Domino Data Lab announced Domino
4.3. This release adds support for the popular Red Hat OpenShift
distribution of Kubernetes to make it easier for its customers to scale
data science workloads on any platform. 4.3 also improves Domino's model
monitoring capabilities, and extends its IT security features for
enterprises via new reporting capabilities.
Domino
offers a data science management platform that centralizes predictive
analytics and machine learning (ML) research and development based on an
open ecosystem that lets data scientists choose their preferred tools
and algorithms while reducing the burden on IT.
"Large,
sophisticated data science organizations demand flexibility in how they
build and deploy their data science stacks. Adding Red Hat OpenShift to
our wide variety of deployment options gives customers even more
flexibility to run on almost any cloud provider or on their own on-prem
hardware," said Nick Elprin, co-founder and CEO at Domino Data Lab.
"We're obsessed with delivering enterprise-grade security, control,
reliability, and observability in a central platform that helps our many
Fortune 100 customers unleash the power of data science. We continue to
focus, with this release, on helping them accelerate and confidently
manage their demanding data science operations."
The Domino 4.3 platform includes new capabilities, such as:
Expanded Elastic Scaling with Red Hat OpenShift Kubernetes Support:
Kubernetes
(K8s) is quickly becoming the IT standard for flexible containerized
application orchestration across clusters with the ability to
automatically deploy, scale capacity up and down on demand, and manage
production workloads.
Red Hat OpenShift Kubernetes Engine,
popular with IT teams, offers an attractive Kubernetes option for many
customers since it can run on virtually all major cloud providers, as
well as on-premise deployments. With this release, Domino can now take
advantage of intelligent Kubernetes orchestration on OpenShift clusters
for efficient management and smart utilization of computing resources.
Rapidly scaling containerized workloads is particularly important as the
demand for high-powered CPUs, GPUs and RAM can spike dramatically when
training models or engineering features, and then quickly scale down
once completed.
For
organizations that have invested in large, centralized Kubernetes
clusters to improve hardware utilization across a large pool of users
and application workloads, Domino now supports multi-tenant Kubernetes
clusters so a dedicated cluster for installation is not required.
Domino Model Monitor (DMM) Enhancements:
Domino Model Monitor (DMM),
introduced in June 2020, now has powerful new capabilities that make it
easier for enterprises to maintain high-performing ML models on any
platform. DMM lets organizations automate the monitoring of model inputs
and outputs to detect changes in production data that could signal when
a model is no longer producing results that are consistent with current
business conditions. Undetected data and model drift are especially
problematic during a pandemic, since drastic changes to the economic
environment and human behavior increase the likelihood of model
inaccuracy and the associated risks of financial loss and a degraded
customer experience.
The
latest update includes new trend analysis capabilities that offer
better insight into how the quality of a model's predictions have been
changing over time. It also includes new traffic charts to track the
volume of model predictions and ground truth data (actual results) over
time.
Advanced Enterprise-grade Authentication and Security:
Domino
broadens its enterprise-grade authentication capabilities to include
options for certification of Domino APIs and third-party services via
short-lived Domino identity (OpenID) tokens to connect to any external
authentication service. When combined with its robust SSO capabilities,
these enhancements make it easier for Domino administrators to grant or
revoke user access while limiting where users are able to connect from.
Domino
has also significantly enhanced its internal processes and tooling to
comply with enterprise application monitoring and security reporting
requirements, for example:
- Domino logs can be exposed to Fluentd-compatible aggregation tools
- Application health metrics can be integrated into Prometheus monitoring systems
- Container and dependencies support vulnerability scanning and remediation
Existing Domino customers can upgrade to this new release immediately. New users who would like to try Domino can do so at dominodatalab.com/try.