One of the most common challenges to AI adoption today is that too many projects don’t get completed and fail to deliver clear business results. After speaking with hundreds of our customers over the past year and further analyzing projects, we quickly realized that a new approach to AI was needed. To implement this new approach, which we call Value-Driven AI, we set out to design new and improved platform features that will enable customers to realize value faster.
Today, we want to share what we’ve learned and established as key requirements for an AI platform to consistently deliver value from investments in AI. We’re also excited to share the innovations and capabilities we’ve developed at DataRobot to meet and exceed those requirements.
Why model-based AI fails to deliver value
Teams that simply focus on model performance using model-centric and data-driven ML risk missing the big business picture. That focus often leads to excessive spin on building a better algorithm or neural network, or finding more data to improve model performance, as opposed to improving business performance. This narrow focus can lead to accurate and true insights that aren’t actually useful, leaving business stakeholders frustrated. What AI teams really need to do is think about the business problem first and use the tools to meaningfully engage with business stakeholders to ensure that the project does not meet expectations.
What do AI teams need to realize value from AI?
- Better ways to collaborate with experiments and businesses. AI teams need the right tools and processes to quickly iterate multiple ML problem statements, compare different approaches, groups and collaborate with SMEs in their business to learn and iterate model building simply and without huge manual effort. .
- Reliable and repeatable ways to scale production volumes within real-world constraints; To achieve sustainable value, teams must be able to get models and insights into production in front of decision-making users. This means they need tools that can help with model testing and documentation, automation across the pipeline, and they need to be able to seamlessly integrate the model into business-critical applications or workflows.
- Best practice compliance and management. Businesses need to know that their data scientists are presenting models they can trust and protect over time. This means proactively applying security best practices and applying the highest governance standards without slowing down the process.
- An AI platform that works well with a broad enterprise ecosystem. A platform that seamlessly integrates with enterprises’ already significant investments in infrastructure, practice tools, data platforms, and business applications.
- Expert advice for navigating the challenges and complexities of AI; AI Teams don’t have to go it alone when it comes to driving value. They need the right expertise at the right stage as they develop the AI maturity curve.
The DataRobot AI platform delivers value-based AI
In our new 9.0 DataRobot AI Platform release, we’ve broken down the barriers that exist in the ML lifecycle. We’ve removed complexity and simplified the end-to-end ML lifecycle so teams can collaborate easily, test quickly, and most importantly, produce any model quickly.
- Collaborative Experience – the new experience, called Workbench, is packed with new features such as new integrated data preparation for modeling and notebooks that provide first-hand experience with code. This helps teams collaborate on all ML assets in one place so they can test faster.
- Cost at production scale – DataRobot’s ML Production is more than just basic MLOps tooling, and now new features make it easier and faster to scale and maintain model work. The new GitHub Marketplace Action for CI/CD integrates DataRobot into your existing DevOps practice with customized inference metrics to track business performance., and an expanded suite of drift control features ensure models perform as expected.
- Guaranteed compliance and governance – DataRobot has always been strong in providing management. We’ve expanded our governance and compliance capabilities to support models built outside of Datarobot, with new compliance documentation for external models, MLflow experiment metadata integration, and bias mitigation capabilities to help teams monitor and control all of their AI artifacts.
- A wide ecosystem of enterprises – The DataRobot AI platform is an open system that supports key integrations to help businesses maximize the value of their existing investments. New Snowflake integrations and a joint SAP solution hardened data from the experimental to the deployment node. Meanwhile, new Kubernetes support standardizes and simplifies deployment. When it comes to platform deployment, customers get the widest range of infrastructure choices, whether it’s a platform deployment managed on-premise or in a public cloud VPC or fully managed multi-tenant SaaS and single-tenant SaaS; have an option that will meet all needs.
- Applied AI expertise – In addition to all the platform innovations, we’re also taking 1,000 years of AI implementation experience and packaging it in two new ways: our new DataRobot service packages to help our customers realize value within 90 days. and our new AI Accelerators, which are code-first, modular building blocks and solution templates for specific use cases designed to help you jump-start your AI projects and results.
Discover the new DataRobot AI platform
Dig deeper and explore our new product details on the site, and stay tuned as we continue the 9.0 blog series and dive deep into the new 9.0 features over the next few weeks. Or contact our team to schedule a demo for an in-depth look at our new features and much more.
We’re just getting started.
About the writer
Chief Product Officer at DataRobot
Venky Veeraraghavan leads DataRobot’s product team, where he leads the definition and delivery of DataRobot’s AI platform. Venky has over twenty-five years of experience as a product leader, having previously worked at Microsoft and early-stage startup Trilogy. Venky has spent over a decade building hyperscale BigData and AI platforms for the world’s largest and most complex organizations. He lives, hikes and runs in Seattle, USA with his family.
Meet Venky Veeraraghavan