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, a Tel Aviv-based startup that specializes in helping businesses manage the entire data lifecycle for their AI projects, including helping them annotate their datasets, today announced that it has now raised a total of $16 million. This includes a $5 seed round that was previously unreported, as well as an $11 million Series A round that recently closed. The Series A round was led by Amiti Ventures with participation from F2 Venture Capital, crowdfunding platform OurCrowd, NextLeap Ventures and SeedIL Ventures. "Many organizations continue to struggle with moving their AI and ML projects into production as a result of data labeling limitations and a lack of real time validation that can only be achieved with human input into the system," said Dataloop CEO Eran Shlomo. "With this investment, we are committed, along with our partners, to overcoming these roadblocks and providing next generation data management tools that will transform the AI industry and meet the rising demand for innovation in global markets." For the most part, Dataloop specializes in helping businesses manage and annotate their visual data.
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).
You now have the ability to run your Azure Machine Learning service pipelines as a step in your Azure Data Factory pipelines. This allows you to run your machine learning models with data from multiple sources (more than 85 data connectors supported in Data Factory). The seamless integration enables batch prediction scenarios such as identifying possible loan defaults, determining sentiment, and analyzing customer behavior patterns. Get started quickly by creating an AzureMLService connection and AzureMLExecutePipelne activity to invoke your Azure Machine Learning pipelines in a Data Factory data pipeline.
As long as "Entire Data Set" means the same features, this is exactly what FeatureUnion does: If you have two different sets of features that you want to combine, you first need to put them into a single dataset, and then have each branch of the FeatureUnion first select the features it should operate on.