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Dataloop raises $16 million for data annotation tools

#artificialintelligence

AI data management and annotation startup Dataloop today announced that it raised $16 million in funding, a combination of an $11 million series A round and a previously undisclosed $5 million seed round. A spokesperson says the funds will enable Dataloop to increase its recruitment efforts and grow its presence in the U.S. and Europe. Training AI and machine learning algorithms requires plenty of annotated data. But data rarely comes with annotations. The bulk of the work often falls to human labelers, whose efforts tend to be expensive, imperfect, and slow. Dataloop claims to solve the annotation challenge with a platform for automating data prep and data operations.


Dataloop Drives Labeling Into the DataOps Pipeline

#artificialintelligence

Data is the fuel for machine learning, but the data needs to be accurately labeled for the machines to learn. To that end, data training startup Dataloop yesterday unveiled that it's received $11 million in Series A funding to build SaaS data pipelines that combine human supervision of the data annotation process, along with data management capabilities. Today's computer vision models are extremely powerful, and the ones based on deep learning approaches can exceed human capabilities. From self-driving cars navigating in the world to programs that can accurate diagnose diseases in MRI images, the potential uses for Ais built upon convolutional neural networks are astonishingly wide. However, there's a catch (there always is).


Global Big Data Conference

#artificialintelligence

Data is the fuel for machine learning, but the data needs to be accurately labeled for the machines to learn. To that end, data training startup Dataloop yesterday unveiled that it's received $11 million in Series A funding to build SaaS data pipelines that combine human supervision of the data annotation process, along with data management capabilities. Today's computer vision models are extremely powerful, and the ones based on deep learning approaches can exceed human capabilities. From self-driving cars navigating in the world to programs that can accurate diagnose diseases in MRI images, the potential uses for Ais built upon convolutional neural networks are astonishingly wide. However, there's a catch (there always is).


Data labelling -- overcoming AI projects' biggest obstacle

#artificialintelligence

Building artificial intelligence (AI) models is not like building software. It requires a constant'test and learn' approach. Algorithms are continually learning and data is being refined -- and as much relevant, high-quality data as possible is key. Data labelling is an integral part of data pre-processing for machine learning. If you're training a system to identify animals in images, for example, you might provide it with thousands of images of various animals from which to learn the common features of each, which would eventually enable it to identify animals in unlabelled images.


Announcing @DataLoopIO Bronze Sponsor @CloudExpo #DevOps #Monitoring

#artificialintelligence

SYS-CON Events announced today that Dataloop.IO, an innovator in cloud IT-monitoring whose products help organizations save time and money, has been named "Bronze Sponsor" of SYS-CON's 20th International Cloud Expo, which will take place on June 6-8, 2017, at the Javits Center in New York City, NY. Dataloop.IO is an emerging software company on the cutting edge of major IT-infrastructure trends including cloud computing and microservices. The company, founded in the UK but now based in San Francisco, is developing the next generation of cloud monitoring required for microservices and DevOps. For more information, please visit https://www.dataloop.io/. The widespread success of cloud computing is driving the DevOps revolution in enterprise IT.