Express Q&A Sessions Ep.2 : The Changing Landscape of ML Engineering

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The MathCo Way

MathCo

11 episodes

3 days ago

In this podcast, listen to the people that make up MathCo talk about the company, its values, culture, and about topics that inspire them.

Technology

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In this podcast, listen to the people that make up MathCo talk about the company, its values, culture, and about topics that inspire them.

Technology

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Express Q&A Sessions Ep.2 : The Changing Landscape of ML Engineering

The MathCo Way

15 minutes 57 seconds

5 years ago

Express Q&A Sessions Ep.2 : The Changing Landscape of ML Engineering

Tune in to this insightful Express Q&A session where Srinidhi Shama Rao, Senior Partner at TheMathCompany, chats with Srivatsa Kanchibotla, Senior Partner at TheMathCompany and recognized as one among the Top 10 Data Scientists in India 2019.Srivatsa has over 10 years of experience in the Data Science industry, and his tenure has seen him work closely with more than 15+ Fortune 500 companies, across BFSI, Technology, Hospitality, Retail and E-Commerce industries, enabling multi-million dollar impact for clients, and also building and managing large teams of Data Engineers and Data Scientists. Srivatsa is known for his unique innovations and unparalleled accomplishments in the industry.In this podcast session, Srinidhi Shama Rao and Srivatsa Kanchibotla discuss advances in ML Engineering, the shift towards a productized landscape, evolving industry expectations from data scientists, and the convergence of ML engineering and Data science disciplines.Get the lowdown on all this and more, in this podcast session.

Podcast Timeline:
0s to 33s - sneak-peek
34s to 47s - Intro
48s - Q1: What is this role called ML Engineering and why is there so much buzz around it?
2:00s - Q2: How easy or difficult is it to make engineering a product or a platform that can cater to most businesses, if not all? What steps are companies taking in this direction?
3:28s - Q3: Is the paradigm of the approach to modeling changing as platform play is coming into the picture? Is it moving on from traditional ways in which we would approach statistical models int the past to more ML-based ways of doing things? If yes, what are they?
5:08s - Q4: As platform play is dominating the world and the world is getting more streamlined and standardized, how is the approach to modeling changing from the statistical ways in which we approached things in the past?
6.57s - Q5: In this new world order, what skills should data scientists be equipped with? How is their world-changing with respect to the new skills that are required to be successful and talk to other peripherals?
8.25s - Q6: As the role of the platform and the data scientist skillset changes, what are the biggest barriers or challenges today for organizations to deploy models, productionalize, and extract value out of them? What is the biggest barrier to value?
10.55s - Q7: Do you see ML Engineering and Data Science roles converging at some point?
11.56s - Q8: As the world gets more productized and in a platform-based paradigm, what is the role of scale? What are its challenges as compared to the scenario a decade ago? Have these challenges changed?
13.25s - Q9: For a minute, I want you to take off your ML Engineer hat and look at this from an outsider perspective. As you look forward 3 to 5 years in the future, do you things becoming more product-dominated or services-dominated?
15.10s - Sign-off