JFrog Qwak

JFrog & Qwak: Accelerating Models Into Production – The DevOps Way

We are collectively thrilled to share some exciting news: Qwak will be joining the JFrog family! Nearly four years ago, Qwak was founded with the vision to empower Machine Learning (ML) engineers to drive real impact with their ML-based products and achieve meaningful business results. Our mission has always been to accelerate, scale, and secure …

Taking a GenAI Project to Production

Generative AI and Large Language Models (LLMs) are the new revolution of Artificial Intelligence, bringing the world capabilities that we could only dream about less than two years ago. Unlike previous milestones, such as Deep Learning, in the current AI revolution, everything is happening faster than ever before. Many feel that the train is about …

The basics of securing GenAI and LLM development

With the rapid adoption of AI-enabled services into production applications, it’s important that organizations are able to secure the AI/ML components coming into their software supply chain. The good news is that even if you don’t have a tool specifically for scanning models themselves, you can still apply the same DevSecOps best practices to securing …

Ensure your models flow with the JFrog plugin for MLflow

Just a few years back, developing AI/ML (Machine Learning) models was a secluded endeavor, primarily undertaken by small teams of developers and data scientists away from public scrutiny. However, with the surge in GenAI/LLMs, open-source models, and ML development tools, there’s been a significant democratization of model creation, with more developers and organizations engaging in …

Qwak and JFrog integration

Advancing MLOps with JFrog and Qwak

Modern AI applications are having a dramatic impact on our industry, but there are still certain hurdles when it comes to bringing ML models to production. The process of building ML models is so complex and time-intensive that many data scientists still struggle to turn concepts into production-ready models. Bridging the gap between MLOps and …

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Four Key Lessons for ML Model Security & Management

With Gartner estimating that over 90% of newly created business software applications will contain ML models or services by 2027, it is evident that the open source ML revolution is well underway. By adopting the right MLOps processes and leveraging the lessons learned from the DevOps revolution, organizations can navigate the open source and proprietary …

Integrating JFrog Artifactory with Amazon SageMaker

Today,  we’re excited to announce a new integration with Amazon SageMaker! SageMaker helps companies build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows. By leveraging JFrog Artifactory and Amazon SageMaker together, ML models can be delivered alongside all other software development components in a modern …

Evolving ML Model Versioning

TL;DR: JFrog’s ML Model Management capabilities, which help bridge the gap between AI/ML model development and DevSecOps, are now Generally Available and come with a new approach to versioning models that benefit Data Scientists and DevOps Engineers alike.  Model versioning can be a frustrating process with many considerations when taking models from Data Science to …

The JFrog Platform Empowers AI Model Development and Security

Navigating AI’s New Horizons: Empowering AI Model Development, Security and Compliance

The Wake-Up Call The rapid rise of artificial intelligence, more specifically, generative AI systems such as OpenAI’s ChatGPT, has simultaneously spurred intense development and concern over the past year. On the 30th of October, President Joe Biden signed an Executive Order that urges new federal standards for AI development, safety, security, and trustworthiness that also …