$7.5M Raised By Ex-Apple Engineers For New Seattle Data Storage Startup

A new data storage startup in Seattle, founded by ex-Apple engineers, has raised $7.5M in funding which was led by Madrona Venture Group. This impressive funding for the new data storage startup clearly reflects that investors see a promising future in this initiative.

As businesses and individuals continue to generate an unprecedented amount of data, the need for efficient and reliable storage solutions has become more important than ever. The success of this startup displays that investors recognize the worth of data storage technology and also perceive future growth in this industry.

What Are Machine Learning And Artificial Intelligence Applications?

Machine learning and artificial intelligence applications in data storage refer to the use of algorithms and predictive models to analyze large amounts of data for insights, improve storage efficiency, and optimize performance.

These applications can help in data compression, deduplication, and data replication.

The use of these applications in data storage is revolutionizing the industry by improving efficiency, optimizing performance, and providing valuable insights to businesses and individuals who rely heavily on data storage solutions.

Investing in this new data storage startup means investing in the future of machine learning and artificial intelligence applications, which can bring innovation and cost-effective solutions.

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The Beginning Of XetHub

XetHub is a new data storage platform for programmers creating machine learning and artificial intelligence applications.

• It is founded by three former Apple employees Rajat Arya, Yucheng Low, and Ajit Banerjee.

• Seattle-based XetHub received $7.5 million in seed fundraising from Madrona Venture Group after exiting the stealth phase.

• In particular for teams that are working remotely, XetHub intends to assist developers in accelerating how they build and implement code for "intelligent" applications.

• Until now, only major companies used this AI-powered functionality, but more and more businesses are going to start using AI.

• XetHub is hoping that businesses would use its platform more effectively as these applications require more and more data.

• Legacy infrastructure and complex data procedures restrict the development and deployment of these apps, and XetHub addresses these issues from the developer's perspective.

• The XetHub platform enables collaboration between machine learning engineers and offers a "governance record" that keeps account of any modifications made to data.

• According to the company, the platform can presently handle datasets that are 1 TB in size and has plans to grow that capacity to 100 TB soon. Later in 2023, a version that is widely available will be released.

Differences Between GitHub And XetHub

• Xethub Is the new Startup data storage platform founded by ex-Apple engineers, while GitHub is a well-established and widely used platform for version control and collaborative software development.

• While both platforms provide data storage solutions, XetHub distinguishes itself with its innovative use of machine learning and artificial intelligence applications for efficient and reliable data storage.

• XetHub offers a more intuitive user interface and advanced collaboration tools that simplify teamwork, as well as real-time code editing capabilities.

• XetHub offers more robust security features that ensure the safety of your code and projects.

• These features make XetHub an attractive alternative to GitHub and a platform that is worth considering for anyone looking for efficient and secure version control solutions.

• Users can cooperate online on software development with GitHub. Developers publish their written code to GitHub, allowing other developers to use it to follow the development of active projects and manage finished ones.

• Engineering teams can perform a similar action using XetHub, but for data storage in addition to code in their Git repository.

• In contrast with GitHub, XetHub has been designed from the ground up to take full advantage of advanced machine learning and artificial intelligence technologies that can optimize data storage efficiency and provide valuable insights to clients.

• Investing in XetHub offers a unique opportunity to be at the forefront of cutting-edge technology in data storage, while GitHub relies on traditional methods.

• Therefore, for those looking to invest in the future of data storage and machine learning technology, XetHub presents a compelling opportunity that should not be overlooked.

About The Founders

1. CEO Yucheng

CEO Yucheng Low, who spent more than three years as Turi's principal architect before spending five years as an engineer at Apple, is in charge of the startup. He received his PhD in machine learning from Carnegie Mellon University, where he also graduated.

2. Arya

Arya served as Turi's director of technical sales before joining Apple as a system software engineer. He has also held positions at Microsoft and Amazon Web Services.

3. Ajit Banerjee

Ajit Banerjee, a former senior software architect at Apple, completes the founding team. An organisation named TalentWorks, which conducts employment interviews and matches, was co-founded by Banerjee.

The Competitors Of XetHub

There are many systems for data storage available to developers today. Git LFS, which allows huge dataset storage within Git, Iterative.ai, a well-funded collaborative machine learning platform, which released its first product, DVC Studio, in June 2021, and DoltHub, a SQL database that imitates Git's capabilities, will be competitors to XetHub.

According to Arya, the platform, received hundreds of signups only through word of mouth. The business collaborated with a "handful of teams across a couple industries" to use XetHub through a private beta over the six months prior to the public debut.

Furthermore, there is no doubt that data storage will continue to be a rapidly growing industry. Therefore, XetHub has an excellent opportunity to capitalize on the growth of data storage and machine learning for years to come.