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Jfrog Platform: DEVSECOPS, MLOPS, AND AI ARTIFACT MANAGEMENT: Artifactory, Xray, and JFrog Fly for Software Supply Chain Security, ML Models, and Agen

by Zinnia Harris
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₹3,657.00
Original price ₹3,657.00
Original price ₹3,657.00
₹3,657.00
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Book cover type: Paperback
  • ISBN13: 9798250250337
  • Binding: Paperback
  • Subject: N/A
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Publication Date:
  • Pages: 300
  • Original Price: USD 34.99
  • Language: English
  • Edition: N/A
  • Item Weight: 522 grams
  • BISAC Subject(s): Security / General

Build a single, governed control plane for software packages, containers, SBOMs, attestations, and ML models, then use it to ship faster with fewer supply chain surprises.

Modern delivery breaks when artifacts are treated as "outputs" instead of the real unit of release, scanning, promotion, rollback, and audit. Dependency drift, inconsistent repositories, weak promotion paths, and unclear evidence chains make security work reactive and deployments fragile.

This book shows how to operate the JFrog Platform as an end to end artifact and security system, covering Artifactory and Xray foundations, SBOM and provenance workflows, ML artifact management with FrogML and JFrog ML, and AI-native and agentic workflows with JFrog Fly and the JFrog MCP Server.

  • Design repository strategy that scales, including local, remote, and virtual patterns, naming conventions, and boundary choices that support teams and governance
  • Publish and resolve dependencies across common package managers with repeatable promotion flows and release integrity controls
  • Capture build context for traceability, then use metadata, properties, and AQL to make artifacts searchable and auditable
  • Use Xray effectively, understand scan coverage, set up watches and policies, manage violations and exceptions, and enforce build-fail patterns in CI
  • Run SBOM workflows with CycloneDX and SPDX, then use SBOMs for policy gates, compliance reviews, and audit evidence
  • Implement evidence, attestations, and provenance, including in-toto statements and DSSE envelopes, and connect evidence to promotion decisions
  • Map platform controls to real governance language, including SLSA provenance concepts, Sigstore fit, and NIST SSDF alignment
  • Manage ML artifacts in Artifactory, log and load models with FrogML, version models cleanly, and promote with reproducibility assets
  • Secure ML models and AI artifacts with Xray, build intake and release policies, and use SBOM visibility for model-serving stacks
  • Integrate MLOps lifecycle steps with artifact security, deployment flow checkpoints, and operational feedback loops
  • Adopt agentic repository workflows with the JFrog MCP Server, including safe query patterns, permissions, and approval gates
  • Use JFrog Fly for AI-native development, registry routing, semantic release operations, and runtime tracking, with practical limits
  • Apply production-ready platform patterns, diagnose common failure modes, and build a maintainable operating model across DevSecOps, MLOps, and AI artifacts

This guide includes working commands, configuration examples, and CI snippets you can adapt directly to real repositories and pipelines.

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