“We celebrate starts and finishes, but rarely discuss the messy journey in between, where odds are defied and great teams and products are ultimately made.”
As someone who is navigating the ups and downs for the growth function of a startup, Scott Belsky’s “The Messy Middle” was like music to my ears. The insights and stories are memorable and digestible, but illuminating enough to keep me thinking for some time afterward. Below are the tactics and ideas that resonated the most with me:
This section discusses the inevitable hardships of any bold project and how others worked through them.
The 71st episode of Datacast is my conversation with Saishruthi Swaminathan, an Advisory Data Scientist at IBM’s AI Strategy and Innovation division. Previously, she was a technical lead and data scientist in the IBM Center for Open-Source Data and AI Technologies team.
Our wide-ranging conversation touches on her childhood and education in India, her transition from electrical engineering to data science, her work at IBM developing and evangelizing open-source software, the current state and the future of responsible AI, public speaking, online teaching, and much more.
Please enjoy my conversation with Saishruthi!
Listen to the show on (1) Spotify…
Our wide-ranging conversation touches on his biomedical engineering background in Egypt, his transition from software engineering to ML, teaching computer vision, Amazon leadership principles, ML deployment on hardware devices, the state of ML testing, his current journey with Kolena, and much more.
Please enjoy my conversation with Mohamed!
The field of MLOps has rapidly gained momentum among Data Scientists, ML Engineers, ML Researchers, Data Engineers, ML Product Managers, and any other roles that involve the process of designing, building, and managing ML-powered software. I have been actively involved in the MLOps community to keep up to date with rapid innovations happening within this domain.
Two months ago, I attended the second edition of MLOps: Production and Engineering World, a multi-day virtual conference organized by the Toronto Machine Learning Society that explores the best practices, methodologies, and principles of effective MLOps. …
Our wide-ranging conversation touches on her educational background in Physics in France; her transition to data science in Silicon Valley; organizational and operational challenges with enterprise ML; Agile for Data Science teams; her current journey with Alectio to tackle the underinvested Data Preparation sector; her advice for women entering the industry, and much more.
Please enjoy my conversation with Jennifer!
In the past few months, I have been consuming a lot of content on the topic of venture capital (books, podcasts, articles, newsletters, etc.). As a data practitioner and startup operator, I’m eager to learn more about what investors look for when evaluating potential investments in the data/ML infrastructure/tooling space.
A major part of this learning journey has been meeting and making new venture friends! I got connected with Morgan Mahlock a few months back and came across her comprehensive guide on venture resources. Morgan has an unique background studying product design and mechanical engineering and making investments…
The 68th episode of Datacast is my conversation with Sarah Catanzaro — a Partner at Amplify Partners, where she focuses on investing in and advising high potential startups in machine intelligence, data management, and distributed systems.
Our wide-ranging conversation touches on her interest in terrorism analysis growing up; her early career in threat intelligence for the public sector; her transition to startups as the Head of Data at Mattermark; her move to venture capital and the criticality of having high stamina; her investments at Amplify; her predictions for emerging trends in the data tooling ecosystem, and much more.
Our wide-ranging conversation touches on her educational background in Electrical Engineering and Computer Science at UC Berkeley; her work on the model store for Uber’s Marketplace; the difference between academic ML and production ML; the case for researching AI Bias; her current journey with Arize to tackle real-time ML observability; her predictions for the ML infrastructure ecosystem, and much more.
Please enjoy my conversation with Aparna!
Listen to the show on (1) Spotify, (2)…
Earlier of June 2021, I attended two great summits organized by REWORK: The MLOps summit that discovers how to optimize the ML lifecycle & streamline ML pipeline for better production and the ML Fairness summit that discovers strategies to ensure ML models are accountable & fair to build secure & responsible AI.
As a previous attendee of REWORK’s in-person summit, I have always enjoyed the unique mix of academia and industry, enabling attendees to meet with AI pioneers at the forefront of research and explore real-world case studies to discover the business value of AI.
In this long-form blog recap…
Our wide-ranging conversation touches on her educational background in Applied Mathematics and Computer Science; her work on recommendation systems and applied ML at Yandex; her popular teaching materials online and in-person; her startup on Industrial AI; her current journey with Evidently to tackle the model monitoring space, and much more.
Please enjoy my conversation with Emeli!