Verta
Pioneering The Operational AI Space

Companies, from startups to global enterprises, are finding increasingly innovative ways to incorporate artificial intelligence (AI) and machine learning (ML) into their products and services. But for most organizations, these efforts boil down to staffing their data science teams and investing in various tools for experimentation and training for building ML models.

Many organizations have not invested in technology tooling and infrastructure to quickly and easily package the ML models and safely deploy them into products and processes for bringing tangible benefits to businesses and customers.

This is where Verta makes the difference.

Its platform operationalizes AI, efficiently and at scale, while maintaining guardrails for safety and compliance. This allows companies to translate their ML research into models that drive business value.

Effectively and Efficiently Operationalizing AI/ML Models

Verta offers two key solutions—an Operational AI platform and an Enterprise Model Management system.

The Verta Operational AI platform is the underlying ML infrastructure for efficiently and safely putting models into production. The platform lets data scientists quickly package and deploy their models with built-in DevOps support for CI/CD—what customers call ‘DevOps in a box.’ It guides data scientists through a series of steps with configurable checklists to ensure that the proper process is followed and the necessary “safety checks” are completed before a model is deployed—which is why Verta talks about “speed with safety.” As a result, an organization can ship products ten times faster, accelerate model velocity by 30 times, and detect model quality issues 100 times faster.

“We continuously serve models into production with our closedloop management system—like a control tower for ML models— giving visibility into clients’ entire model portfolio,” says Manasi Vartak, founder and CEO of Verta.

The Verta Enterprise Model Management system brings clients’ ML models under one roof within a centralized AI/ML control center, adds governance to them, and orchestrates their deployments to different platforms. A data scientist can publish all relevant information about their model into the system, like a catalog, and authorized users with proper access can search the catalog to find the right model. This facilitates improved collaboration across different business lines or data science teams and a new level of executive insight into the value that AI/ML investments deliver to the business.

For example, product managers can see which models can be reused in other parts of the organization, or DevOps can find models with live deployments. The system consolidates model documentation, such as production checklists and reproducibility, and makes the documentation accessible to authorized colleagues across the organization. It tracks all activity related to a model, enabling data scientists to quickly see if the model was recently changed or when documentation was updated. Risk teams can also conduct audits efficiently by filtering for critical events or downloading the whole log.

The system provides detailed, enterprise-wide visibility into an organization’s model portfolio. They get a clear understanding of the performance of the models and versions in production. This improves governance and reduces regulatory and legal risks for businesses over issues like bias, privacy, and security. It also enables and promotes sharing, reuse, and discovery of models so that different data science teams don’t have to redo the work done elsewhere in the organization. And finally, the system provides executive visibility into the business value the models bring.

“From The Beginning, We Developed Verta’s Operational AI Platform And Enterprise Model Management System To Help Any Company Achieve The Same Kind Of High-Impact Data Science And ML That Our Team Members Delivered In Their Past Work At AI-Forward Tech Giants”

Focus on AI/ML Delivery and Management

Verta’s unique focus and perspective on the AI/ML space revolves around understanding the challenges of delivering AI/ML in real time and at scale. What enables the company to conduct this with utmost efficiency is the founders’ experience in building and running operational AI platforms for some of the largest AI-forward organizations, including Facebook, Twitter, Google, and NVIDIA. They founded Verta, leveraging their deep data science and operational expertise in order to bring model velocity and model serving at scale to the broader AI/ML community.

“From the beginning, we developed Verta’s Operational AI platform and Enterprise Model Management system to help any company achieve the same kind of high-impact data science and ML that our team members delivered in their past work at AI forward tech giants,” says Vartak, who created ModelDB at MIT CSAIL, which later became the basis for the open-source MLflow experiment management product.

Scaling AI in the Enterprise

Verta is known for its enterprise readiness and the ability to deploy into and integrate with diverse IT ecosystems and seamlessly work with any model across any computing environment— on-prem, cloud, hybrid, or multi-cloud. It can also handle traditional batch workloads and support real-time use cases at an enterprise scale, which is increasingly essential given the sharp increase in the use of real-time ML. These capabilities uniquely position Verta to deliver Operational AI and Enterprise Model Management at scale for a diverse set of Fortune 500 companies, enabling them to drive greater model velocity and value from their model assets.

We Continuously Serve Models Into Production With Our Closed-Loop Management System— Like A Control Tower For ML Models— Giving Visibility Into Clients’ Entire Model Portfolio

The AI/ML space is moving at a breathtaking pace, and Verta is constantly current with the latest developments by working closely with customers to understand their challenges and how they can enable the next set of workloads. Its customer advisory panel has been an insightful source to help customers improve the value they are getting from their AI/ML investments. Verta also has thought leaders who participate in industry events and collaborate with industry practitioners and academics to understand the latest innovations.

Verta has been acknowledged as a leading innovator through recognition in industry media lists of top companies in this space, and Gartner included it in its list of “Cool Vendors in AI Core Technologies—Scaling AI in the Enterprise” in 2022, as well as its latest report on AI TRiSM (Trust, Risk and Security Management).

A Robust Roadmap for Operational AI and Enterprise Model Management.

Verta is focused on expanding its core capabilities around Operational AI and Enterprise Model Management to support customers’ evolving requirements. At the same time, it is aware that all companies leveraging AI/ ML, regardless of their industry, are facing a range of market forces that are challenging their approach to machine learning. An increasing number of AI regulations are being passed that will require new levels of reporting and accountability for ML models and their impact. ChatGPT, which burst into the public consciousness late last year, is already prompting organizations to weigh how they can incorporate generative AI into their products, processes, and services.

Verta has a robust roadmap for its Operational AI and Enterprise Model Management capabilities to support companies in managing these new challenges and opportunities. The number of customers for its products will continue to grow, and the company is committed to meeting this growing market need.