Today Verta, the AI/ML model management and operations company, formally launched with its product that helps data science teams bring order to the chaos of sprawling enterprise machine learning environments. Used by well-known global brands, including one of the world's leading workplace collaboration tools, Verta helps enterprise data science teams standardize otherwise fragmented workflows in order to ship models more frequently with full traceability and low overhead.

"Data science teams live in their own specialized world, working with data, running experiments and building great models. The software deployment teams that take those models and use them to power production applications have a completely different focus and set of trusted tools. Forcing these teams to learn each other's tools is a distraction neither of them need. With Verta, we help each team to stay focused on what they do best," said Manasi Vartak, founder and CEO of Verta.

Vartak, who has worked at Facebook, Google, Microsoft and Twitter in software engineering and machine learning roles, created ModelDB, the first open source modern model management system, during her PhD at MIT's CSAIL lab. ModelDB is widely used in the industry and Fortune 500 companies today and forms the backbone of Verta.

Mike Leone, senior analyst at ESG, said: "As the AI/ML market expands rapidly, data science teams are becoming overburdened. They're tasked with solving strategic business problems, but they're bogged down by several non-data science tasks: data integration, data quality, cumbersome checkpoints; governance/compliance; learning intricacies of technology in the operational stack, like Kubernetes. This is where Verta can help by taking on the AI operational burdens data science teams face and enabling them to concentrate on strategy, innovation and what they were hired for: data science."

Verta interoperates with the rich variety of tools and workflows used by data science and machine learning teams-including TensorFlow, PyTorch, Spark.ml and R-allowing them to stay productive using the approaches they determine best suit their needs. Verta's support for the rapidly changing landscape ensures data science teams can continue to innovate quickly, rather than wasting time supporting a brittle, home-grown patchwork of systems that need constant care. Verta's MLOps capabilities have been designed to be compatible with trusted application platforms such as Kubernetes, helping enterprise infrastructure teams to support model-based applications with well-established methods and tools.

"Before Verta, it used to take us about six months to deploy a new model into production. This wasn't delivering value to customers fast enough. With Verta, we can deploy models multiple times every month, so effectively ten times faster, and with a lot less overhead for our team," said Jenn Flynn, senior data scientist at LeadCrunch.ai. "Verta lets us focus on the data science without worrying about infrastructure and operations."

For enterprises with large-scale model management needs, Verta's ModelDB-based model catalog and governance mechanisms enable them to keep track of model designs, deployment approvals and full traceability audits to provide the highest levels of assurance demanded by the toughest global regulations. Customers can design and deploy models with confidence, trusting that Verta's visibility and reporting functions will show them what they need to know, when they need it.

Verta's model monitoring capabilities help data science teams ensure their models remain accurate and that their intelligent products keep providing value to customers. By monitoring model performance, data drift and service levels across deployment environments-including Verta's Inference Engine, AWS SageMaker, and other domain-specific systems-Verta ensures model-based applications continue to perform at their best, and can be rapidly refreshed as and when needed. Data science teams can monitor the performance of their models, and ensure they are well-informed of the state of production applications at all times. IT teams also benefit from Verta's integrations with standard monitoring tools, ensuring they see the views they need in the formats they prefer.

Today Verta also announced $10 million in Series A funding led by Intel Capital with participation from General Catalyst who led the seed round.

Mark Rostick, VP and senior managing director at Intel Capital, said: "Verta is addressing one of the key challenges companies face when adopting AI - bridging the gap between data scientists and developers to accelerate the deployment of machine learning models. Companies need a solution that solves the DevOps part, deploys ML models into production, monitors the ML model performance and accuracy, applies governance and supports reproducibility. Verta connects data scientists, DevOps and production engineers and enables them to more quickly and easily create and deploy efficient ML solutions that give their companies a competitive edge."

Steve Herrod, Managing Director at General Catalyst, said: "AI/ML is such a hot area, but there are so few companies focusing on the challenges faced by organizations running intelligent products in production. Developing theoretical models is one thing but turning them into products that deliver real value to customers is quite another. I'm excited by Verta's focus on the part of the market with all the value."