Comparison
evalml vs awesome-mlops
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.
Markdown twin · evalml alternatives · awesome-mlops alternatives
GraphCanon updated Oct 4, 2026
11views this month
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Trust & integrity
| Signal | evalml | awesome-mlops |
|---|---|---|
| Maintenance | Slowing (258d since push) As of Sep 30, 2026 · github_public_v1 | Steady (48d since push) As of Oct 4, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 30, 2026 · github_public_v1 | Not a fork · Personal account As of Oct 4, 2026 · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- evalml
- An AutoML library written in Python
- awesome-mlops
- A curated list of awesome MLOps tools.
Stars
- evalml
- 850
- awesome-mlops
- 5.3k
Forks
- evalml
- 96
- awesome-mlops
- 792
Open issues
- evalml
- 325
- awesome-mlops
- 98
Language
- evalml
- Python
- awesome-mlops
- Python
Adopt for
- evalml
- 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
- Awesome MLOps is a curated list of tools encompassing AutoML to CI/CD for ML.
Persona
- evalml
- -
- awesome-mlops
- -
Runtime
- evalml
- -
- awesome-mlops
- -
License
- evalml
- EvalML uses the BSD-3-Clause license which allows free use, modification, and distribution but requires preservation of copyright notices.
- awesome-mlops
- -
Last pushed
- evalml
- Jan 14, 2026
- awesome-mlops
- Aug 17, 2026
Categories
- evalml
- Evaluation & Observability, Model Training
- awesome-mlops
- Developer Tools, Evaluation & Observability, Inference & Serving, Model Training
Trust and health
Maintenance
- evalml
- Slowing (36%)
- awesome-mlops
- Steady (60%)
Days since push
- evalml
- 258d
- awesome-mlops
- 48d
Open issues (now)
- evalml
- 325
- awesome-mlops
- 98
Stars delta
- evalml
- -2 (30d)
- awesome-mlops
- +55 (30d)
Open issues delta
- evalml
- +1 (30d)
- awesome-mlops
- +27 (30d)
Owner type
- evalml
- Organization
- awesome-mlops
- User
Full report
- evalml
- Trust report
- awesome-mlops
- Trust report
Shared compatibility
- Python · evalml: Python runtime · awesome-mlops: Python runtime
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.
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (alteryx/evalml) · observed Sep 30, 2026
- GitHub forks (alteryx/evalml) · observed Sep 30, 2026
- Last push (alteryx/evalml) · observed Jan 14, 2026
- License file (BSD-3-Clause) · observed Sep 30, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (kelvins/awesome-mlops) · observed Oct 4, 2026
- GitHub forks (kelvins/awesome-mlops) · observed Oct 4, 2026
- Last push (kelvins/awesome-mlops) · observed Aug 17, 2026
- License file (unknown) · observed Oct 4, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: evalml 850 · awesome-mlops 5.3k (synced Sep 30, 2026).
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 (5,284 vs 850). 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 and awesome-mlops alternatives (evalml markdown twin, awesome-mlops markdown twin), 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 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: Steady. 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; awesome-mlops trust report.