In today's competitive business landscape, data isn't just a resource—it's the driving force behind growth and innovation. A vast majority of data—estimated between 80-90%—is unstructured. This includes images, videos, and written documents. This vast reservoir of unstructured data presents an extraordinary opportunity for analytics and advanced machine learning tools to unlock significant business potential. Despite this fact, however, only 18% of organizations are tapping into this resource according to a 2019 Deloitte report. Not surprisingly when one considers that unstructured data needs to be refined in order to be of use.
SuperAnnotate rises to the challenge, turning this unstructured data into valuable and actionable SuperData. Through its robust platform and comprehensive managed services, it enables an increasing number of innovative organizations to utilize its tools in the creation of cutting-edge AI products.
Introducing the SuperAnnotate-Databricks Collaboration
We are pleased to announce a collaboration between SuperAnnotate and Databricks, the go-to platform for over 9000 worldwide organizations for their data analytics and AI operations. This partnership aspires to empower Databricks users by:
- Enhancing their ability to effectively harness unstructured data.
- Streamlining the integration of Databricks' powerful computing and AI tools with SuperAnnotate.
This partnership represents an exciting fusion of SuperAnnotate's all-in-one AI data infrastructure platform that helps to annotate, debug, manage, and version top-quality training data with Databricks' exhaustive data management, distributed computing, and machine learning capabilities. This blend aims to unlock and amplify the exploitation of unstructured data across a diverse range of businesses.
Optimizing Data Management and Model Training
With the integration of SuperAnnotate's Python SDK with Databricks, users can expect a seamless initiation, configuration, and handling of projects and data directly from Databricks. The new SuperAnnotate-Databricks connector simplifies this process even further by transforming annotation data into Apache SparkTM dataframes, enabling ML teams to shift their focus from data wrangling to training their machine learning models.
Further benefits of this collaboration include the ability to easily set up active learning workflows, where low-confidence predictions are automatically routed to the SuperAnnotate platform. This functionality could:
- Boost model performance.
- Substantially reduce costs by enabling the model to guide data labeling and collection efforts.
Additionally, this partnership enables businesses to assess model performance by adding both predictions and ground truth to the SuperAnnotate platform. SuperAnnotate's suite of editors covering Image, Video, Text, LiDAR, and much more allow for training and fine-tuning of everything from Object Detection to large Language Models (LLMs).
Get started with SuperAnnotate on Databricks
To help you get started with SuperAnnotate on Databricks, we have created a series of notebooks walking you through project setup, training an object detection model, and setting up an active learning workflow. Make sure to check out the video below and request a demo here.
