Hi, I'm Merelda ❤

Product managers have a tremendously tough job. An AI product managers does everything a traditional PM does, and much more .

As one of the fastest-growing industries, being a data scientist without a research group can be incapacitating. At the end of last year, I realized I am often missing out on new development in the industry, unknowingly reinventing the wheel, and falling short in conversations with experts. Being a data science consultant, my stress-level exploded as I constantly felt under-prepared going into client meetings.

Having no logging is like flying without a Blackbox. Any terrorist can hijack the plane without any consequences.

There is an emergence of two types of AI focuses within an organisation according to WORKERA:

According to O’Reillly online learning platform report, summarising the AI/ML:

A couple of days ago, we had a meeting to help a client operationalise their machine learning model.

Can you believe Pandas 1.0.0 is finally here?

Hello 2020! We started the year by reviewing this white paper published by Algorithmia on the 2020 State of Enterprise Machine Learning.

This study from McKinsey suggested that there have been measurable benefits from deployed AI. It is encouraging to see that AI adoption across all industries and regions are growing, especially that the majority of the executives reported that it has increased their revenue and/or reduced cost.

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 realise the mismatch between your model, your project manager’s timeline and your stakeholder’s expectation. What they needed (often) is not a 128-layer ResNet, but a simple select & group by query that delivers actionable insights.

In April this year, instead of organising my 20-something birthday party, I spent hours writing motivating emails to my company to convince them to send me to Deep Learning Indaba.