Heather Arranie 
Heartex, an AI-focused open source data labelling platform, recently raised $25 million in series A funding to help with AI development. As machine learning technology evolves and more businesses enter the space, having access to quality data labelling is increasingly important.
Heartex’s platform helps with this by delivering high-quality training datasets and other machine learning data labelling services. This article will explore how their platform works and the value it provides AI developers.
Overview of Heartex
Heartex is an AI-focused, open source data labelling platform founded in 2017 to help companies rapidly manage the labelling of data needed to develop and deploy algorithms in AI applications. Recently, the Palo Alto-based startup raised $25 million in a Series A round led by Index Ventures.
Using machine learning and natural language processing, the HeartEx platform enables teams to automatically verify the accuracy of labels created by human experts, exponents, or those without prior knowledge of computers or datasets. This reduces both effort and cost while ensuring a high degree of accuracy. In addition, Heartex offers industry-specific algorithms such as medical imaging, self-driving cars, voice recognition and robotics.
This combination of manual/automated labelling makes it easy for businesses ranging from small startups to enterprise level organisations to quickly generate insights from large data sets — insights that can lead to improved model training accuracy for artificial intelligence projects such as machine vision and natural language processing. This data can then be used for predictive analytics and other applications that require trained models for decision making tasks.
Heartex’s mission
Heartex is on a mission to help companies build AI-based products faster. By providing an open source data labelling platform, Heartex aims to reduce the time and overhead required to train and test AI models.
Funded by $25M in series A investment and supported by investors such as Cisco, Google Ventures, and ZhenFund, Heartex offers data labelling applications that allow companies to quickly label data sets with an intuitive crowd-based approach. This approach is built on a distributed platform that combines the user experience of community collaboration with modern technology for secure enterprise grade annotation tools.
The user interface makes it easy for users of varying technical skills to ANNOTATE DATA visually from images or video broadcasts; custom labels can be created intuitively from scratch or easily integrate existing labels. In addition, it offers advanced integration tools for all leading AI development platforms such as TensorFlow, scikit-learn, Caffe2 and more.
The DATA LABELLING PLATFORM is designed for all stages of training AI models – from label generation through full model deployment. A flexible API enables teams to create deep learning models faster; validation feature ensures high quality data labelling process; team collaboration encourages agile practices when working with any data set or deep learning problem. In addition, the platform uses robust scalability that supports large numbers of concurrent users at all times and automatic backups on both local machines and cloud servers while offering accurate performance metrics that ensure good results in workloads of any complexity or size.
Heartex’s AI-focused Data Labelling Platform
Heartex recently raised a series A round of $25M to continue developing their AI-focused open source data labelling platform. Their platform is designed to help developers automate the process of labelling data and to make it much easier to use machine learning and deep learning technologies.
In this section, we will discuss the features and benefits of Heartex’s data labelling platform.
Features of the platform
Heartex’s data labelling platform was specifically designed to help fuel AI development and accelerate the adoption of open source in the AI industry. This customizable platform can easily manage supervised machine learning projects and optimise them according to specific needs.
Some important features of the Heartex platform include:
– Ability to collectively label datasets – With the Heartex platform, developers can label data quickly by collaborating with others and leveraging resources like gamified labelling.
– Ability to customise projects – With configurable settings, labelling rules, domain specific tasks, and quality benchmarks, users have full control over their project design and can adjust it as needed.
– Powerful analytics – Users can easily measure progress through interactive dashboards and powerful metrics within the platform. The feature lets users obtain real-time insights that increase accuracy and insight into project performance.
– Real-time validation tools – Users can define validation strategies for every task based on their own rules or best practices from the subdomain to keep projects consistent and accurate.
– Automation capabilities–Automated API integration allows users to integrate their dataset processing pipelines with their custom ML pipelines or any external services providers like AWS Sagemaker or Google Cloud Machine Learning Hub.
With these features available on Heartex’s easy-to-use data labelling platform, AI developers have all they need, allowing them to efficiently produce results without any hassle while focusing on meaningful tasks at a rapid pace!
Benefits of using the platform
Heartex’s AI-focused, open source data labelling platform is designed to facilitate the development of Artificial Intelligence (AI) systems. With its powerful combination of crowdsourcing and AI capabilities, the platform enables data scientists to quickly tag and label data sets to help train, evaluate and improve machine learning models. It also supports a variety of tasks such as image classification, object detection and text annotation.
Using Heartex’s data labelling platform offers numerous benefits for AI development:
1) Enhanced Quality – With Heartex’s sophisticated system of quality assurance measures, users can be confident that their outputs will be accurate even with large datasets. This means that developers don’t need to spend extra time manually checking each labelled entry for accuracy or consistency.
2) Cost Savings – By utilising crowdsourced labour with numerous workers, developers can access more labels at a fraction of the cost compared to hiring an in-house team or fully automated service. Moreover, it’s easier to outsource more complex tasks when you can access verified expert level contributors already familiar with the ground rules for quality assurance.
3) Increased Efficiency – From generating insights faster through improved model training speed, Heartex’s open source platform streamlines both frontend and backend processes from start-to-finish including process automation resulting in better decision making abilities in real-time scenarios where rapid response is needed.
4) Seamless Connectivity – Through deeper integration using APIs & docker containers among various platforms/apps within a single ecosystem achieves automated model creation for AI Datasets allowing effective resource utilisation and faster transfer rates–all without any user intervention required throughout deployment process!
Heartex’s Recent Funding
Heartex has recently raised $25 million in Series A funding to develop its AI-focused, open source data labelling platform.
The European Innovation Council and Emery Capital co-led this new funding round. This brings the total funding to date of Heartex to $31 million.
The new funds will accelerate the development of Heartex’s AI-powered platform.
Amount of funding
On February 25, 2021, Heartex announced that it had raised $25 million in a Series B funding round. Investors included Balderton and Runa Capital, who had previously invested in Mount Knight, a start-up Heartex acquired in 2020. This follows an April 2020 Series A funding round led by OpenOcean and PointNine Capital.
This recent $25 million in funding adds to Heartex’s total amount of investor capital since it was founded. This brings the overall amount raised to over $37 million so far. The new funds will be used to expand its AI-focused open source annotation platform Annotate and develop its new machine transcription service Transcribe. The money will also fuel growth in Europe and the United States, enabling Heartex to further increase its teams working on product development, sales & marketing and customer success.
Investors involved
Heartex recently announced that the company has secured $25 million in a Series A funding round. The round was led by Hornbill Capital, with participation from Battery Ventures, Amplo, Point Nine Capital and Singapore’s Pavilion Capital. Previous investors such as Super Seed, Breega and moonactive also participated in the round.
The funding will support Heartex’s growth in data labelling platforms and expand its presence into new industries and international markets. It will also enable the company to boost its open source product innovation and amplify their engineering capabilities across machine learning, computer science and software engineering.
This comes two years after Heart had secured seed investment of $2 million which was raised in 2018 by lead investor SuperSeed VC and follow-on investors including Breega capital moonactive. Furthermore, the platform leverages powerful mechanised annotation algorithms developed to provide quick AI development insights for research teams utilising laptop or cluster-based computing environments. This open-source product utilises tensorflow or pytorch libraries for annotating data packaged into a clean interface that can be used out-of-the-box or customised according to your requirements.
Purpose of the funding
Heartex recently raised $25M in funding to bolster its AI-focused data labelling platform.
This funding round was led by Y Combinator with additional participation from Amplify Partners and existing investors. This is Heartex’s second major financing round, including Innovation Endeavours and Impactful Ventures investments.
This new funding seeks to further develop Heartex’s platform, which features open source tools that help companies quickly and accurately label their data sets, while ensuring privacy and security. Developing quality data sets is a critical factor in improving the accuracy of AI models; however, it can be a difficult process as it requires manual work. Heartex aims to address this problem by providing a suite of products that can automate labelling while maintaining privacy protections throughout the entire pipeline.
This latest round of funding will allow Heartex to focus on growing its user base and scaling its product offerings; to help companies develop their training datasets faster and more efficiently than ever before. In addition, the new funds will be used for research & development activities such as creating new technologies anchored on automated labelling solutions.
Impact of Heartex’s Platform on AI Development
Heartex, a company focused on developing AI-focused data labelling platforms, recently raised $25M for its open source platform. This platform offers a powerful data labelling component that improves the accuracy and speed at which AI models learn. This has significantly impacted AI development, as data labelling is a crucial component of the development process.
Let’s take a deeper look at how this platform has impacted AI development.
How the platform helps with AI development
Heartex’s platform offers a full suite of tools to help developers efficiently and accurately label datasets for training AI algorithms. In addition, this open source platform simplifies the entire process, making it easier and faster to create high-quality datasets essential for building successful AI projects.
The platform is designed to improve the quality and speed of data labelling by integrating with existing automated ML systems, such as converged datasets, cluster analysis, and even active learning models. It also allows teams to share datasets securely across multiple organisations and leverage sophisticated computer vision technology for large-scale video annotation tasks.
By optimising the pipeline for data labelling, Heartex’s platform makes it far easier for companies to create AI-ready datasets from diverse sources such as text, images and videos. Its tools can help developers accurately label dataset elements relevant to a specific task or algorithm by leveraging deep learning capabilities. The system also eliminates manual workflows and provides timely feedback so developers can monitor, validate and refine their results quickly without missing critical details.
Heartex’s automated data labelling software also has features that allow users to customise labels using flexible strategies such as natural language processing (NLP) and image recognition algorithms to tailor the data sets more specifically to their needs. This flexibility ensures that companies have access to quality labelled data sets that match their project’s requirements as closely as possible while remaining current with market trends regarding accuracy rate and dataset size limitations.
Overall, with Heartex’s efficient platform coupled with its powerful tools for customising labels using NLP algorithms, image recognition features etc., companies now have access to better labelled datasets at faster speeds which will help them deliver successful AI projects in a cost effective manner.
How the platform is different from other data labelling platforms
Heartex’s platform is aimed at helping scientists and developers easily create datasets for machine learning. While many other data labelling platforms are available, Heartex’s platform stands out due to its openness and flexibility. As an open source platform, users can customise the codebase to their needs without restrictions. Additionally, Heartex has integrated AI technology into its software to make data annotation faster and more accurate.
The open source codebase enables teams to build custom annotation pipelines or use existing ones shared by the community, creating a development environment uniquely tailored to the team’s workflow. This improves development times while providing better control over data labelling accuracy.
Heartex also includes AI-powered labelling tools such as an object tracking system and value estimator, which can help speed up manual tasks like object segmentation and text classification by predicting where objects should go in images or suggesting similar words for difficult text annotations.
Beyond speeding up the process of annotating dataset inputs, Heartex’s platform provides features like bulk actions that can accelerate production output drastically. For example, its Form Builder feature lets users fill out multiple fields with single click actions, saving time and reducing mistakes from retyping information multiple times on different fields.
In short, Heartex’s open source approach to data annotation makes it a distinct offering from others in the industry — offering developers improved speed and accuracy through automation while providing total flexibility when customising team needs via the open source codebase.
