The context and purpose behind this series: “The 17 Companies Reshaping the Landscape of Enterprise AI“.
Who They Are
Founded in 2012, DataRobot launched itself into the artificial intelligence arena and is now serving customers in multiple industries. As a result, it’s delivering over a trillion predictions for companies globally.
The backbone of DataRobot is “the transformational power of AI“. They are delivering on that notion by having the “world’s only AI Cloud platform combined with an AI-native strategic success team“.
Headquartered in Boston, MA, and led by CEO Dan Wright, July 2021 was a pivotal moment for DataRobot. First, they announced that they raised $300 million in funding which led to a $6.3 billion valuation.
Second, DataRobot announced the acquisition of Algorithmia an MLOps Enterprise Platform. This positions DataRobot as a strong AI and ML player with an enterprise-grade security and governance model.
Lastly, the culture of DataRobot shines through all they do. This strengthens the core mission to empower people to “be at the forefront of the Fourth Industrial Revolution”.
What They Do
Recently, DataRobot AI Cloud platform was launched which is described as “a single system of record, accelerating the delivery of AI to production for every organization.”
But, the AI Cloud is more than just a platform. It’s built on three foundational pillars of Trusted AI that define the purpose of DataRobot.
The Ethics Pillar of Trusted AI
There is no one-size-fits-all approach to AI ethics. Find out how to make sure an AI system reflects your organizational values.
The Performance Pillar of Trusted AI
Trustworthy AI requires accurate, stable, and robust model performance, built on meaningful and high-quality data.
The Operations Pillar of Trusted AI
An AI system is more than just a model. When embedding trust in your AI, you also have to take into account the infrastructure of software and people that operationalize a model.
DataRobot AI Cloud Platform Breakdown
The AI Cloud is constructed to be resilient across the full machine learning operations lifecycle. Additionally, it can run anywhere based on your unique needs for public cloud, private cloud, or at the edge.
AI Cloud Capabilities
- AI Catalog: Find, manage and share your AI data assets in one place
- Data Connection Library: Connect to a wide variety of data sources and formats
- Code-Centric Data Pipelines: Build and run sophisticated data pipelines in the languages you love
- Exploration & Visulization: Explore and visualize data to uncover new patterns and insights
- Composable ML: Your expertise augmented with world-class automation
- Visual AI: Automated computer vision to see the big picture
- Text-Aware AI: Natural language processing to extract meaning from text
- Location AI: Add geospatial context to your machine learning models
- Deep Learning: Practical deep learning approaches that are 100% ready for production
- Eureqa Models: Solve sophisticated problems with human-readable mathematical formula
- MLOps Agents: Deploy, monitor, and manage any model in any location
- Portable Prediction Servers: Easy-to-use Docker containers to host production models
- Champion Challengers: Test alternative strategies to challenge your production models
- Model Registry: A single hub to stage and manage all your production models
- No Code App Builder: Create stunning AI apps to unlock the value of AI
- AI Apps: Put the power of AI in the hands of decision makers
- Decision Flows: Create rules to automate your decisions
- Humble AI: Define rules to keep your production models in check
- Model Grader: Grade your existing AI models using our trusted AI framework
- AI Governance: Set up policies, rules and controls for your production deployments
- Compliance Documentation: Automatically generated AI reports and compliance documentation
Most Unique / Impactful Application
The growth of AI is unmistakable. For example, the Artificial Intelligence as a Service market is expected to hit about $77 billion by 2025.
However, the balance between people and AI is what DataRobot has created with its Augmented Intelligence. This is the unifying of the combined strengths of humans and machines while maintaining the speed and efficiency of a computational approach.
The goal is to truly unlock the value of AI while celebrating human expertise. All of which leads to more than just intelligent or faster decisions but the right decision.
Who They Have Impacted
The experience of a farmer’s market is amazing: fresh produce, support of local producers, and connecting with people in the community.
However, trying to consistently have a wide variety of products at peak freshness is a huge challenge. And, for the Australian-based Harris Farm Markets and their over two dozen stores, they needed a predictable way to determine the demand.
Founded in 1971 by David and Cathy Harris, the core sustainability principles of Driven by Nature, Delivered with Joy, Done with Integrity, and Always Fair feed into their motto: We’re for the greater goodness.
Harris Farms needed to address growing demand and forecast their needs in an unpredictable environment. They turned to DataRobot to assist with managing about 20,000 SKUs out of which 1,200 represent fresh produce.
From this need, DataRobot collaborated with Harris Farms which resulted in:
- 400 deployments for demand
- 30 for customer-number forecasts
- 25 individual models used for hourly numbers
- 5 clustered models for daily numbers
All of these capabilities now help Harris Farms to not only predict produce needs but to understand customer behaviors. And, this garnered them a tenfold capacity in their resources leading to much more accurate purchasing of perishable inventory.