Maximizing Current Snowflake Investments
Some companies have spent vital cash on instruments to stay progressive and aggressive. Whereas this may be a superb technique for a future-oriented firm, it may show futile if you happen to 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 programs, information sources, and applied sciences inside an ecosystem might be troublesome and time-consuming, resulting in inefficiencies, information silos, damaged machine studying fashions, and locked ROI.
The DataRobot AI Platform and the Snowflake Knowledge Cloud present an interoperable, scalable AI/ML resolution and distinctive providers that combine with various ecosystems in order that data-driven enterprises can give attention to delivering trusted and impactful outcomes.
Extending Snowflake Integration: New Capabilities and Enhancements
To assist clients maximize their Snowflake funding, DataRobot is extending its Snowflake integration to assist clients rapidly iterate, enhance fashions, and full the ML lifecycle with out repeated configuration.
This contains:
- Supporting Snowflake Exterior OAuth configuration
- Leveraging Snowpark for exploratory information 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 in search of mature information science platforms that may scale to the dimensions of their complete enterprise. With the newest capabilities launched by DataRobot, clients can now assure the safety and governance of their information used for ML, whereas concurrently rising the accessibility, efficiency, and effectivity of knowledge preparation, mannequin coaching, and mannequin observability by their customers,” stated Miles Adkins, Knowledge Cloud Principal, AI/ML at Snowflake. “By bringing the unequalled AutoML capabilities of DataRobot to the information in Snowflake’s Knowledge Cloud, clients get a seamless and complete enterprise-grade information science platform.”
Full the Machine Studying Lifecycle, With out Repeated Configuration
Connecting to Snowflake
Connect with Snowflake by way of exterior identification suppliers utilizing Snowflake Exterior OAuth with out offering consumer and password credentials to DataRobot. Scale back your safety perimeter by reusing your current Snowflake safety insurance policies with DataRobot.
Study extra about Snowflake Exterior OAuth.
Exploratory Knowledge Evaluation
After we connect with Snowflake, we are able to begin our ML experiment.
We not too long ago introduced DataRobot’s new Hosted Notebooks functionality.
For our joint resolution with Snowflake, which means that code-first customers can use DataRobot’s hosted Notebooks because the interface and Snowpark processes the information straight within the information warehouse. This permits 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 will get pleasure from a hosted expertise with code snippets, versioning, and easy atmosphere administration for speedy AI experimentation.
Study extra about DataRobot hosted notebooks.
Mannequin Coaching
As soon as the information is ready, customers select their most popular strategy for mannequin growth utilizing DataRobot AutoML by way of the GUI, hosted Notebooks, or each.
When the coaching course of is full, DataRobot will advocate the best-performing mannequin for manufacturing primarily based on the chosen metric and supply an evidence.
Mannequin Deployment
Prospects want the flexibleness to deploy fashions into totally different environments. Deploying to Snowflake reduces infrastructure operations complexity, information switch latency and related prices, whereas bettering effectivity and offering close to limitless scale.
A brand new Snowflake prediction atmosphere configured by DataRobot will mechanically handle and management the atmosphere, 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 information for velocity and elasticity, whereas preserving information 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 clients preserve observe of their enterprise selections primarily based on predictions and precise information 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, information drift, and accuracy over time and will help decide mannequin accountability.
Study 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.
If you’re already a buyer of Snowflake and DataRobot, attain out to your account crew to rise up to hurry on these new options.
Getting Began with DataRobot AI and Snowflake, the Knowledge Cloud
DataRobot and Snowflake collectively supply an end-to-end enterprise-grade AI expertise and experience to enterprises by decreasing complexity and productionizing ML fashions at scale, unlocking enterprise worth. Study extra at DataRobot.com/Snowflake.
1 Supply: Flexera 2021 State of the Cloud Report
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