Hi, I'm Merelda ❤

It has been a decade since Harvard Business Review called Data Scientist the sexiest career in the 21st Century. The companies who grabbed onto these talents have not only transformed but completely disrupted the status quo in the data & analytics industry. Much of this hype was initially driven by big tech giants (FAANG, or MAANG circa Nov 2021), but there has been an explosion of high-performing AI teams popping out of every corner of the world.

The software industry has been obsessed with the various ops-terms since the popularisation of DevOps in the late 2000s. Ten years ago, software development to deployment has a throw-over-the-world approach. A software engineer develops the app, then throw it over to the operational engineers. The app breaks often during deployment and creates significant friction between the teams.

Are you testing your data in your data pipeline 🧪?

Interesting view from O’Reilly on why Best-of-Breed trumps All-in-One data science platforms ‼️

Roadblocks to model production vary for different roles   🚧

We have all felt the impact of the recession in one way or another. What would be the impact of the shrinking economy on analytics and data science?

You hired 5x ML Professionals, but you are not getting 5x the return.

Found a beautiful and clean illustration from Valohai on MLOps Stack.

It’s 2020 - which “Age” of MLOps are you in❓

There have been many articles focusing on how machine learning can and is helping during the pandemic. South Korean and Taiwanese governments successfully demonstrated how they used AI to slow the spread of COVID-19. French tech company identifying hot spots where masks are not being worn, advising where governments can focus on education. Data and medical professionals collaborating to index medical journal papers on COVID-19.

Most companies choose to start with building small scale PoCs or POVs. Unfortunately, with this approach, many struggled to move from experimentation to scale. According to Michael Gale at the recent Nvidia virtual GTC conference: starting small means staying small.

As a data scientist, you expect to get a job that lets you do cool stuff - Big data, Big machine (or cloud, like the grown-ups), and Deep neural networks. Reality quickly creeps in as you realize the mismatch between your model, your project manager’s timeline, and your stakeholder’s expectation.