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5 Steps to Frictionless AI

How can we make AI frictionless?

What the Ops are you talking about?

How to choose between DataOps vs MLOps vs AIOps - what are the right Ops for your big data team?

Why data quality is key to successful ML Ops

Are you testing your data in your data pipeline 🧪?

Why Best-of-Breed is a Better Choice than All-in-One Platforms for Data Science

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

Roadblocks to Production Vary for Different Roles

Roadblocks to model production vary for different roles 🚧

The Recession's Impact on Analytics and Data Science]

We have all felt the impact of the recession in one way or another.

How to Bridge Missed Opportunity

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

How to Bridge Missed Opportunity

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

Which Age of MLOps Are You In?

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

Launching Into Uncertainty - 6 Smart Steps to Future-Proof Your ML Models

Your machine learning model is going to fail in the pandemic.

With AI, You Either Go Big or Go Home

Most companies choose to start with building small scale PoCs or POVs.

3 Tips You Need To Save Your Data Science Project Scope

Never spend another second building a monster-app that is miles away from the original scope.

O'Reilly: What you need to know about Product Management for AI

Product managers have a tremendously tough job.

Rise above the ordinary — a data science reading list

An hour reading sprint a day to keep your knowledge broad and up-to-date

Considerations for Deploying an ML Model

✈ Deploying a machine learning model is like flying an airplane 🛩 Having no logging is like flying without a Blackbox.

WORKERA: AI Career Pathways

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

O'Reilly: 5 key areas for tech leaders to watch in 2020

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

Build Things Properly

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

Pandas is finally version 1.0

Can you believe Pandas 1.0.0 is finally here?

Algorithmia: 2020 state of enterprise machine learning

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

McKinsey: Global AI Survey

This study from McKinsey suggested that there have been measurable benefits from deployed AI.

3 Tips on Defining a Data Science Project Scope with Business

Never be in trouble for delivering the wrong thing at the wrong time to the wrong person.

5 Reasons Why You Should Join Deep Learning Indaba 2019

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.