes it so hard and what can be done to alleviate the situation beg to be asked.
ML Ops is a set of practices that combine ML, DevOps, and data engineering to deploy and maintain ML systems in production reliably and efficiently. Knowledge of the various maturity levels of ML Ops based on the industry is imperative for ML teams to succeed. Addressing the above issues and more is Suteja Kanuri, an ML expert and engineer, most recently of Citi and ShopBack.
During this session, you can expect to learn about:
1. The basic principles of MLops
2. How MLops is used in various industries
3. The tools and technologies used to accomplish MLops
4. The three levels of MLops and MLops for production
5. A career path and outlook for an ML engineer
This is a session not to be missed, especially if you are a data scientist, your company’s CIO, or part of your company’s infrastructure and operations or business development teams.
Date: Thursday, 10 February 2022
Time: 8.00 PM SGT / 5.30 PM IST / 11.00 PM AEDT
Duration: 1 hour
About the Speaker:
Suteja Kanuri is a Machine Learning expert based out of Singapore. She started her career in the data industry and has had prior stints as Engineering Manager across the banking and e-commerce sectors. She possesses immense data technology experience working with machine learning teams and is well-versed in ML Ops practices across multiple industries. Most recently, Suteja was responsible for managing and nurturing a 15-member MLops team which comprised both ML and data engineers.