--- title: "evalml vs awesome-mlops" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/alteryx-evalml-vs-kelvins-awesome-mlops" tools: ["alteryx-evalml", "kelvins-awesome-mlops"] --- # evalml vs awesome-mlops *GraphCanon updated Aug 4, 2026* ## Verdict Pick evalml if evalML serves Python users seeking automated machine learning services with streamlined feature engineering, selection, and hyperparameter tuning, underpinned by the BSD-3-Clause license; pick awesome-mlops if awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. [evalml](https://evalml.alteryx.com) reports 852 GitHub stars, 93 forks, and 324 open issues, last pushed Jan 14, 2026. [awesome-mlops](https://github.com/kelvins/awesome-mlops) has 5.2k stars, 762 forks, and 71 open issues, last pushed Apr 29, 2026. Figures are from public GitHub metadata via [evalml's repository](https://github.com/alteryx/evalml) and [awesome-mlops's repository](https://github.com/kelvins/awesome-mlops). | | [evalml](/tools/alteryx-evalml.md) | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | | --- | --- | --- | | Tagline | An AutoML library written in Python | A curated list of awesome MLOps tools. | | Stars | 852 | 5,229 | | Forks | 93 | 762 | | Open issues | 324 | 71 | | Language | Python | Python | | Adopt for | EvalML serves Python users seeking automated machine learning services with streamlined feature engineering, selection, and hyperparameter tuning, underpinned by the BSD-3-Clause license. | Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. | | Persona | - | - | | Runtime | - | - | | License | EvalML uses the BSD-3-Clause license which allows free use, modification, and distribution but requires preservation of copyright notices. | - | | Categories | Evaluation & Observability, Model Training | Developer Tools, Evaluation & Observability, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [evalml](/tools/alteryx-evalml.md) | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | | --- | --- | --- | | Days since push | 201d | 97d | | Open issues (now) | 324 | 71 | | Owner type | Organization | User | | Full report | [trust report](/tools/alteryx-evalml/trust.md) | [trust report](/tools/kelvins-awesome-mlops/trust.md) | ## Shared compatibility - **Python**: [evalml](/tools/alteryx-evalml.md) - Python runtime; [awesome-mlops](/tools/kelvins-awesome-mlops.md) - Python runtime ## Decision facts: evalml - **Pricing:** freemium - Access to features comes at no cost due to its open-source nature; however, premium support can be purchased. - **Requirements:** Min 2 GB RAM - **Adopt for:** EvalML serves Python users seeking automated machine learning services with streamlined feature engineering, selection, and hyperparameter tuning, underpinned by the BSD-3-Clause license. - **License detail:** EvalML uses the BSD-3-Clause license which allows free use, modification, and distribution but requires preservation of copyright notices. ## Decision facts: awesome-mlops - **Adopt for:** Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML. ## Choose when ### Choose evalml if… - Pricing: Access to features comes at no cost due to its open-source nature; however, premium support can be purchased.. - Requirements: Min 2 GB RAM. - Tags unique to evalml: automl, feature-engineering, feature-selection, hyperparameter-tuning. - You value an intuitive API for automating model training processes in Python contexts where feature engineering and selection are critical. ### Choose awesome-mlops if… - Tags unique to awesome-mlops: ai, awesome, machine-learning-engineering, ml. - Also covers Developer Tools, Inference & Serving. - You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving. ## When NOT to use evalml - You require deep customization of feature engineering processes that go beyond what EvalML automates out-of-the-box. - Your team prefers tools that offer more advanced explainability features for model decisions and behavior analysis, as this is a focus area lacking specific mention in EvalML's capabilities. ## When NOT to use awesome-mlops - In search of a single comprehensive tool for end-to-end ML project management; Awesome MLOps is a repository of links rather than a standalone platform. - Looking for proprietary solutions or detailed vendor-specific documentation as it focuses on broad, open-source offerings. ## Common questions ### What is the difference between evalml and awesome-mlops? evalml: An AutoML library written in Python. awesome-mlops: A curated list of awesome MLOps tools.. See the comparison table for live GitHub stats and shared categories. ### When should I choose evalml over awesome-mlops? Choose evalml over awesome-mlops when Pricing: Access to features comes at no cost due to its open-source nature; however, premium support can be purchased.; Requirements: Min 2 GB RAM; Tags unique to evalml: automl, feature-engineering, feature-selection, hyperparameter-tuning; You value an intuitive API for automating model training processes in Python contexts where feature engineering and selection are critical. ### When should I choose awesome-mlops over evalml? Choose awesome-mlops over evalml when Tags unique to awesome-mlops: ai, awesome, machine-learning-engineering, ml; Also covers Developer Tools, Inference & Serving; You need resources across multiple facets of the machine-learning pipeline, from data validation to model serving. ### When should I avoid evalml? You require deep customization of feature engineering processes that go beyond what EvalML automates out-of-the-box. Your team prefers tools that offer more advanced explainability features for model decisions and behavior analysis, as this is a focus area lacking specific mention in EvalML's capabilities. ### When should I avoid awesome-mlops? In search of a single comprehensive tool for end-to-end ML project management; Awesome MLOps is a repository of links rather than a standalone platform. Looking for proprietary solutions or detailed vendor-specific documentation as it focuses on broad, open-source offerings. ### Is evalml or awesome-mlops more popular on GitHub? awesome-mlops has more GitHub stars (5,229 vs 852). Stars measure visibility, not whether either tool fits your constraints. ### Are evalml and awesome-mlops open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to evalml or awesome-mlops? GraphCanon lists graph-backed alternatives at [evalml alternatives](/tools/alteryx-evalml/alternatives) and [awesome-mlops alternatives](/tools/kelvins-awesome-mlops/alternatives) ([evalml markdown twin](/tools/alteryx-evalml/alternatives.md), [awesome-mlops markdown twin](/tools/kelvins-awesome-mlops/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-kelvins-awesome-mlops.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, evalml or awesome-mlops? evalml: Slowing. awesome-mlops: Slowing. 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-mlops? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [evalml trust report](/tools/alteryx-evalml/trust); [awesome-mlops trust report](/tools/kelvins-awesome-mlops/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/_