Anthill Inside 2019

Machine learning (ML) has seen substantial adoption, and a large number of data
science teams are being created. Taking on ML projects requires product managers
and engineers to learn an ML approach to problem solving, to be able to effectively
work with data scientists and data engineers. There exists a huge gap in understanding

• both cultural & technical - in most organizations since product owners and the
  engineering teams have not worked with ML before. The ML approach is sufficiently
  different from a usual software engineering approach that it needs deeper
  understanding and adoption

The aim of this workshop is to acquaint product managers and engineering managers
with ML ways, to effectively lead ML teams producing outcomes of value.

Outline

The workshop starts with an introduction to ML to get an intuitive understanding of ML algorithms work,
and covers the typical project lifecycle and roles and responsibilities. It also covers
common mistakes while executing ML products followed by best practices & design
patterns from an ML software perspective. Finally, there is a brief session on ML data
governance challenges. Multiple case studies are discussed to illustrate these concepts,
along with interactive sessions to practice applying the content to custom usecases.

Requirements

Should have 5+ years experience in the Industry. Preferably people who
have worked as a Product Manager / Engineering Manager before.

Speaker bio

Lavanya Sita Tekumalla is a Machine Learning Scientist and the founder of AiFonic Labs. She holds a PhD in Machine Learning (Bayesian Models) from the Indian Institute of Science and a Masters in Computer Graphics from the Uninversity of Utah. She has been in the Industry for over 9 years in various roles (Ex-Amazon-twice, InMobi, Myntra, Kenome). She has also owned product and helped with ML at Kenome, a deep-tech startup until recently.