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?
When it comes to the more creative tasks, natural language processing (NLP) lacks one thing: the human touch. However, this could soon change thanks to one simple technique we as humans use to learn language: listening. Google is now training its NLP algorithms with human dialogue.
The Machine Learning Conference can help you understand your data, optimise your models, and enhance your business. At the Voice Conference, you’ll learn how to develop and design the future using new voice technologies. Attend these conferences in-person in Berlin or remotely from home.
ML Conference and Voice Conference give you the opportunity to learn about the latest tools and technologies. Seasoned experts will share insights on how to understand your data, optimize your models, process the power of natural language and more. Attend remotely or in-person in Berlin.
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.
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.
Software design is also an area where AI can prove to be very effective. There are various benefits of AI-powered coding. AI-assisted tools can help developers to reduce around 50% of the number of keys pressed in the development of software.
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.
Computer vision technology has helped Taiwan, the country located close to the virus hotbed, to nip the aggressive COVID-19 dissemination in the bud. The technology has also proved efficient for virus spread prevention and monitoring. See how it’s at the forefront at beating the virus.
The world of cybersecurity is rapidly becoming an ML arms race, where security pros arm themselves with ML and AI-enhanced defensive tools, while the bad guys use the technology to amplify the threat they pose. See what open source machine learning project is helping hunt security flaws.
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.
Why does DIY AIOps fail and what is the root cause? In many cases, all the time and effort put into a do-it-yourself project simply winds up being wasted. This article looks at how to safely encourage AIOps exploration and measure ROI from AIOps without the risk of failure.
The purpose of this series of articles is to provide a complete guide (from data to predictions) to machine learning, for .NET developers in a .NET ecosystem, that is now possible using Microsoft ML.NET and Jupyter Notebooks. Even more, you don’t have to be a data scientist to do machine learning.
The purpose of this series of articles is to provide a complete guide (from data to predictions) to machine learning, for .NET developers in a .NET ecosystem, and that is possible now using Microsoft ML.NET and Jupyter Notebooks. Even more, you don’t have to be a data scientist to do machine learning.
