--- title: "evalml vs awesome-production-machine-learning" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/alteryx-evalml-vs-ethicalml-awesome-production-machine-learning" tools: ["alteryx-evalml", "ethicalml-awesome-production-machine-learning"] --- # evalml vs awesome-production-machine-learning *GraphCanon updated Jul 11, 2026* ## Verdict Pick evalml when license: evalml is BSD-3-Clause, awesome-production-machine-learning is MIT; pick awesome-production-machine-learning when license: awesome-production-machine-learning is MIT, evalml is BSD-3-Clause. [evalml](https://evalml.alteryx.com) reports 849 GitHub stars, 93 forks, and 324 open issues, last pushed Jan 14, 2026. [awesome-production-machine-learning](https://ethicalml.github.io/awesome-production-machine-learning) has 21k stars, 2.6k forks, and 32 open issues, last pushed Jul 3, 2026. Figures are from public GitHub metadata via [evalml's repository](https://github.com/alteryx/evalml) and [awesome-production-machine-learning's repository](https://github.com/EthicalML/awesome-production-machine-learning). | | [evalml](/tools/alteryx-evalml.md) | [awesome-production-machine-learning](/tools/ethicalml-awesome-production-machine-learning.md) | | --- | --- | --- | | Tagline | EvalML is an AutoML library written in python. | A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning | | Stars | 849 | 20,719 | | Forks | 93 | 2,585 | | Open issues | 324 | 32 | | Language | Python | - | | Adopt for | - | - | | Persona | - | - | | Runtime | - | - | | License | BSD-3-Clause | MIT | | Categories | Vector Databases, Evaluation & Observability | LLM Frameworks, AI Agents, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [evalml](/tools/alteryx-evalml.md) | [awesome-production-machine-learning](/tools/ethicalml-awesome-production-machine-learning.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Active (82%) | | Days since push | 178d | 8d | | Open issues (now) | 324 | 32 | | Full report | [trust report](/tools/alteryx-evalml/trust.md) | [trust report](/tools/ethicalml-awesome-production-machine-learning/trust.md) | ## Shared compatibility - **Python**: [evalml](/tools/alteryx-evalml.md) - Python runtime; [awesome-production-machine-learning](/tools/ethicalml-awesome-production-machine-learning.md) - Python runtime ## Choose when ### Choose evalml if… - License: evalml is BSD-3-Clause, awesome-production-machine-learning is MIT. - Tags unique to evalml: automl, data-science, model-selection, optimization. - Also covers Evaluation & Observability. ### Choose awesome-production-machine-learning if… - License: awesome-production-machine-learning is MIT, evalml is BSD-3-Clause. - Tags unique to awesome-production-machine-learning: awesome, deep-learning, data-mining, large-scale-ml. - Also covers LLM Frameworks, AI Agents. ## When NOT to use evalml - Last GitHub push was 178 days ago (slowing maintenance, Jan 14, 2026). Validate activity before betting a new project on evalml. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## When NOT to use awesome-production-machine-learning - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## Common questions ### What is the difference between evalml and awesome-production-machine-learning? evalml: EvalML is an AutoML library written in python.. awesome-production-machine-learning: A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning. See the comparison table for live GitHub stats and shared categories. ### When should I choose evalml over awesome-production-machine-learning? Choose evalml over awesome-production-machine-learning when License: evalml is BSD-3-Clause, awesome-production-machine-learning is MIT; Tags unique to evalml: automl, data-science, model-selection, optimization; Also covers Evaluation & Observability. ### When should I choose awesome-production-machine-learning over evalml? Choose awesome-production-machine-learning over evalml when License: awesome-production-machine-learning is MIT, evalml is BSD-3-Clause; Tags unique to awesome-production-machine-learning: awesome, deep-learning, data-mining, large-scale-ml; Also covers LLM Frameworks, AI Agents. ### When should I avoid evalml? Last GitHub push was 178 days ago (slowing maintenance, Jan 14, 2026). Validate activity before betting a new project on evalml. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### When should I avoid awesome-production-machine-learning? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### Is evalml or awesome-production-machine-learning more popular on GitHub? awesome-production-machine-learning has more GitHub stars (20,719 vs 849). Stars measure visibility, not whether either tool fits your constraints. ### Are evalml and awesome-production-machine-learning open source? Yes - both are open-source projects on GitHub (evalml: BSD-3-Clause, awesome-production-machine-learning: MIT). ### Where can I find alternatives to evalml or awesome-production-machine-learning? GraphCanon lists graph-backed alternatives at [evalml alternatives](/tools/alteryx-evalml/alternatives) and [awesome-production-machine-learning alternatives](/tools/ethicalml-awesome-production-machine-learning/alternatives) ([evalml markdown twin](/tools/alteryx-evalml/alternatives.md), [awesome-production-machine-learning markdown twin](/tools/ethicalml-awesome-production-machine-learning/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/alteryx-evalml-vs-ethicalml-awesome-production-machine-learning.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, evalml or awesome-production-machine-learning? evalml: Slowing. awesome-production-machine-learning: Active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for evalml and awesome-production-machine-learning? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [evalml trust report](/tools/alteryx-evalml/trust); [awesome-production-machine-learning trust report](/tools/ethicalml-awesome-production-machine-learning/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=alteryx-evalml`](/api/graphcanon/graph?tool=alteryx-evalml) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_