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 DevSecOps workflows is key to streamlining this process.

Despite the proliferation of tools on the market, bringing the right ones together to build a comprehensive ML pipeline isn’t easy. That’s why we’re excited to announce a new technology integration with Qwak. Qwak is a fully managed ML Platform that brings together machine learning models and traditional software development lifecycle processes to accelerate, scale, and secure the delivery of ML applications.

Managing Your ML Lifecycle

MLOps is the connection between Machine Learning and Operations, incorporating Machine Learning, DevOps and Data Engineering. During the model development stage, we need a system that manages all of the experiments and identifies the most effective model that we want to use. As in the software development lifecycle, the ML lifecycle continuously iterates, striving to improve the model’s accuracy and general quality.

As a Data Scientist, you’re building ML models that continuously need to be experimented (fine tuned and trained) and deployed to production. This process produces an immense amount of data and artifacts that need to be stored, scanned for potential security vulnerabilities and license compliance issues, and finally made available in production. Organizations need to securely govern their artifacts (ML models) in a trusted location, where they can control access to their data. This ensures an uncompromised secure management process from the model’s development stage all the way to production.

This is where an MLOps Platform and an advanced binary manager come into play.

The Qwak Solution

Numerous obstacles can hinder the advancement of ML projects, impacting critical tasks such as overseeing model experiments and research, evaluating diverse model build outcomes, incorporating user metadata into models, and handling model deployment. Fortunately, Qwak provides ML professionals with a comprehensive toolkit to simplify these procedures, enhancing efficiency and effectiveness.

Qwak key features: 

• Deploying and iterating on your models faster
• Testing and packaging your models using a flexible build mechanism
• Comprehensive logging of artifacts, parameters, and metrics during model training and evaluation
• Deploying models as REST endpoints, batch transformation jobs, or streaming applications
• Gradually deploying and A/B testing your models in production
• Querying model results and visualizing model behavior in production
• Automation capabilities for re-training and deploying models

The Synergy of JFrog and Qwak

The integration of JFrog with Qwak provides customers with a complete MLSecOps solution that helps bridge the MLOps/DevSecOps-gap by bringing ML models in line with other, more established software development processes. By creating a single source of truth for all software components, this integration enables seamless cross-collaboration between Engineering, DevOps, and DevSecOps teams so they can build and release AI applications at greater speed, with minimal risk, and at a lower cost.

Overview of the JFrog and Qwak Integration Architecture

Comprehensive Dependency Scanning

Real-time analysis of dependencies ensures that data scientists, ML engineers, developers, and compliance stakeholders clearly understand the components influencing their models. This integration empowers users to make informed decisions by integrating the advanced MLOps capabilities of Qwak with advanced scanning capabilities powered by JFrog.

Enforced Control and Compliance

By leveraging the JFrog Platform as the exclusive platform for your models, dependencies, and other artifacts, you gain complete control and visibility over all your software components. With JFrog’s advanced resource management capabilities, which can be defined for teams, groups, projects, or on an organizational level, you can ensure that your ML model’s outcomes adhere to configured policies and organizational standards. The strict governance enforced by this integration promotes consistency, mitigates risks, and aligns development practices with organizational guidelines.

Centralized Artifact Management

By using JFrog as Qwak’s main artifact source, you can benefit from JFrog’s comprehensive management capabilities, such as:

• Centralizing all models, artifacts, and software components within a single source of truth
• Reducing the potential hazards linked to external service disruptions or the elimination of models, packages, or package versions from public repositories
• Enable organizations, teams, groups, and project owners to manage and limit access to external private or public repositories, ensuring that only approved sources can be utilized by users
• Offer comprehensive transparency to teams, groups, projects, managers, and other stakeholders regarding the content utilized within the company

Get started with JFrog and Qwak

Watch this demo for an overview of the integration. To get started now, see step-by-step instructions here.

Summing it up

Together, JFrog and Qwak instill governance, transparency, visibility, and security into every facet of the development and deployment lifecycle for ML models. From managing dependencies to ensuring compliance and optimizing storage, this integration empowers your organization to embrace the future of machine learning with confidence and efficiency.

Register for our upcoming webinar to learn more about this integration.