JFrog Ltd, a Liquid Software company, has taken a major leap forward in machine learning model management by announcing its integration with Amazon SageMaker. This groundbreaking partnership will empower companies to seamlessly build, train, and deploy their machine learning models.
The focal point of this integration is JFrog’s ML Model Management solution, which now boasts new versioning capabilities. By augmenting model development with DevSecOps workflows, JFrog has introduced a higher level of transparency. Developers, DevOps teams, and data scientists can now ensure that the correct and secure version of a model is utilized, thereby optimizing performance and mitigating risks.
One of the key features of JFrog’s integration with Amazon SageMaker is the consolidation of all artifacts used in ML development and data science applications in JFrog Artifactory. This guarantees that all components are securely saved in a single location, easily accessible to JFrog customers and users.
Kelly Hartman, the SVP of Global Channels and Alliances at JFrog, highlighted the significance of this collaboration, stating, “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.”
To educate organizations on the best practices for integrating model use and development into secure software supply chain processes, JFrog will be hosting an educational webinar on January 31. This will be an excellent opportunity for companies to gain insights and learn from industry experts.
In addition to ensuring accessibility and traceability, JFrog’s integration with Amazon SageMaker brings machine learning closer to software development and production workflows. Not only does it protect models from deletion or modification, but it also allows for the secure and compliant development, training, and deployment of ML models. The integration even provides the ability to scan ML licenses, ensuring adherence to company policies and regulatory requirements.
By joining forces with Amazon SageMaker, JFrog is revolutionizing machine learning model management, offering organizations an unrivaled combination of efficiency, security, and flexibility. This partnership sets the stage for a new era of ML development, propelling the industry forward into uncharted territories.
JFrog Integration with Amazon SageMaker
JFrog Ltd, a Liquid Software company, has announced its integration with Amazon SageMaker, a machine learning service provided by Amazon Web Services. This partnership aims to enhance machine learning model management for companies, enabling them to build, train, and deploy their models seamlessly.
ML Model Management Solution and Versioning
One of the main features of this integration is JFrog’s ML Model Management solution, which now includes new versioning capabilities. This allows developers, DevOps teams, and data scientists to ensure that the correct and secure version of a model is utilized, optimizing performance and reducing risks. By integrating with DevSecOps workflows, JFrog introduces a higher level of transparency into the model development process.
Consolidation in JFrog Artifactory
JFrog’s integration with Amazon SageMaker consolidates all artifacts used in ML development and data science applications in JFrog Artifactory. This ensures that all components are securely saved in a single location, easily accessible to JFrog customers and users.
Webinar and Education
To educate organizations on best practices for integrating model use and development into secure software supply chain processes, JFrog will be hosting an educational webinar on January 31. This webinar will provide valuable insights and learning opportunities from industry experts.
Accessibility, Traceability, and Compliance
JFrog’s integration with Amazon SageMaker not only ensures accessibility and traceability of ML models but also brings machine learning closer to software development and production workflows. It protects models from deletion or modification and enables secure and compliant development, training, and deployment of ML models. The integration also allows for scanning ML licenses to ensure adherence to company policies and regulatory requirements.
Revolutionizing Machine Learning Model Management
By partnering with Amazon SageMaker, JFrog aims to revolutionize machine learning model management, offering organizations a powerful combination of efficiency, security, and flexibility. This integration sets the stage for a new era in ML development, pushing the industry forward into uncharted territories.
Key Terms:
– Machine learning model: A mathematical model that is trained on data to make predictions or decisions without being explicitly programmed.
– DevSecOps: A collaborative approach that integrates development, security, and operations teams to ensure secure and efficient software delivery.
– ML licenses: Licenses that govern the use and distribution of machine learning models.
Related Links:
– JFrog official website
– Amazon SageMaker official website
The source of the article is from the blog foodnext.nl