Maximizing Current Snowflake Investments
Some companies have spent vital cash on instruments to stay modern and aggressive. Whereas this may be a wonderful technique for a future-oriented firm, it could show futile for those who don’t maximize the worth of your funding. In keeping with Flexera1, 92% of enterprises have a multi-cloud technique, whereas 80% have a hybrid cloud technique.
Integrating completely different programs, knowledge sources, and applied sciences inside an ecosystem may be tough and time-consuming, resulting in inefficiencies, knowledge silos, damaged machine studying fashions, and locked ROI.
The DataRobot AI Platform and the Snowflake Information Cloud present an interoperable, scalable AI/ML answer and distinctive companies that combine with numerous 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 prospects maximize their Snowflake funding, DataRobot is extending its Snowflake integration to assist prospects rapidly 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 in search of mature knowledge science platforms that may scale to the dimensions of their total enterprise. With the most recent 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, Information Cloud Principal, AI/ML at Snowflake. “By bringing the unrivaled AutoML capabilities of DataRobot to the information in Snowflake’s Information Cloud, prospects get a seamless and complete enterprise-grade knowledge science platform.”
Full the Machine Studying Lifecycle, With out Repeated Configuration
Connecting to Snowflake
Connect 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 Information Evaluation
After we hook up 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 answer with Snowflake, which means that code-first customers can use DataRobot’s hosted Notebooks because the interface and Snowpark processes the information immediately within the knowledge warehouse. This permits customers to work with acquainted Python syntax that will get pushed right 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 fast AI experimentation.
Be taught extra about DataRobot hosted notebooks.
Mannequin Coaching
As soon as the information is ready, customers select their most popular method for mannequin improvement utilizing DataRobot AutoML by means of the GUI, hosted Notebooks, or each.
When the coaching course of is full, DataRobot will advocate 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 completely 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 information for pace and elasticity, whereas maintaining knowledge in place.
Mannequin Monitoring
Inner and exterior components have an effect on fashions’ efficiency.
The brand new monitoring job functionality is run seamlessly from the DataRobot GUI helps prospects maintain observe of their enterprise choices 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 might help decide mannequin accountability.
Be taught extra in regards to the new monitoring job and automated deployment.
There’s extra coming
We’ve got extra thrilling capabilities to share, many associated to the Snowflake integration, which might 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 stand up to hurry on these new options.
Getting Began with DataRobot AI and Snowflake, the Information 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
In regards to the writer
World Technical Product Advocacy Lead, DataRobot
Atalia Horenshtien is a World Technical Product Advocacy Lead at DataRobot. She performs an important position because the lead developer of the DataRobot technical market story and works carefully with product, advertising and marketing, and gross sales. As a former Buyer Going through Information 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 the best way to undertake AI/ML throughout the group utilizing the DataRobot platform. A few of her talking periods on completely different subjects like MLOps, Time Sequence Forecasting, Sports activities initiatives, and use instances from varied verticals in business occasions like AI Summit NY, AI Summit Silicon Valley, Advertising AI Convention (MAICON), and companions occasions akin 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.
