Data scientists, ML engineers and DevOps are used to not having a common process for delivering software. The Indian service provider, JForg has introduced ML Model  management capabilities to streamline the management and security of these models. The feature on JFrog platform brings AI deliveries in line with an organisation’s existing DevOps and DevSecOPs to accelerate, secure and govern the release of the machine learning components. 

The lack of common process amongst teams introduces friction, difficulty in scale, and lacks standards across a portfolio. ML models are incomplete with Python and are often served using Docker containers. Yoav Landman, co-founder and CTO of JFrog looks at this release as a unified software supply chain platform to help developers deliver trusted software at scale. 

“It can take time and effort to deploy models into production from start to finish. However, even once in production, users face challenges with model performance, model drift, and bias,” said Jim Mercer, Research Vice President, IDC Research. 

According to IDC Research, the AI/ML market, including software, hardware and services, is poised to grow at 19.6%, approximately $500 billion in 2023. However, as more models are being moved to production, the end users often face cost and scaling challenges. 

JFrog’s latest management offers a proxy to the popular repository Hugging Face to cache open source AI models, protecting them from deletion or modification. It will also detect and block the use of malicious models. It scans model licences to ensure compliance with policies. 

Having a single system of record that can help automate the development, ongoing management, and security of models that get packaged into applications offers a compelling alternative for optimising the process, said Mercer. 

Commenting on the release, Yossi Shaul, SVP Product and Engineering, JFrog said, “We’re excited to give customers an easy way to proxy, store, secure, and manage models alongside their other software components to help accelerate their pace of innovation while remaining well-positioned for tomorrow’s demands.”

The post JFrog Introduces Support for ML Models To Streamline Workflows appeared first on Analytics India Magazine.

Data scientists, ML engineers and DevOps are used to not having a common process for delivering software. The Indian service provider, JForg has introduced ML Model  management capabilities to streamline the management and security of these models. The feature on JFrog platform brings AI deliveries in line with an organisation’s existing DevOps and DevSecOPs to accelerate, secure and govern the release of the machine learning components. 

The lack of common process amongst teams introduces friction, difficulty in scale, and lacks standards across a portfolio. ML models are incomplete with Python and are often served using Docker containers. Yoav Landman, co-founder and CTO of JFrog looks at this release as a unified software supply chain platform to help developers deliver trusted software at scale. 

“It can take time and effort to deploy models into production from start to finish. However, even once in production, users face challenges with model performance, model drift, and bias,” said Jim Mercer, Research Vice President, IDC Research. 

According to IDC Research, the AI/ML market, including software, hardware and services, is poised to grow at 19.6%, approximately $500 billion in 2023. However, as more models are being moved to production, the end users often face cost and scaling challenges. 

JFrog’s latest management offers a proxy to the popular repository Hugging Face to cache open source AI models, protecting them from deletion or modification. It will also detect and block the use of malicious models. It scans model licences to ensure compliance with policies. 

Having a single system of record that can help automate the development, ongoing management, and security of models that get packaged into applications offers a compelling alternative for optimising the process, said Mercer. 

Commenting on the release, Yossi Shaul, SVP Product and Engineering, JFrog said, “We’re excited to give customers an easy way to proxy, store, secure, and manage models alongside their other software components to help accelerate their pace of innovation while remaining well-positioned for tomorrow’s demands.”

The post JFrog Introduces Support for ML Models To Streamline Workflows appeared first on Analytics India Magazine.