# ML Ops

Published articles for ML Ops.

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## The maturity gap in ML pipeline infrastructure

DevFeed: [The maturity gap in ML pipeline infrastructure](<https://devfeed.tech/articles/the-maturity-gap-in-ml-pipeline-infrastructure-13263.md>)

Original publisher: [Read original article](<https://www.chainguard.dev/unchained/the-maturity-gap-in-ml-pipeline-infrastructure>)

Published: 2026-01-26T00:00:00Z

Content type: opinion

Language: en

Sources: [Chainguard: Unchained](<https://devfeed.tech/sources/chainguard-unchained.md>)

Topics: [Security](<https://devfeed.tech/topics/security.md>), [Architecture & Design](<https://devfeed.tech/topics/architecture-design.md>), [Tooling](<https://devfeed.tech/topics/tooling.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Software Engineering](<https://devfeed.tech/topics/software-engineering.md>), [PyTorch](<https://devfeed.tech/topics/pytorch.md>)

Tags: [ai-coding](<https://devfeed.tech/tags/ai-coding.md>), [attacks](<https://devfeed.tech/tags/attacks.md>), [chainguard-pytorch-image](<https://devfeed.tech/tags/chainguard-pytorch-image.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [machine-learning-pipelines](<https://devfeed.tech/tags/machine-learning-pipelines.md>), [ml](<https://devfeed.tech/tags/ml.md>), [ml-ops](<https://devfeed.tech/tags/ml-ops.md>), [pipeline](<https://devfeed.tech/tags/pipeline.md>), [python](<https://devfeed.tech/tags/python.md>), [pytorch](<https://devfeed.tech/tags/pytorch.md>), [secure-by-default](<https://devfeed.tech/tags/secure-by-default.md>), [security](<https://devfeed.tech/tags/security.md>), [security-best-practices](<https://devfeed.tech/tags/security-best-practices.md>), [serialization](<https://devfeed.tech/tags/serialization.md>), [serialization-format](<https://devfeed.tech/tags/serialization-format.md>), [tooling](<https://devfeed.tech/tags/tooling.md>)

### AI overview

This article argues that ML pipeline infrastructure in 2026 has a security maturity gap: common tooling does not yet provide the secure-by-default protections expected in software engineering. It examines risks including data poisoning, model laundering, and insecure model serialization, and discusses short-term mitigations and longer-term industry improvements.

### Source excerpt

ML pipelines in 2026 still lack secure-by-default tooling. Learn the key security gaps in ML Ops and how teams can reduce risk today.

## Good MLOps is good ML supply chain security

DevFeed: [Good MLOps is good ML supply chain security](<https://devfeed.tech/articles/good-mlops-is-good-ml-supply-chain-security-13070.md>)

Original publisher: [Read original article](<https://www.chainguard.dev/unchained/good-mlops-is-good-ml-supply-chain-security>)

Published: 2023-07-25T00:00:00Z

Content type: article

Language: en

Sources: [Chainguard: Unchained](<https://devfeed.tech/sources/chainguard-unchained.md>)

Topics: [supply-chain-security](<https://devfeed.tech/topics/supply-chain-security.md>), [MLOps](<https://devfeed.tech/topics/mlops.md>), [Security](<https://devfeed.tech/topics/security.md>), [AI, ML & Data Engineering](<https://devfeed.tech/topics/ai-ml-data-engineering.md>), [Software Engineering](<https://devfeed.tech/topics/software-engineering.md>), [chainguard](<https://devfeed.tech/topics/chainguard.md>)

Tags: [data](<https://devfeed.tech/tags/data.md>), [ml-ops](<https://devfeed.tech/tags/ml-ops.md>), [ml-security](<https://devfeed.tech/tags/ml-security.md>), [ml-supply-chain](<https://devfeed.tech/tags/ml-supply-chain.md>), [mlops](<https://devfeed.tech/tags/mlops.md>), [secure-by-default](<https://devfeed.tech/tags/secure-by-default.md>), [security](<https://devfeed.tech/tags/security.md>), [software-supply-chain-security](<https://devfeed.tech/tags/software-supply-chain-security.md>), [software-supply-chain-security-research](<https://devfeed.tech/tags/software-supply-chain-security-research.md>), [supply-chain-security](<https://devfeed.tech/tags/supply-chain-security.md>), [tensorflow](<https://devfeed.tech/tags/tensorflow.md>)

### AI overview

The article explains how MLOps and ML supply chain security share a common infrastructure philosophy. It identifies training data, data delivery, software dependencies, model code, training environments, build steps, and production deployment as parts of the machine learning supply chain whose security and correctness must be addressed together.

### Source excerpt

Uncover the symbiosis of good MLOps and ML supply chain security with Chainguard's expert insights.