By John P. Desmond, AI Trends Editor
The AI Infrastructure Alliance is taking shape, adding more partners who sign up to the effort to define a “canonical stack for AI and Machine Learning Operations (MLOps).” In programming, “canonical means according to the rules,” from a definition in webopedia.
The mission of the organization also includes, according to its website: develop best practices and architectures for doing AI/ML at scale in enterprise organizations; foster openness for algorithms, tooling, libraries, frameworks, models and datasets in AI/ML; advocate for technologies, such as differential privacy, that helps anonymize data sets and protect privacy; and work toward universal standards to share data between AI/ML applications.
Core members listed on the organization’s website include Determined AI, an early stage company focused on improving developer productivity around machine learning and AI applications, improving resource utilization, and reducing risk.
The determined.ai team encompasses machine learning and distributed systems experts, including key contributors to Spark MLlib, Apache Mesos, and PostgreSQL; PhDs from UC Berkeley and University of Chicago; and faculty at Carnegie Mellon University. Investors include GV (formerly Google Ventures), Amplify Partners, CRV, Haystack, SV Angel, The House, and Specialized Types. Founded in 2017, the company has raised a total of $13.6 million so far, according to Crunchbase.
Determined CEO Evans Says AI Stack “Needs to be Defined”
“At Determined, we have always been focused on democratizing AI, and our team remains incredibly optimistic about the future of bringing AI-native software infrastructure to the broader market,” said Determined Cofounder and CEO Evan Sparks, in an email response to a query from AI Trends on why the company joined the alliance. “This same mindset led us to open source our software last year in order to reach more teams across industries. As software becomes increasingly powered by AI, we think that the infrastructure stack to support developing and running software needs to be defined.”
He felt the challenge was too big for one company. “It’s going to take multiple companies solving different problems on the way as AI applications move from R&D into production, working together to define interfaces and standards to benefit data scientists and machine learning engineers. The AI Infrastructure Alliance is poised to be a powerful force in making this a reality.”
Asked why the mission of the AI Infrastructure Alliance is important, Sparks said, “In order to see the true potential of AI, AI development needs to be as accessible as software development, with little to no barriers to adoption. At Determined, we
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