Machine learning has siblings.

Machine Learning Vs. Artificial intelligence: What’s the difference? Machine learning is one of the subfields of Artificial Intelligence. It refers to the process of getting a computer to learn from data without being explicitly programmed.
Machine Learning Vs. Artificial intelligence: What’s the difference? — Photo by Pavel Danilyuk from Pexels

Artificial Intelligence is not a thing.

It’s more of an umbrella term that brings together several subfields of computer science. This field is divided into multiple parts, algorithms, theories, and applications.

Each has different goals and methods to pursue them. …

Data science has been democratized for the most part. AI is now mainstream!

Welcome to the Age of Citizen Data Scientists. Data science has been democratized for the most part. AI is now mainstream!
Citizen data scientists transform the way organizations work — Photo by Keira Burton from Pexels.

Data science is no longer the exclusive province of large companies with deep pockets.

AI has finally made its way into our everyday lives, and it’s only a matter of time before you start seeing it becoming more widely used across all industries, not just finance!

With that came a…

Large-scale data science teams can have several distinct roles & responsibilities to manage machine learning operations.

MLOps ensures seamless integration of machine learning models to production systems in a data science project. There are various roles and responsibilities in this space.
MLOps in data science has various roles and responsibilities — Photo by Lucas Santos on Unsplash.

There hasn’t been anything called MLOps five years ago. But it’s an indispensable part of any data science project now.

MLOps refers to the practice of applying Applying DevOps principles to machine learning (ml) systems. …

Deploy ML models and make them available to users or other components of your project.

Deploying machine learning models to production — Photo by Kindel Media from Pexels

Working with data is one thing, but deploying a machine learning model to production can be another.

Data engineers are always looking for new ways to deploy their machine learning models to production. They want the best performance, and they care about how much it costs.

Well, now you can…

Edge computing is a hot topic. But what exactly does it mean? What it has for the healthcare industry?

Edge computing on IoT and mobile devices has transformed the way healthcare was, and it is now.
Edge computing on IoT and mobile devices has transformed the way healthcare was, and it is now. — Photo by Alena Shekhovtcova from Pexels

Edge computing refers to the processing of data at or near the source of generation rather than transferring all data back to a central hub for processing.

In other words, this means that any device with an internet connection can have its processor and storage unit — allowing for more…

Technology is transforming patient care by making it more accessible and convenient.

Examples of digital transformation in healthcare. Technology is transforming patient care by making it more accessible and convenient.
Technology is transforming patient care by making it more accessible and convenient. — Photo by Chevanon Photography from Pexels

I am a big fan of the digital transformation in healthcare.

I think that it is one of the most important things going on in this time and age, and I have been following its progress with interest for some time. …

Transfer learning will drastically reduce training time and cost when you start a new deep learning project

Transfer learning could help reduce the deep learning training time and cost. For example, you can use a dog classifier to train a cat classifier.
You can use a dog classifier deep learning model to train and recognize cats using transfer learning — Image by huoadg5888 from Pixabay

Training a deep learning model can take days, weeks, or even months.

Transfer Learning could solve this problem. It’s a machine learning method where trained models are reused as starting points for new tasks. This speeds up training and improves performance on related issues.

It is one of the most…

85% of data science projects fail for a simple reason.

The #1 Mistake Companies Make When Creating Their Data Science Foundation
The #1 Mistake Companies Make When Creating Their Data Science Foundation — Photo by Andrea Piacquadio from Pexels

Imagine you just finished training an excellent neural network after months of hard work.

It works well on the training data, test data and passes all your validation tests.

But as you move it to the production, you start to notice; it doesn’t do a great job.

If this sounds…

A quick guide to creating a competitive data quality assessment

Firefighting data quality issues
Firefighting data quality issues — Photo by Black Light Media from Pexels

Data quality assessment is the continuous scientific process of evaluating if your data meets the standards. These standards may be tied to your business or the project goals.

The need for ensuring data quality has increased as the many different ways to acquire data are multi-folded.

Handling a single data…

Thuwarakesh Murallie

Data scientist @ Stax, Inc & Top writer in Artificial Intelligence. https://www.the-analytics.club

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