How can we make AI frictionless?
How to choose between DataOps vs MLOps vs AIOps - what are the right Ops for your big data team?
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.
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❓
Your machine learning model is going to fail in the pandemic.
Most companies choose to start with building small scale PoCs or POVs.
Never spend another second building a monster-app that is miles away from the original scope.
Product managers have a tremendously tough job.
An hour reading sprint a day to keep your knowledge broad and up-to-date
✈ Deploying a machine learning model is like flying an airplane 🛩 Having no logging is like flying without a Blackbox.
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.
Never be in trouble for delivering the wrong thing at the wrong time to the wrong person.
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.