New JFrog Artifactory and Amazon SageMaker
integration empowers developers and data scientists to build,
train, and deploy ML Models in the cloud
JFrog Ltd. (“JFrog”) (Nasdaq: FROG), the Liquid Software company
and creators of the JFrog Software Supply Chain Platform, today
announced a new integration with Amazon SageMaker, which helps
companies build, train, and deploy machine learning (ML) models for
any use case with fully managed infrastructure, tools, and
workflows. By pairing JFrog Artifactory with Amazon SageMaker, ML
models can be delivered alongside all other software development
components in a modern DevSecOps workflow, making each model
immutable, traceable, secure, and validated as it matures for
release. JFrog also unveiled new versioning capabilities for its ML
Model management solution, which help ensure compliance and
security are incorporated at every step of ML model
development.
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the full release here:
https://www.businesswire.com/news/home/20240117147895/en/
JFrog and Amazon SageMaker Accelerate the
Delivery of Intelligent, Trusted Software (Graphic: Business
Wire)
"As more companies begin managing big data in the cloud, DevOps
team leaders are asking how they can scale data science and ML
capabilities to accelerate software delivery without introducing
risk and complexity," said Kelly Hartman, SVP, Global Channels and
Alliances, JFrog. "The combination of Artifactory and Amazon
SageMaker creates a single source of truth that indoctrinates
DevSecOps best practices to ML model development in the cloud –
delivering flexibility, speed, security, and peace of mind –
breaking into a new frontier of MLSecOps.”
According to a recent Forrester survey, 50 percent of data
decision-makers cited applying governance policies within AI/ML as
the biggest challenge to widespread usage, while 45 percent cited
data and model security as the gating factor. JFrog’s Amazon
SageMaker integration applies DevSecOps best practices to ML model
management, allowing developers and data scientists to expand,
accelerate, and secure the development of ML projects in a manner
that is enterprise-grade, secure, and abides by regulatory and
organizational compliance.
JFrog’s new Amazon SageMaker integration allows organizations
to:
- Maintain a single source of truth for data scientists and
developers, ensuring all models are readily accessible, traceable,
and tamper-proof.
- Bring ML closer to the software development and production
lifecycle workflows, protecting models from deletion or
modification.
- Develop, train, secure and deploy ML models.
- Detect and block the use of malicious ML models across the
organization.
- Scan ML model licenses to ensure compliance with company
policies and regulatory requirements.
- Store home-grown or internally augmented ML models with robust
access controls and versioning history for greater
transparency.
- Bundle and distribute ML models as part of any software
release.
“Traditional software development processes and machine learning
stand apart, lacking integration with existing tools,” said Larry
Carvalho, Principal and founder of RobustCloud. “Together, JFrog
Artifactory and Amazon SageMaker provide an integrated end-to-end,
governed environment for machine learning. Bringing these worlds
together represents significant progress towards harmonizing
machine learning pipelines with established software development
lifecycles and best practices.”
Along with its Amazon SageMaker integration, JFrog unveiled new
versioning capabilities for its ML Model Management solution that
incorporate model development into an organization’s DevSecOps
workflow to increase transparency around each model version so
developers, DevOps teams, and data scientists can ensure the
correct, secure version of a model is utilized.
The JFrog integration with Amazon SageMaker, available now for
JFrog customers and Amazon SageMaker users, ensures all artifacts
consumed by data scientists or used to develop ML applications are
pulled from and saved in JFrog Artifactory.
For a deeper look at the integration and how it works, read this
blog. You can also register to join JFrog and AWS on Wednesday,
January 31 at 1 p.m. ET/10 a.m. PT for an educational webinar,
"Building for the future: DevSecOps in the era of AI/ML model
development," describing best practices for introducing model use
and development into secure software supply chain and development
processes.
Like this story? Post this on X (formerly Twitter):
.@jfrog rolls out new integration with @awscloud SageMaker to
unlock greater #ML #security and innovation across the software
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#SoftwareSupplyChain #DevSecOps #SDLC #MachineLearning #AI
About JFrog
JFrog Ltd. (Nasdaq: FROG) is on a mission to create a world of
software delivered without friction from developer to device.
Driven by a “Liquid Software” vision, the JFrog Software Supply
Chain Platform is a single system of record that powers
organizations to build, manage, and distribute software quickly and
securely, ensuring it is available, traceable, and tamper-proof.
The integrated security features also help identify, protect, and
remediate against threats and vulnerabilities. JFrog’s hybrid,
universal, multi-cloud platform is available as both self-hosted
and SaaS services across major cloud service providers. Millions of
users and 7K+ customers worldwide, including a majority of the
Fortune 100, depend on JFrog solutions to securely embrace digital
transformation. Once you leap forward, you won’t go back! Learn
more at jfrog.com and follow us on Twitter: @jfrog.
Cautionary Note About Forward-Looking Statements
This press release contains “forward-looking” statements, as
that term is defined under the U.S. federal securities laws,
including but not limited to statements regarding the JFrog
Artifactory and Amazon SageMaker integration enabling collaboration
on building and deploying ML Models, JFrog new versioning
capabilities for its ML Model Management solution and the
anticipated benefits to customers.
These forward-looking statements are based on our current
assumptions, expectations and beliefs and are subject to
substantial risks, uncertainties, assumptions and changes in
circumstances that may cause JFrog’s actual results, performance or
achievements to differ materially from those expressed or implied
in any forward-looking statement. There are a significant number of
factors that could cause actual results, performance or
achievements, to differ materially from statements made in this
press release, including but not limited to risks detailed in our
filings with the Securities and Exchange Commission, including in
our annual report on Form 10-K for the year ended December 31,
2022, our quarterly reports on Form 10-Q, and other filings and
reports that we may file from time to time with the Securities and
Exchange Commission. Forward-looking statements represent our
beliefs and assumptions only as of the date of this press release.
We disclaim any obligation to update forward-looking
statements.
View source
version on businesswire.com: https://www.businesswire.com/news/home/20240117147895/en/
Media Contact: Siobhan Lyons, Sr. MarComm Manager, JFrog,
siobhanL@jfrog.com Investor Contact: Jeff Schreiner, VP of
Investor Relations, jeffS@jfrog.com
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