Maximizing Current Snowflake Investments
Some companies have spent important cash on instruments to stay progressive and aggressive. Whereas this may be a wonderful technique for a future-oriented firm, it will probably show futile when you don’t maximize the worth of your funding. In response to Flexera1, 92% of enterprises have a multi-cloud technique, whereas 80% have a hybrid cloud technique.
Integrating totally different techniques, knowledge sources, and applied sciences inside an ecosystem could be troublesome and time-consuming, resulting in inefficiencies, knowledge silos, damaged machine studying fashions, and locked ROI.
The DataRobot AI Platform and the Snowflake Knowledge Cloud present an interoperable, scalable AI/ML answer and distinctive companies that combine with numerous ecosystems in order that data-driven enterprises can concentrate on delivering trusted and impactful outcomes.
Extending Snowflake Integration: New Capabilities and Enhancements
To assist prospects maximize their Snowflake funding, DataRobot is extending its Snowflake integration to assist prospects shortly iterate, enhance fashions, and full the ML lifecycle with out repeated configuration.
This consists of:
- Supporting Snowflake Exterior OAuth configuration
- Leveraging Snowpark for exploratory knowledge evaluation with DataRobot-hosted Notebooks and mannequin scoring.
- A seamless consumer expertise when deploying and monitoring DataRobot fashions to Snowflake
- Monitoring service well being, drift, and accuracy of DataRobot fashions in Snowflake
“Organizations are on the lookout for mature knowledge science platforms that may scale to the scale of their total enterprise. With the newest capabilities launched by DataRobot, prospects can now assure the safety and governance of their knowledge used for ML, whereas concurrently rising the accessibility, efficiency, and effectivity of information preparation, mannequin coaching, and mannequin observability by their customers,” mentioned Miles Adkins, Knowledge Cloud Principal, AI/ML at Snowflake. “By bringing the unequalled AutoML capabilities of DataRobot to the info in Snowflake’s Knowledge Cloud, prospects get a seamless and complete enterprise-grade knowledge science platform.”
Full the Machine Studying Lifecycle, With out Repeated Configuration
Connecting to Snowflake
Hook up with Snowflake by means of exterior id suppliers utilizing Snowflake Exterior OAuth with out offering consumer and password credentials to DataRobot. Cut back your safety perimeter by reusing your current Snowflake safety insurance policies with DataRobot.
Be taught extra about Snowflake Exterior OAuth.
Exploratory Knowledge Evaluation
After we connect with Snowflake, we will begin our ML experiment.
We not too long ago introduced DataRobot’s new Hosted Notebooks functionality.
For our joint answer with Snowflake, which means code-first customers can use DataRobot’s hosted Notebooks because the interface and Snowpark processes the info immediately within the knowledge warehouse. This enables customers to work with acquainted Python syntax that will get pushed all the way down to Snowflake to run seamlessly in a extremely safe and elastic processing engine. They’ll take pleasure in a hosted expertise with code snippets, versioning, and easy setting administration for speedy AI experimentation.
Be taught extra about DataRobot hosted notebooks.
Mannequin Coaching
As soon as the info is ready, customers select their most well-liked strategy for mannequin growth utilizing DataRobot AutoML by means of the GUI, hosted Notebooks, or each.
When the coaching course of is full, DataRobot will suggest the best-performing mannequin for manufacturing based mostly on the chosen metric and supply a proof.
Mannequin Deployment
Prospects want the flexibleness to deploy fashions into totally different environments. Deploying to Snowflake reduces infrastructure operations complexity, knowledge switch latency and related prices, whereas bettering effectivity and offering close to limitless scale.
A brand new Snowflake prediction setting configured by DataRobot will robotically handle and management the setting, together with mannequin deployment and alternative.
When deploying a DataRobot mannequin to Snowflake, this new seamless integration considerably improves the consumer expertise, reduces effort and time, and eliminates consumer errors.
The automated deployment pushes educated fashions as Java UDFs, operating scalable inference inside Snowflake, and leveraging Snowpark to attain the info for pace and elasticity, whereas preserving knowledge in place.
Mannequin Monitoring
Inside and exterior elements have an effect on fashions’ efficiency.
The brand new monitoring job functionality is run seamlessly from the DataRobot GUI helps prospects hold observe of their enterprise selections based mostly on predictions and precise knowledge adjustments and govern their fashions at scale.
Over time fashions degrade and require alternative or retraining. The DataRobot MLOps dashboards current the mannequin’s well being, knowledge drift, and accuracy over time and can assist decide mannequin accountability.
Be taught extra concerning the new monitoring job and automated deployment.
There’s extra coming
We now have extra thrilling capabilities to share, many associated to the Snowflake integration, which can be introduced on the DataRobot 9.0 launch occasion on March sixteenth. Register right here to be a part of this digital occasion.
In case you are already a buyer of Snowflake and DataRobot, attain out to your account staff to rise up to hurry on these new options.
Getting Began with DataRobot AI and Snowflake, the Knowledge Cloud
DataRobot and Snowflake collectively provide an end-to-end enterprise-grade AI expertise and experience to enterprises by lowering complexity and productionizing ML fashions at scale, unlocking enterprise worth. Be taught extra at DataRobot.com/Snowflake.
1 Supply: Flexera 2021 State of the Cloud Report
Concerning the creator
World Technical Product Advocacy Lead, DataRobot
Atalia Horenshtien is a World Technical Product Advocacy Lead at DataRobot. She performs a significant position because the lead developer of the DataRobot technical market story and works carefully with product, advertising, and gross sales. As a former Buyer Going through Knowledge Scientist at DataRobot, Atalia labored with prospects in several industries as a trusted advisor on AI, solved advanced knowledge science issues, and helped them unlock enterprise worth throughout the group.
Whether or not chatting with prospects and companions or presenting at business occasions, she helps with advocating the DataRobot story and undertake AI/ML throughout the group utilizing the DataRobot platform. A few of her talking classes on totally different matters like MLOps, Time Collection Forecasting, Sports activities tasks, and use circumstances from varied verticals in business occasions like AI Summit NY, AI Summit Silicon Valley, Advertising AI Convention (MAICON), and companions occasions corresponding to Snowflake Summit, Google Subsequent, masterclasses, joint webinars and extra.
Atalia holds a Bachelor of Science in industrial engineering and administration and two Masters—MBA and Enterprise Analytics.
