Perspectives on recurring challenges faced by ML product teams
Talk proposal for Workshop on Applied ML Management at KDD 2023
Likhitha Patha, Nisarga Markandaiah
lpatha@etsy.com, nmarkandaiah@etsy.com

ABSTRACTIn 2011, Marc Andreessen of a16z famously said
“Software is eating the world”[1], and in 2017, Jensen
Huang, CEO of NVIDIA, has further extended that adage
to say “AI is going to eat the software”[2]. Fast forward totoday, we see that Machine Learning has become
ubiquitous – with the advent of foundational models andreadily available APIs built on top of them, a whopping
35% of companies around the world are alreadyleveraging machine learning in some capacity [3]. As theadoption of machine learning continues to grow across theindustry, we hypothesize that a few challenges are boundto surface that most machine learning product teams will
eventually navigate. In this talk, we plan to discuss key
recurring challenges that we’ve faced while leading teams
that are building machine learning products and offer ourinsights in effectively addressing them.
For instance, 1) How do we build roadmaps suchthat they balance both product and science goals? 2) How
do we adapt our roadmap to the evolving developments inindustry and academia? 3) How do we set goals when MLdriven product development is experimental in nature, anddoesn’t always offer certainty in KPI improvements? 4)
How do we define success in qualitative and quantitative
terms? 5) How do we ship ML products without losing
customer trust?
We will use our unique experience gained inleading machine learning oriented product teams over thelast decade across organizations such as IBM Watson,Amazon Alexa, Twitter Cortex and currently Etsy Search
to help draw parallels to recurring patterns of challenges
we’ve observed during our tenure. We will also share
anecdotes from our experience both from the engineeringand product management perspectives to further shed lighton how we approached these key problems and haveattempted to solve them.
POTENTIAL DISCUSSION POINTS
1) Stages of ML product development and lifecycle
management
2) Evolving a traditional software engineering team
to tackle ML problems3) Avoiding Maslow’s hammer while building yourproduct roadmap
4) Effective strategies for setting OKRs whileaccounting for innovation risk5) Building transparency with customers when ML
isn’t perfect from the get go
AUTHOR BIOS
Likhitha Patha is currently a Staff Product Manager at
Etsy leading Search for International Markets. Prior toEtsy, she spent the last 10 years building products at
Twitter (Cortex Applied ML Signals), Amazon (Alexa AI
NLU), and Microsoft (Visual Studio, Intune, Intune for
Education). She served as a 2020 White House
Presidential Innovation Fellow to help strengthen the techstack in the U.S. government. She received her bachelorsin Electrical Engineering from Virginia Tech, and hermasters in Computer Science from Georgia Tech.
Nisarga Markandaiah is currently an Engineering
Manager at Etsy leading Search for International Markets.
Her team is responsible for building and maintaining theretrieval and ranking machine learning systems that power
Search experiences for all users outside of the US. Prior toEtsy, she worked at IBM’s Watson AI organization on
enterprise machine learning solutions. She received
bachelors in Computer Science from PES University,
India and her masters degree in Computer Science fromthe Language Technologies Institute, Carnegie MellonUniversity.
COMPANY PROFILE
Etsy is the global marketplace for unique and creative
goods. It’s home to a universe of special, extraordinary
items, from handcrafted pieces to vintage treasures. In atime of increasing automation, it’s our mission to keep
human connection at the heart of commerce. We help ourcommunity of sellers turn their ideas into successful
businesses. Our platform connects them with millions ofbuyers looking for an alternative—something special witha human touch, for those moments in life that deserve
imagination.

REFERENCES[1] Andreessen, M. (2011) Why software is eating theworld, Andreessen Horowitz. Available at:https://a16z.com/2011/08/20/why-software-is-eating-theworld/ (Accessed: 02 June 2023).
[2] Simonite, T. (2017) Nvidia CEO: Software is eating
the world, but ai is going to eat software, MIT Technology
Review. Available at:https://www.technologyreview.com/2017/05/12/151722/n
vidia-ceo-software-is-eating-the-world-but-ai-is-going-toeat-software/ (Accessed: 02 June 2023).
[3] IBM - United States (2022) IBM Global AI Adoption
Index 2022. Available at:https://www.ibm.com/downloads/cas/GVAGA3JP(Accessed: 02 June 2023).