{"id":1446,"date":"2022-06-14T15:56:51","date_gmt":"2022-06-14T23:56:51","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?post_type=press_release&p=1446"},"modified":"2022-06-14T15:56:51","modified_gmt":"2022-06-14T23:56:51","slug":"comet-expands-alliances-across-the-complete-machine-learning-lifecycle","status":"publish","type":"press_release","link":"https:\/\/www.comet.com\/site\/about-us\/news\/press-releases\/comet-expands-alliances-across-the-complete-machine-learning-lifecycle\/","title":{"rendered":"Comet Expands Alliances Across the Complete Machine Learning Lifecycle"},"content":{"rendered":"

Comet<\/a>, provider of the leading development platform for machine learning (ML) teams from startup to enterprise, today announced several integrations \u2013 including Ray, Kubeflow, and Google Vertex AI \u2013 that will eliminate complexity, provide optionality, and bolster scalability across the entire machine learning lifecycle.<\/p>\n

These integrations deliver on Comet\u2019s commitment to providing a ML platform that allows data scientists and ML practitioners to use the best tools and frameworks available for their use cases, industries, and business challenges from experimentation through production.<\/p>\n

Comet\u2019s unique approach to integration comes at a time when more organizations are pursuing the promise of artificial intelligence (AI) while their development teams are increasingly encountering friction between tools and among teams<\/a>. This friction is the result of a rapidly evolving MLOps technology landscape and the lack of processes that support the burgeoning needs of companies looking to generate value from AI.<\/p>\n

Too often, teams in fast-growing organizations become siloed, and their use of tools and processes become even more disconnected and inefficient. Comet addresses this issue through strategic integrations, making it seamless for teams to utilize the tools that best fit their specific business requirements, integrated within a single platform. This approach reduces the friction, allowing teams to reduce the time to achieve value from their AI initiatives, and enables new levels of management and insight, fostering better collaboration across teams.<\/p>\n

\u201cIn this early adoption phase of AI\/ML software, we\u2019re seeing a massive proliferation of tools and platforms that were created to address the unique, highly specific issues of data science. While these are effective in solving pieces of the puzzle, there hasn\u2019t been a comprehensive solution. Enterprises are struggling to cobble together the dozens of different tools needed to make a complete ML platform as a result,\u201d said Daniel Jeffries, Managing Director of the AI Infrastructure Alliance (AIIA)<\/a>. \u201cFor ML to reach its potential, deeper interoperability between enterprise products is very much needed now. Tools that work together extremely well is the best approach to scaling ML for all.\u201d<\/p>\n

The Comet platform makes it possible for ML development teams to track, compare, debug, and monitor models from early experimentation all the way through production. It uniquely offers both standalone experiment tracking and model production monitoring, and its platform can run on any infrastructure, whether it is cloud, on-premises, or virtual private cloud (VPC). The latest integrations join what is already the most extensive list in the industry. In addition to Ray, Kubeflow, and Vertex AI, Comet enables customers to leverage the best frameworks, libraries, and compute resources \u2013 such as Amazon Sagemaker<\/a>, Catalyst<\/a>, GitLab<\/a>, Keras<\/a>, New Relic<\/a>, PyTorch<\/a>, Scikit-learn<\/a>, Spark NLP<\/a> and Tensorflow<\/a>.<\/p>\n

New Tools, Better Insights in One Comprehensive View<\/strong><\/p>\n

Comet\u2019s most recent integrations provide enterprises with necessary capabilities for developing a cohesive ML platform. For example:<\/p>\n