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#machine learning

Who's using ML?

Exploring the Applications of Machine Learning

Today, machine learning has expanded far beyond simple games of checkers. Although there is still room for innovation, this technology has seen tremendous improvement. And with its many applications, it is moving into the public consciousness as well.

How the technology helps pharma companies speed up vaccine design and handle administration

AI in vaccine development and rollout

It’s possible to accelerate and improve vaccine development and rollout by involving AI. This article describes use cases and tools that AI healthcare companies and research teams built to facilitate vaccine design, speed up trials, predict mutations, prioritize patients, and address vaccine hesitancy.

What will drive NLP growth in the coming year?

A Look Ahead to NLP in 2022

The world is facing a global AI talent shortage, so while there’s a great demand for NLP implementations, the supply of data scientists needed to bring these projects to life are limited. But what if we could democratize NLP, reducing the need for data scientist intervention?

Developer and tester tips

Chatbot Testing: How to Get it Right in the First Go

Chatbots do more than just messaging. They are rapidly adding value to conversations and have context-driven intelligence that aims to solve customer problems in a convenient matter. This is just the tip of the iceberg. In this article, we will talk about the must-haves in your chatbot testing checklist.

Answering key ML questions

What is Data Annotation and how is it used in Machine Learning?

What is data annotation? And how is data annotation applied in ML? In this article, we are delving deep to answer these key questions. Data annotation is valuable to ML and has contributed immensely to some of the cutting-edge technologies we enjoy today. Data annotators, or the invisible workers in the ML workforce, are needed more now than ever before.

5 main challenges & solutions

What is ML governance?

Why do organizations struggle with ML governance? There are five main challenges that we see our customers face when it’s time to tackle ML governance for their organizations. Learn how organizations can improve and implement an MLOps platform and its impact.

…with Microsoft ML.NET – part 3

Machine Learning 101: Part 3

To conclude what we have covered so far, it’s clear that when building a model, the trainer selection is not the most difficult part. AutoML is able to suggest a list with the best models, grace to the evaluation metrics which accompany every model.