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2021, it’s a wrap!

2021 was a monumental year for Heartex and Label Studio. We innovated, built the largest data labeling community, and hired an amazing team. What's in store for 2022?

Michael Malyuk
Michael Malyuk
January 5, 2022
5 min read
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What an amazing and productive year 2021 was for Heartex and the Label Studio community. We collaborated with our customers and our community of Data Scientists to continue innovating and building the best data labeling platform on the market. 

While we accomplished (and shipped) too many things to include in this article, we are extremely proud of these key innovation themes:

  • New Data Types & Multimodal Tasks: Label Studio now supports time series and video data in addition to text, image, and audio data. Label Studio’s customizable interface allows for multimodal annotation tasks that display data samples of different data types at the same time on a single screen to streamline annotation and improve accuracy.
  • Improved Organization & Team Management: Label Studio Teams and Enterprise Editions added Workspaces and Projects to simplify the management and security of large and dynamic annotation teams.
  • The Largest Data Labeling Community - we are now 3,200+ experts strong! In addition to our product innovation, we are extremely proud of the Heartex and Label Studio community. Our community experienced tremendous growth in 2021 as we surpassed 3,200 expert members. We can’t thank our community of experts enough for all of their support and their openness and willingness to share and help other community members.

Dozens of Fortune 500 organizations adopted Label Studio in 2021 for a myriad of use cases and projects. Flexibility and the ability to customize Label Studio continues to be a major reason organizations adopt Label Studio as their annotation platform of choice.  Zhuoru (Simon) Lin, Data Scientist at Bombora and Label Studio Enterprise customer explains: 

“One thing I love about Label Studio is the flexibility it provides. Other data labeling platforms offer a generic interface that can’t really be customized. With Label Studio’s custom config, we tailored the entire interface to our specific needs. Having a well-designed interface improves labeling accuracy. And the scripting is very easy. Anyone can do it”

Label Studio’s inherent flexibility has allowed our customers to consolidate all of their annotation projects on one platform regardless of data type, project, internal team, etc. Now they use Label Studio to power their data-centric AI/ML development.

The age of data-centric AI

As one year comes to an end and a new year begins, it’s always fun and informative to take a trip down memory lane and appreciate just how far we’ve come as an industry. While machine learning and data science have existed for some time now, this past decade has seen rapid acceleration in innovation and adoption and most importantly a drastic increase in the number of organizations, big and small, that are effectively leveraging data science to improve business results. TL;DR; Data Science and machine learning are no longer R&D experiments but proven value drivers and critical to competitive differentiation. How did we get here and where are we going as an industry?

  • The first wave of innovation was algorithm-centric. Experts focused on developing and tuning algorithms. As a result, the data science community considerably improved machines’ ability to learn, understand, and produce accurate predictions. This phase ultimately validated the possibilities of data science and proved, in often non-scalable ways, that models could predict outcomes with high-degrees of accuracy.
  • As algorithm’s proved their value, hardware and infrastructure quickly became a bottleneck and the community quickly focused on developing solutions to optimize the underlying operational systems. While the algorithm and infrastructure challenges were being worked on, data labeling was merely a tactical method of getting data prepared for experimental training, validation, and feeding into the pipeline.
  • As algorithms and model architectures achieved stability, they also became largely commoditized, making it clear that algorithms, on their own, are not a competitive advantage to any one organization. Organizations, which were largely outsourcing data annotation, began to experience challenges with inaccurate training datasets leading to inaccurate models.
  • Data Scientists came to understand that the business value and competitive advantage derived from machine learning and data science largely depends on the organization’s data, internal experts who can uniquely and accurately annotate datasets, and ability to operationalize, produce, and iterate on models at scale.

As we say goodbye to 2021 we find ourselves in a new evolutionary stage in our industry. We are no longer in pure R&D and saying ‘what if’. We are in production and saying ‘watch this’. And the most competitive organizations are achieving these results by extracting value from their proprietary data and by leveraging their internal employees with subject matter expertise to annotate data and solve even more complex problems where domain specific knowledge about your industry, geography, customers, or organization is a requirement.

2022, here we come!

The Heartex team couldn’t be more excited about the year ahead. Our vision is to continuously innovate, build, and provide solutions and expertise to our customers, community, and the broader industry. We have and will continue to enable organizations to build their most accurate models quickly, while establishing operational best practices that power secure, efficient, and scalable practices for iterating, updating, and fine tuning production models as the business evolves.

Our accomplishments in 2021 have set the stage for another year of innovation. But, our vision is BIG and our product roadmap is long (and growing). We think that’s a great thing! But we do need help. That’s why we are so excited to formally announce the addition of Joe Alfaro to the Heartex team as Vice President of Engineering. Joe brings a tremendous amount of experience and expertise in building large-scale software systems and managing and growing innovative software development teams at companies like Citrix, GoDaddy, Sauce Labs, Reciprocity, Valimail, and more. We are so excited to have you on the team Joe! 

It’s safe to say that Joe will be looking for more than a few good engineers to join his team in 2022! If you’re a passionate software engineer, machine learning engineer, engineering manager, or product manager that loves to build innovative solutions from the ground up, then we want to meet you. Check out our open positions or drop us a note.  

Finally, thank you to all of our customers, community members, supporters, and our uniquely talented and hard working team!





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