Weights & Biases Weighs In With $1 Billion Valuation

<p><strong>SAN FRANCISCO<&sol;strong> &&num;8212&semi; Weights &&num;038&semi; Biases&comma; operator of a developer-first MLOps platform&comma; has closed a Series C funding round of more than &dollar;100 million giving the company a &dollar;1 billion valuation&comma; from new investors Felicis Ventures and BOND as well as existing investors Insight Partners and Coatue&period;<&sol;p>&NewLine;<p>Weights &&num;038&semi; Biases &lpar;W&&num;038&semi;B&rpar; provides a best-in-class developer-first MLOps platform to more than 100&comma;000 machine learning engineers and data scientists across the globe&period; Founded by repeat entrepreneurs Lukas Biewald&comma; Shawn Lewis&comma; and Chris Van Pelt&comma; W&&num;038&semi;B began as an experiment tracking solution and quickly expanded to other areas of the machine learning pipeline&comma; including dataset versioning&comma; model evaluation&comma; data visualization&comma; collaborative model training&comma; reproducibility and pipeline management&period; W&&num;038&semi;B is trusted by cutting-edge research organizations including OpenAI and Toyota Research Institute as well as category-leading companies like NVIDIA&comma; Facebook Research&comma; Graphcore&comma; Pandora&comma; and hundreds more who are on the forefront of commercializing the application of machine learning &lpar;ML&rpar;&period;<&sol;p>&NewLine;<p>&&num;8220&semi;Our ultimate goal is to make machine learning work in the real world with a platform that ML engineers love&comma;&&num;8221&semi; said Lukas Biewald&period; &&num;8220&semi;That means solving pain points in every production machine learning workflow&comma; encouraging best practices and helping teams work together more effectively&period; This funding allows us to continue building out our platform&comma; adding new and better functionality&comma; and scaling with the growing needs of the ML community&period;&&num;8221&semi;<&sol;p>&NewLine;<p>As workflows become more complex and machine learning continues to enjoy widespread adoption&comma; W&&num;038&semi;B is in an ideal position to scale and adapt in tandem with the community&comma; providing the solutions practitioners and teams need to push state-of-the-art models into production more quickly and more confidently&period;<&sol;p>&NewLine;<p>&&num;8220&semi;It&&num;8217&semi;s rare to see a company that has both explosive bottoms-up adoption and incredible enterprise customers as partners&comma;&&num;8221&semi; said Aydin Senkut&comma; Founder and Managing Partner at Felicis Ventures&period; &&num;8220&semi;It&&num;8217&semi;s impressive to see a major NVIDIA commercial partnership one day&comma; followed by an academic mention in Nature the next&period; Weights &&num;038&semi; Biases does an exceptional job supporting the next generation of ML practitioners&comma; whether in academia&comma; research organizations&comma; or in industry&period; They are poised to be a de facto component in the pipelines of the next big breakthroughs in machine learning&period;&&num;8221&semi;<&sol;p>&NewLine;<p>&&num;8220&semi;To say that W&&num;038&semi;B&&num;8217&semi;s MLOps platform is impressive is a massive understatement&comma;&&num;8221&semi; said George Mathew&comma; Managing Director at Insight Partners&period; &&num;8220&semi;As machine learning continues to be rapidly adopted and leveraged globally by ML practitioners and data scientists&comma; the need for better collaboration on models is increasingly apparent&period; W&&num;038&semi;B exceeds this need with its category-leading platform focused on hyper-parameter tuning and experiment tracking&period; Insight is thrilled to continue our partnership with W&&num;038&semi;B with this reinvestment and we look forward to powering the team through this exciting next phase of their ScaleUp journey&period;&&num;8221&semi;<&sol;p>&NewLine;<p>&&num;8220&semi;Weights &&num;038&semi; Biases solves a mission-critical pain point for machine learning practitioners&comma;&&num;8221&semi; said BOND partner Jay Simons&period; &&num;8220&semi;By helping users better track&comma; iterate and share model experiments&comma; W&&num;038&semi;B has not only addressed this point&comma; but created a wedge to expand into additional product categories&period;&&num;8221&semi;<&sol;p>&NewLine;<p>&&num;8220&semi;For us&comma; Weights &&num;038&semi; Biases was a game changer&comma;&&num;8221&semi; said Heinrich Küttler&comma; a Research Engineer at Facebook AI Research &lpar;FAIR&rpar;&period; &&num;8220&semi;No other MLOps tool available allows for rapid iteration of AI experiments with the same ease of sharing results&comma; annotating interesting behavior and long-term storage of logging data&period; When any issues arose&comma; we found the support team at Weights &&num;038&semi; Biases to be quick and helpful&excl;&&num;8221&semi;<&sol;p>&NewLine;<p>&&num;8220&semi;Weights and Biases moved the AI field from traditionally babysitting a single experiment to managing multiple experiments across many teams spanning entire companies&&num;8221&semi;&comma; said Wojciech Zaremba&comma; co-founder of OpenAI&period; &&num;8220&semi;Collaboration and sharing of scientific insights and results are central tenets of AI today&comma; and only grows more prevalent each day&period; We are limited as individuals&comma; and can only overcome this weakness together&period; Thank you W&&num;038&semi;B for making us work together&period;&&num;8221&semi;<&sol;p>&NewLine;

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