Cloudera, the
enterprise data cloud company, today announced an expanded set of production
machine learning capabilities for MLOps is now available in Cloudera Machine
Learning (CML). Organizations can manage and secure the ML lifecycle for
production machine learning with CML's new MLOps features and Cloudera SDX for
models. Data scientists, machine learning engineers, and operators can
collaborate in a single unified solution, drastically reducing time to value
and minimizing business risk for production machine learning models.
"Companies past the piloting phase of machine learning
adoption are looking to scale deployments in production to hundreds or even
thousands of ML models across their entire business," said Andrew Brust,
Founder and CEO of Blue Badge Insights, an independent advisory firm.
"Managing, monitoring and governing models at this scale can't be a
bespoke process. With a true ML operations platform, companies can make AI a
mission-critical component of their digitally transformed business."
The release of Cloudera Machine Learning with new MLOps features
and Cloudera SDX for models provides a fundamental set of model and lifecycle
management capabilities to enable the repeatable, transparent, and governed
approaches necessary for scaling model deployments and ML use cases.
Benefits include:
- Unique model cataloging and lineage
capabilities allow
visibility into the entire ML lifecycle to eliminate silos and blind spots
for full lifecycle transparency, explainability and accountability.
- Full end-to-end machine learning
lifecycle management
that includes everything required to securely deploy machine learning
models to production, ensure accuracy, and scale use cases.
- A first-class model monitoring
service designed
to track and monitor both technical aspects and accuracy of predictions in
a repeatable, secure, and scalable way.
- Built on a 100% open source
standard and fully
integrated with Cloudera Data Platform, enabling customers to integrate
into existing and future tooling while not being locked into a single
vendor.
"Cloudera has been working across our industry and with some of
our largest customers and partners to build open standards for machine learning
metadata," said Arun Murthy, chief product officer, Cloudera. "We have
implemented those standards as part of Cloudera Machine Learning to deliver
everything enterprises need for deploying and sustaining machine learning
models in production at scale. With first-class model deployment, security,
governance, and monitoring, this is the first end-to-end ML solution for
full-lifecycle management from data to ML driven business impact across hybrid
and multi-cloud."
The expanded set of production machine learning capabilities
available in Cloudera Machine Learning (CML) include:
- New MLOps features for monitoring the
functional and business performance of machine learning models:
- Detect model performance and drift
over time with native storage and access to custom and arbitrary model
metrics.
- Measure and track individual
prediction accuracy, ensuring models are compliant and performing
optimally.
- Cloudera SDX for models extends SDX governance capabilities
to now support models:
- Track, manage, and understand
large numbers of ML models deployed across the enterprise with model
cataloging, full lifecycle lineage, and custom metadata in Apache Atlas.
- View the lineage of data tied to
the models built and deployed in a single system to help manage and
govern the ML lifecycle.
- Increased Model security for Model
REST endpoints, which allows models to be served in a CML production
environment without compromising security.
Availability and Pricing
Cloudera Machine Learning with new MLOps features and Cloudera SDX
for models is available on CDP for both Microsoft Azure and Amazon Web Services
as an integral part of the Cloudera Machine Learning platform. CML is charged
by the hour and starts at $0.68/hr per instance. Detailed CDP and CML pricing
information can be found here.