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.
Justin Reock, Chief Architect for OpenLogic at Perforce Software, describes what is software fuzzing and why it is needed. Justin is the author on a chapter about fuzzing in a new book from Perforce Software: “Accelerating Software Quality: Machine Learning & Artificial Intelligence in the Age of DevOps”.
Michael Larsen, Head of Customer Success & Academy at BotXO.ai, takes us through the essential steps of building a chatbot. A chatbot project should be easy to carry out, easy to maintain and edit, and bring results pretty quickly. It should meet the needs of the business and potential problems to solve, proper flow and content, and integrations.
Every month, we take a look back at our top ten most clicked topics. Last month was packed full of exciting news such as more info on Java 16 with its upcoming migration to Git and GitHub. Other top news include interviews on the programming language Julia, the visualization platform Grafana and the Node alternative Deno. In May, we also learned how to analyze big data using Java and saw C pass Java in the monthly TIOBE Index.
The financial sector is a late adopter of machine learning. Top applications include fraud detection, customer care, and risk hedging. See how the mass adoption of machine learning can apply even to the most conservative sectors.