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Tools

The tools used in this guide.

Core tools

The following chapters explain each tool in detail.

This guide covers one toolset, but alternatives exist for every stage. For a broader compilation, see MLOps.toys.

Data management

Alternatives to DVC.

  • LakeFS - Git-like version control for data lakes
  • DagsHub - Data science collaboration platform
  • DoltHub - Collaborative versioned databases
  • Delta Lake - Open-source storage layer for lakehouses

Experiment tracking

Alternatives to CML for tracking experiments and visualizing metrics. CML reports results inside CI/CD pipelines, these tools track and visualize experiments instead.

  • Guild AI - Open-source toolkit for running, tracking, and optimizing ML experiments
  • Aim - Open-source, self-hosted tool for tracking and visualizing ML experiments
  • TensorBoard - Open-source toolkit for visualizing ML experiment metrics and model graphs

Model monitoring

These are alternatives to Evidently AI for monitoring models in production.

  • NannyML - Detect model and data drift, including estimated performance degradation, without ground truth labels
  • Deepchecks - Test and validate ML models and data, with a library or self-hosted UI
  • Seldon Alibi Detect - Algorithms for outlier, adversarial, and drift detection

Logging and observability

Alternatives to Fluent Bit for collecting, processing, and forwarding logs and observability data.

  • Vector - High-performance, end-to-end observability data pipeline for logs, metrics, and traces

Data annotation

Label Studio handles many data types, but most competitors specialize in one. See the awesome-data-labeling repository for specific alternatives.

Pipeline orchestration

Alternatives to GitHub Actions.

  • GitLab CI - DevOps platform with built-in CI/CD and container registry
  • Gitea - Self-hosted Git service with built-in CI/CD using GitHub Actions-compatible syntax
  • Forgejo - Self-hosted Git service and soft fork of Gitea with GitHub Actions-compatible workflows

Model packaging and serving

Alternatives to BentoML for packaging and serving models.

  • MLEM - Open-source tool to simplify ML model deployments
  • Cog - Package machine learning models in standard, production-ready containers
  • Seldon Core - Open-source platform to deploy ML models on Kubernetes
  • Kubeflow - ML workflows on Kubernetes, including training and serving

Container tools

Alternatives to Docker.

  • Podman - Daemonless, open-source tool for running, building, and sharing OCI containers and images

Self-hosted infrastructure

Tools for running the MLOps stack on your own hardware instead of managed cloud services.

  • CNCF Landscape - Graduated CNCF projects considered production-ready
  • Kubespray - Deploy production-ready Kubernetes clusters on bare-metal or virtual machines
  • Argo - Kubernetes-native continuous delivery (Argo CD) and workflow orchestration (Argo Workflows)
  • Harbor - Self-hosted container registry with vulnerability scanning and RBAC
  • Distribution Registry - Lightweight local container registry, also known as Docker Registry
  • Helm - Package manager for Kubernetes; commonly used to install and manage Argo, registries, and CI runners
  • Docker Swarm - Simpler, built-in container orchestration alternative to Kubernetes

End-to-end platforms

Tools that cover the whole ML lifecycle in one platform. They are often opinionated, so this guide prefers composable tools.

  • MLflow - Open-source platform for the machine learning lifecycle
  • MLRun - Open-source framework to orchestrate MLOps from research to production