Datasaur

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Datasaur is the leading NLP data-labeling platform, driving 10X quicker project times and improving model performance by 2X.

How Datasaur can help you:

Boost the efficiency of NLP and LLM projects significantly.
Customize your data labeling set up to meet specific model needs.
Reduce errors with robust quality controls.
Automate up to 80% of repetitive cleaning and labeling tasks.
Seamlessly integrate with existing workflows and systems for streamlined operations.

Why choose Datasaur: Key features

Customizable Workflows
Advanced Workforce Management
Robust NLP Labeling
Comprehensive Audio Labeling

Who should choose Datasaur:

Engineers focusing on NLP and LLM projects seeking efficiency and accuracy.
Teams striving for high-quality data with fewer errors in labeling.
Organizations needing customizable workflows and seamless integration into existing systems.

About Datasaur

Website

https://datasaur.ai

Release Date

March 2024

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LS Digital and DataMorph Partner to Speed-up Enterprise AI Readiness

Strategic Partnership to Cut Data Engineering Time by 90%, Fast-Tracking AI Readiness for Enterprises LS Digital, India's leading integrated Digital Business Transformation (DBT) company, today announced its strategic partnership with DataMorph™, a pioneer in Data Pipeline Automation. This collaboration aims to enhance both companies' offerings in the global market, targeting enterprises striving to become AI-ready by significantly reducing the time spent on data engineering and pipeline creation. In an age where enterprises are rapidly moving towards AI readiness, approximately 60% of their time is consumed in data engineering and pipeline creation. DataMorph's innovative platform addresses this challenge by reducing that time by 90%, enabling businesses to accelerate their AI initiatives. The platform's user-friendly interface and no-code/low-code capabilities allow entry-level developers or even business analysts to efficiently manage data pipelines, reducing the dependency on highly skilled data engineering resources. While announcing this partnership, Mr. Prasad Shejale, Founder and CEO, LS Digital, said, "We are excited to collaborate with DataMorph to take their state-of-the-art platform to businesses beyond the US market. This partnership aligns perfectly with our goal to drive digital business transformation for our clients, enabling them to unlock the full potential of their data." As businesses continue to navigate the complexities of digital transformation, DataMorph is set to become a game-changer in the industry. With its innovative approach to data accuracy and seamless implementation of analytics solutions, it provides businesses the confidence and clarity they need to make data-driven decisions. By ensuring compliance and unlocking the full potential of their data, DataMorph empowers organizations to not only meet today's challenges but also future-proof their digital strategies in an increasingly data-centric world. This launch marks a pivotal moment for businesses seeking to thrive in the age of advanced analytics. Speaking on the partnership, Mr. Krishna Katikaneni, CEO and Co-founder, DataMorph, said, "Partnering with LS Digital marks a pivotal step in DataMorph's mission to enable enterprises to be data driven, treat data as a product and become mature in data engineering.. Their expertise in digital business transformation and strong market presence will be crucial in expanding our reach and helping enterprises achieve AI readiness faster." Key Features and Benefits of DataMorph: DataMorph™ is a visually driven data engineering platform designed to scale ELT/ETL processes. It provides a single integrated framework for various types of analytical workloads and orchestration requirements. It's a simple, flexible, extensible, and cloud-agnostic platform. With pre-built connectors & transformation processors along with customizable workflows, enterprises can build reliable data products faster with the existing talent and knowledge. Real-time monitoring, alerts, and robust security measures ensure reliable and scalable operations, reducing data engineering time by 90% and expediting AI readiness for enterprises. Mr. Vinay Tamboli, CEO - Data & Insights, LS Digital, added, "In today's business environment, data is the lifeblood of decision-making and can transform a business. However, the challenge lies not in collecting data but in the complex processes of compiling, sorting, analysing, storing, and implementing it. Without the right system, businesses can waste thousands of dollars and countless hours on this aspect alone. DataMorph's state-of-the-art data pipeline automation platform addresses these challenges head-on, saving both time and resources." In today's digital-first world, businesses are increasingly overwhelmed by the complexity and sheer volume of data generated across various platforms. With growing privacy regulations and the need for accurate, actionable insights, many companies struggle to implement seamless data tracking systems. Issues such as data accuracy, incomplete migrations, and challenges in deriving meaningful insights have become common hurdles. This has created a critical need for services that offer end-to-end solutions to ensure data precision, compliance, and actionable intelligence for businesses striving to stay ahead in a competitive market. About LS Digital LS Digital is a leading independent Digital Business Transformation (DBT) company, integrating the expertise of four innovative entities: LS Digital, Langoor, f1studioz, and Social Panga. Our comprehensive suite of services encompasses business consulting, activation, and transformation through a robust DBT solutions framework; Media, Creative & Communication, Data & Insights, Technology & Innovation, UI/UX and CX/EX. Leveraging digital as a catalyst, we enable brands to holistically develop their businesses across various platforms, ensuring exceptional brand experiences in a digital-first world. For more details, visit: https://www.lsdigital.com/ About DataMorph.ai DataMorph.ai located in Milpitas, California is an independent company that offers data engineering solutions and tools. Our focus is to enable enterprises to spend more time in driving data driven innovation and business outcomes. For more details, visit: https://www.datamorph.ai

CXOToday.com

Tue, 8 Oct, 10:21 AM UTC

Building complex gen AI models? This data platform wants to be your one-stop shop

Exclusive: Encord puts multimodal AI data - including audio - all in one platform. Data development platform Encord is going beyond business analysis to become "the world's only multimodal AI data development platform." On Thursday, the company announced new multi-modal data annotation capabilities for classifying audio and documents -- all in one interface. The update expands on Encord's existing support for medical, computer vision, and video data. Also: I've tested a lot of AI tools for work. These 4 actually help me get more done every day By now, AI chatbots and image generators are relatively commonplace. But it's much harder to generate convincing video or audio than it is to generate text. The AI industry is focused increasingly on multi-modal capabilities, especially with the release of features like ChatGPT's Voice Mode. To fine-tune an AI model, you need quality -- and sometimes hyper-specific -- data. Text-based data doesn't provide the nuance these complex models need, and accuracy is even more important in high-stakes contexts like medicine. Builders need platforms that can annotate and evaluate all kinds of data -- video, audio, images, graphs, reports, retail listings, PDFs, and more, ideally in one place. Several of Encord's clients use the platform for medical images like MRI scans to develop better models for assisting doctors. Having high-quality, well-annotated audio data helps build speech and emotion recognition models, and can even identify sounds. Video and audio AI products need increasingly sophisticated data support to achieve a human-like realism, whether in transcription or lip-syncing accuracy. For example, the AI text-to-video platform Synthesia uses Encord to develop training models for its lifelike AI avatars. Encord's update includes new annotation and curation features for documents, audio files, vision, and medical data. With multimodal annotation, AI teams can customize an interface to review and edit different file types side by side. Currently, different data types often are siloed across multiple services and platforms, adding time and costs to data annotation. Encord already supports key data annotation categories such as entity recognition, translation, summarization, text classification, and sentiment analysis. "It is time-consuming and often impossible for teams to gain visibility into large-scale datasets throughout model development due to a lack of integration and consistent interface to unify these siloed tools," the company said in the release. Also: Organizations face mounting pressure to accelerate AI plans, despite lack of ROI With Encord, AI teams can filter through their data to identify and curate exactly what they need to build a model. Its evaluation dashboard can also flag data that's hampering a model's performance so that teams can remove or replace it. "On average, Encord customers use 35% smaller data sets, which leads to models performing 20% more accurately," an Encord rep told ZDNET via email. In a demo, Encord co-founder and president Ulrik Stig Hansen told ZDNET that he sees the company's focus on quality and centralization as eventually enabling artificial general intelligence (AGI).

ZDNet

Thu, 14 Nov, 8:16 PM UTC

SuperAnnotate wants to help companies manage their AI data sets

High-quality data may be the key to high-quality AI. With studies finding that data set curation, rather than size, is what really affects an AI model's performance, it's unsurprising that there's a growing emphasis on data set management practices. According to some surveys, AI researchers today spend much of their time on data prep and organization tasks. Brothers Vahan Petrosyan and Tigran Petrosyan felt the pain of having to manage lots of data while training algorithms in college. Vahan went so far as to create a data management tool during his Ph.D. research on image segmentation. A few years later, Vahan realized that developers -- and even corporations -- would happily pay for similar tooling. So the brothers founded a company, SuperAnnotate, to build it. "During the explosion of innovation in 2023 surrounding models and multimodal AI, the need for high-quality datasets became more stringent, with each organization having multiple use cases requiring specialized data," Vahan said in a statement. "We saw an opportunity to build an easy-to-use, low-code platform, like a Swiss Army Knife for modern AI training data." SuperAnnotate, whose clients include Databricks and Canva, helps users create and keep track of large AI training data sets. The startup initially focused on labeling software, but now provides tools for fine-tuning, iterating and evaluating data sets. With SuperAnnotate's platform, users can connect data from local sources and the cloud to create data projects on which they can collaborate with teammates. From a dashboard, users can compare the performance of models by the data that was used to train them, and then deploy those models to various environments once they're ready. SuperAnnotate also provides companies access to a marketplace of crowd-sourced workers for data annotation tasks. Annotations are usually pieces of text labeling the meaning or parts of data that models train on, and serve as guideposts for models, "teaching" them to distinguish things, places and ideas. To be frank, there are several Reddit threads about SuperAnnotate's treatment of the data annotators it uses, and they aren't flattering. Annotators complain about communication issues, unclear expectations, and low pay. For its part, SuperAnnotate claims it pays fair market rates and that its demands on annotators aren't outside the norm for the industry. We've asked the company to provide more detailed information about its practices and will update this piece if we hear back. There are several competitors in the AI data management space, including startups like Scale AI, Weka and Dataloop. San Francisco-based SuperAnnotate has managed to hold its own, however, recently raising $36 million in a Series B round led by Socium Ventures, with participation from Nvidia, Databricks Ventures, Play Time Ventures and Defy.vc. The fresh capital, which brings SuperAnnotate's total raised to just over $53 million, will be used for augmenting its current team of around 100, for product R&D, and for growing SuperAnnotate's customer base of roughly 100 companies. "We aim to build a platform capable of fully adapting to enterprises' evolving needs and offering extensive customization in data fine-tuning," Vahan said.

TechCrunch

Mon, 18 Nov, 4:12 PM UTC

Top Free AI Tools for Data Labeling

Annotate your data accurately with SuperAnnotate, CVAT, and more Data labeling is essential to the development process of machine learning models. It ensures data accuracy by providing properly annotated datasets. The rapid advancement in AI technology has made it easier for businesses, developers, and researchers to advance in data labeling without incurring huge costs. AI tools for data labeling offer a range of features from text and image annotation to audio and video labeling. This helps minimize effort and save time and costs resulting in an enhanced quality of your machine-learning projects.

Analytics Insight

Wed, 2 Oct, 10:03 AM UTC

Top Data Annotation Tools for Computer Vision Projects

Choose the right platform for your specific needs and build better datasets faster Data is the backbone of every successful computer vision project. But raw data alone won't build a smart model. It needs to be annotated, i.e., carefully labeled so that machines can learn to "see" like humans. This process is what transforms gigabytes of images and videos into valuable training datasets for AI. The challenge? Manual annotation is time-consuming and tedious. Without the right tools, it's like navigating a maze blindfolded. That's where data annotation platforms step in, offering streamlined workflows, automation, and quality assurance. Selecting the right tool can make or break your project. Whether it's recognizing pedestrians in a bustling cityscape or detecting anomalies in a medical scan, choosing the right annotation tool can accelerate development and ensure high-quality results. This article highlights the top data annotation tools designed for computer vision projects, diving into their key features, benefits, and best use cases.

Analytics Insight

Wed, 9 Oct, 12:16 PM UTC

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