Get to Know JFrog ML

AI/ML development is getting a lot of attention as organizations rush to bring AI services into their business applications. While emerging MLOps practices are designed to make developing AI applications easier, the complexity and fragmentation of available MLOps tools often complicates the work of Data Scientists and ML Engineers, and lessens trust in what’s being delivered.

As part of the JFrog Software Supply Chain Platform, JFrog ML unifies the elements that matter most for AI/ML development, allowing ML teams to focus on delivering innovation in a trusted fashion while handling everything else behind the scenes. With JFrog ML, AI builders can deliver fast while DevOps and DevSecOps teams maintain the visibility and control needed to keep their organizations secure. In this blog, we’ll introduce you to JFrog ML and explain what it does, who it’s for, and the value it provides as part of the JFrog Platform.

From MLOps to DataOps: the key functionality Data Scientists need in one place

JFrog ML is an end-to-end ML solution that simplifies the tangled ML toolchain. It provides the core functionality Data Science teams need to deliver value to the business.

• Experiment Tracking: Organized and Reproducible Research Processes
  JFrog ML simplifies experiment tracking by providing a structured approach to logging model configurations, datasets, and results. This ensures that every experiment is documented, making it easier to reproduce results and identify what works best. The platform offers visualization tools for comparing experiments, helping teams make data-driven decisions while maintaining a detailed history of progress for accountability and collaboration.

• Model Registry: Centralized Storage for Versioned Models
  JFrog ML uniquely leverages JFrog Artifactory as the most advanced, secure, centralized repository for versioned ML models and the components that go into your models. It enables teams to store, manage, and share models effortlessly while maintaining a complete history of changes. This facilitates collaboration, ensures compliance with governance standards, and simplifies the transition from development to production by making models readily accessible. Enhanced by JFrog Security, the registry includes automated scans for vulnerabilities and compliance checks, ensuring models meet security standards before being deployed to production environments.

• Deployment and Serving: Scalable and Reliable Model Deployment
  JFrog ML streamlines model deployment with tools for scalable and reliable serving. It supports multiple deployment strategies – including real-time, batch, and streaming – ensuring flexibility for diverse use cases. The JFrog Platform handles the deployment leveraging DevOps best practices, reducing the complexity of going live and providing high availability and robust performance in production environments.

• Model Monitoring in Production: Real-Time Insights into Model Performance
  Once models are deployed, JFrog ML ensures their effectiveness through comprehensive monitoring capabilities. It tracks both essential ML-related metrics as well as infrastructure-related metrics such as latency, throughput, error rate, and CPU / GPU utilization latency, providing actionable insights for both Data Science and DevOps teams. This helps teams detect issues proactively, maintain compliance, and continually improve model outcomes to meet business objectives.

• Feature Store and Management: Simplified Data Preparation and Reuse
  The Feature Store in JFrog ML centralizes the management of ML features, enabling efficient data preparation and reuse across projects. It ensures consistency by providing a single source of truth for feature definitions, improving collaboration among teams. With tools to standardize feature extraction and transformation, data scientists can quickly access high-quality, preprocessed data, accelerating experimentation and reducing redundancy in data workflows.

• Model Testing: Ensuring Model Quality Before Deployment
  JFrog ML provides robust tools for model testing, ensuring that models meet performance and reliability standards before deployment. From unit testing of algorithms to stress testing models under real-world conditions, it supports a variety of evaluation techniques. This step reduces the risk of deploying underperforming or biased models, ensuring high-quality results in production environments.

• Prompt Management: Support for Generative AI Workflows
  JFrog ML’s support for generative AI workflows includes advanced prompt management and a vector store for embedding-based retrieval. These features allow data scientists to experiment with and optimize prompts, as well as store and retrieve embeddings efficiently for applications like semantic search and personalization. This makes it a powerful tool for leveraging large language models (LLMs) and similar technologies.

JFrog ML reduces the number of tools and stakeholders Data Science teams need to interact with to develop and test new ML models. It also handles deployment, saving teams from having to manage infrastructure so they can focus on getting more models into production.

Bring security into your AI/ML workflows

Security concerns are some of the biggest hurdles impacting the successful deployment of models today. According to a recent research report commissioned by JFrog, organizations flag malicious code in models as the second most prevalent AI security and compliance concern.

Because JFrog ML is part of the JFrog Platform, all of the security tools, frameworks, and policies benefiting traditional software developers are now also available to AI developers. The open-source models and components used as the basis for AI services are scanned to ensure they are safe to use. This also applies to any libraries and containers used to deliver models.

An MLOps platform like no other

The JFrog Platform is the single source of truth for over 7,000 companies. As such, it plays a critical role in how organizations have built their development workflows. By building and deploying ML models with the JFrog Platform, teams are able to bring AI development in line with standard business processes and frameworks. This ensures that the final output of AI development is an asset that your DevOps, Security, and Dev teams are going to utilize and trust.

JFrog ML is available today as part of the JFrog Platform. You can start using it as part of your JFrog subscription right away or start a trial to take it for a spin. For more information on MLOps practices, including hands-on learning with JFrog ML, consider joining an upcoming monthly Masterclass.