Datatron: Enabling Model Management at Scale

Datatron

Lakshmi Randall, VP, Head of Product Marketing, DatatronLakshmi Randall, VP, Head of Product Marketing
Recent advancements in AI and ML have put the business world face-to-face with the new age of automation. With their new capabilities, computers can perform a range of routine physical tasks better and more cost-effectively than humans. Moreover, they are increasingly capable of accomplishing activities that include cognitive operations, which were once considered too difficult to automate successfully. While AI and ML are incredibly powerful technologies, they introduce a new type of risk associated with data volume, privacy, and complexity. A poorly built ML model can be detrimental to business outcomes, lead to terrible customer experiences, and may pose compliance risks.

Consequently, since deploying ML models is a very manual, complicated, and time-consuming task, organizations require an efficient model operations and governance platform. Datatron, a machine learning company, offers a single platform for model operations and governance to organizations looking toward implementing ML, AI, and data science models in production. The company helps its clients automate and scale model operations to ensure that they are running smoothly and efficiently in the production environment and achieving the intended business goals.

In an interview with CIO Applications, Lakshmi Randall, VP, head of product marketing at Datatron, sheds her insights on the challenges associated with deploying and managing ML models and how their platform helps organizations mitigate the same. Lakshmi is an experienced marketing leader who analyzes and responds to emerging, disruptive industry trends to drive company revenue and market share.

According to you, what are some of the trends that you see in the AI&ML space?

The entire AI and ML market is improving and maturing at an unprecedented rate. Organizations are identifying new business use cases and applying ML to automate or semi-automate various manual business processes. However, when it comes to moving ML models into production, there are many inherent challenges. Today, it is relatively straightforward for a data scientist to build an AI/ML model, especially with the proliferation of open-source tools. The challenges arise when organizations need to deploy many models, many of which need to be categorized in groups that need to work in concert. For that reason, they lose the ability to innovate business processes like underwriting, claims processing, and more. This is where Datatron can play a key role by helping companies realize the true value of their investments made on ML. We enable them to operationalize the models and embed them in their business processes. We also provide governance capabilities and help organizations achieve compliance with the enterprise's policies.

Despite many technological advances, most businesses today still operate by a form of “gut thinking.” As organizations evolve, they will need broader and deeper kinds of insights, and these will be provided by AI/ML

Could you walk us through Datatron’s offerings?

Datatron offers a comprehensive ML model operations and governance platform for managing, monitoring, and governing models in production. The platform also provides analytic leaders, business stakeholders, and data scientists a birds-eye, multi-level view of how their models perform in production via the smart visualization of key metrics. Datatron ensures that models are continually managed and appropriately governed, especially since further AI/ML adoption and acceptance depend on trust, transparency, and validation. With our governance dashboard, we enable companies to report model governance through KPIs with a bird’s eye view into all models in production, relevant stakeholders can be immediately apprised of any model behavior that could raise a red flag.

Could you walk me through a typical client onboarding process?

As our product is model-agnostic, customers can deploy the models - built using any choice of framework -to our platform. We provide a point-and-click type of interface to put ML models into production. We have successfully deployed our solution for diverse use-cases like improved labor scheduling, streamlined vehicle routing, and risk management. With our enterprise-grade platform, customers can quickly automate and standardize model deployment and management.

Could you highlight some of the benefits Datatron brings to its customers?

From a business perspective, we improve productivity significantly. As we automate deployment, monitoring, governance, and validation of all models, we reduce the efforts required by data scientists and other scarce IT resources, resulting in reduced time to market and reduced cost. We help our clients realize the value of their AI&ML implementations while ensuring that they follow good governance.

Companies that use homegrown solutions to operationalize models may be losing both time and money. Organizations cannot always maintain a homegrown tool to keep it up-to-date. Instead, they can embrace an automated platform like Datatron to realize the benefits quickly. When it comes to model risk management, we strive to provide transparency, fairness, accountability, and interpretability. For these reasons, we enable organizations to self-regulate their ecosystems.

What does the future hold for Datatron?

More regulations and policies governing technologies and industry sectors that are enabled by AI and ML are imminent. Companies will need to balance innovation and compliance with the regulations in the days to come. By ensuring that models operate risk-free and bring better transparency, companies can reap the benefits of AI and ML in the long run.

Our mission is to make sure that the Datatron platform continues to support new regulations while concurrently innovate with AI/ML.

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