The modelling the commercial ML platforms charge for, free: deep feature engineering, stacked ensembles, forecasting, anomaly detection, regulated‑model templates and interpretation down to every rule. Your own AI agent does the work and you make the decisions. See how it compares →
docker run -d --name parleml -p 8000:8000 -v parleml-data:/app/data_root ghcr.io/parleml/parleml:0.8.0
Mac with Apple silicon: turn on Rosetta in Docker Desktop and add --platform linux/amd64. Details
Built on the strongest open-source engines, rather than yet another booster
Deep feature engineering, stacked ensembles or deep hyperparameter search, foundation models, regulated-model templates and fast Treelite scoring. All built in, not an add-on.
Three rule engines write plain if-then rules. Every rule carries its Train and its Test result, so you see at once which ones hold up. Plus archetypes, interactions and a trust summary of nine checks.
IF police_report = no AND claim_to_value > 0.9 THEN fraud
IF vehicle_make = X AND days_to_report < 2 THEN fraud
Every one of them explained.
The no-skill floor is shown before you train. An ensemble ships only if it beats the simpler model.
It sees every result and explanation, runs the checks every time, and hands the decisions to you. Who answered, and when, is on record.
Weight-of-evidence scorecards, glass-box and monotonic models, fairness by any column you mark, reason codes for scored rows, AutoDoc model cards and sign-off records.
| prior_claims_count = 0 | +38 |
| policy_tenure_months ≥ 36 | +24 |
| police_report_filed = no | −41 |
No telemetry, no usage reporting, no update checks, and the libraries' own telemetry switched off. Use a local model for the agents and ParleML runs fully offline.
Agents can already do a lot. What they lack is a reliable ML engine and the evidence to make good decisions. ParleML gives them both, in both directions.
Codex, Gemini CLI or a local model run whole sessions: frame, build, challenge, release. Claude Code and Cursor too, with the extended image. You follow them on a live flow map.
An MCP server with 26 tools in three permission levels, interactive panels in Claude Desktop, ChatGPT, VS Code and Cursor, and 17 skills you can export to your own agent.
A ChatGPT, Claude or Google AI subscription, an API key, or a local model through Ollama (or LM Studio, with Codex). ParleML adds no charge on top.
Tip: a long ML session reads a lot of results, so a flat monthly plan can cost far less than per-token billing. Agents in depth
Real screens from a running ParleML on its sample workspaces: classification to forecasting, clusters and anomalies, your data, and the release record. Pick one, or click the picture to see it full size.
Each product category, each product, and the sub-models it was split into (new, existing, returning customers, or one unified model), coloured by F1.
1 / 15What is under the hood, in the same words as the manual, and straight answers. Open any part.
ParleML builds on AutoGluon, LightGBM, XGBoost, CatBoost and neural networks rather than writing yet another booster. AutoGluon's multi-layer stacking is one option, not the only one. Each experiment uses a template, and every template can be opened and changed:
| You want | Templates |
|---|---|
| A sensible start | Default, Quick Explore |
| Top accuracy by breadth and stacking | High Accuracy, Diverse Ensemble |
| Deep tuning, the Driverless AI and mljar approach: an in-depth search on one or several model families, with an ensemble of the best on top | Tuned Single, Tuned Ensemble, Reliable Tuned Ensemble, Tuned Variants |
| Foundation models | Foundation Ensemble (TabICL as an ensemble member) |
| A model a regulator can read | Regulated Glassbox (GLM, EBM or RuleFit), Regulated Scorecard (weight of evidence, coefficients as points), Regulated Monotonic |
| Fast scoring | Real Time: one tuned booster, scored through Treelite |
| Your data's shape | Large Dataset, Small Data, Text Heavy |
Underneath: grouped and time-aware validation, a sealed test set, Optuna search, class-imbalance treatment chosen by search (class weights), SMOTE-family resampling, automatic monotonic constraints, sample weights, GPU training and calibration (isotonic, Platt, temperature).
Driverless AI's real strength is its range of automatic transformers. Going by its 2.5.1 documentation (as of 18 October 2026), ParleML has a counterpart for nearly every one, then goes further:
| Driverless AI transformers | ParleML |
|---|---|
| CVTargetEncode, NumCatTE, NumToCatTE, WeightOfEvidence, NumToCatWoE (monotonic) | Six target encoders (mean, weight of evidence, James-Stein, M-estimate and others) and a count encoder, fitted inside the folds |
| Interactions, InteractionsSimple | Tree-guided interactions: the pairs and operations chosen by a model, with an optional feedback round |
| CVCatNumEncode | Group aggregates of a number within a category, plus within-group percentile ranks |
| ClusterId, ClusterDist, Binner | Clustering encoder and learned bins |
| OneHotEncoding, Frequent, LexiLabelEncoder and SortedLE, StandardScaler, QuantileTransformerNormal and QuantileTransformerUniform | Category handling, contrast coding (Helmert, backward difference, sum), power transforms, scaling for linear models |
| Dates, IsHoliday, DatePolar | Calendar parts and cyclical encodings, plus optional holiday and business-hours flags |
| Lags, EwmaLags, LagsAggregates | Per-entity lags, EWMA, rolling windows and time since the last event, when you switch them on |
| Text, TextLinModel, BERT, TextCNNV2 and TextBiGRUV2, TextClustTE and TextClustDist | See the text section below |
Beyond what we found in the other platforms:
Driverless AI also offers image features and ELM features, which ParleML doesn't have yet. Isolation forest, which Driverless AI uses to add an anomaly-score column, is in ParleML a full anomaly detector, one of 23.
Most platforms treat a text column as prose. ParleML tells four kinds of text column apart and gives each the encoder that fits it:
| Kind of column | Example | What ParleML does |
|---|---|---|
| Prose | complaint notes, claim descriptions | TF-IDF topics plus a cross-validated linear text signal, or neural sentence embeddings |
| Tag and keyword lists | action|comedy|drama | Every token target-encoded on its own and combined per row, per class for multiclass, fitted inside each fold |
| Packed counts | Netflix(2), McAfee(1) | Tokens and their counts parsed and compressed into a few topic columns |
| Messy short strings | job titles, merchant names with typos | Gamma-Poisson topics or MinHash fingerprints of character patterns |
We haven't found encoders for tag lists or packed counts in the other platforms we compared: a standard encoder sees each such cell as one rare category and learns almost nothing from it.
Around the encoders:
As documented, Driverless AI covers prose with TF-IDF, a linear text model, CNN and GRU networks and BERT. It also explains single rows token by token (NLP LOCO), which ParleML doesn't do yet.
The standard views the commercial platforms ship are included (some also offer K-LIME or XEMP, which ParleML doesn't):
| Question | Views |
|---|---|
| What drives the model? | Native, SHAP and permutation importance |
| How does each feature move the prediction? | Partial dependence, ICE, ALE |
| Why this prediction? | SHAP reason codes, LOCO, what-if and sensitivity |
| Can a simple model mimic it? | Surrogate decision tree, residual surrogate |
| Is it fair? | Disparate impact by any column you mark |
| Can I document it? | AutoDoc model card |
Then views we haven't found in the other platforms we compared:
| View | What it shows |
|---|---|
| Archetypes | The groups inside each class the model sees, named, with members, stability and errors |
| Three rule engines | Plain if-then rules, each with its Train and Test result; one engine maximises business value |
| SHAP interactions and compacity | Which features act together, and how few features explain a decision |
| Archetype counterfactuals | What would move a case from its risky group towards a safer one, and how much each feature changes |
| Trust summary | Nine checks, from lift and calibration to drift, fairness and rule survival |
| Label-noise review | Rows a model that never trained on them confidently disputes (confident learning, as in cleanlab), listed as candidates to review |
Also included: an error tree that finds the groups of rows where the model is wrong most.
Forecasts, clusters, anomalies, ranking, recommendations and basket rules are explained too, each in the way that fits it. Classifiers and regressors trained elsewhere (scikit-learn, LightGBM, XGBoost, CatBoost) can be brought in and explained the same way.
ParleML doesn't deploy or monitor models in production. Use it with your MLOps tooling: models export as Python packages and MLflow models.
Compared with SAS Viya, the incumbent in banks and insurers (from SAS's public pages, as of 18 October 2026): SAS leads on enterprise model risk management (model inventory and validation workflows), offered as its own product, with credit scorecards in a Risk Modeling add-on. ParleML covers much of the regulated modelling itself for free: weight-of-evidence scorecards, glass-box and monotonic models, fairness checks, reason codes, model cards and sign-off records.
| Problem | What you get |
|---|---|
| Classification and regression | Binary, multiclass and regression, with calibration, thresholds and business value |
| Forecasting | Four backends, 28 named models plus AutoGluon's zoo: 19 statistical (ARIMA, ETS, Theta, CES, TBATS, MSTL, intermittent demand), LightGBM, XGBoost and CatBoost on lags, six Chronos foundation checkpoints, and AutoGluon-TimeSeries. Race them per series, with prediction intervals |
| Ranking | Learning to rank with LambdaRank, tuned for NDCG |
| Recommendation | Next best product, with calibrated models per product and customer group |
| Basket rules | Association rules tested on held-out baskets, corrected for multiple testing |
| Clustering | Seven algorithms, the number of clusters chosen for you, clusters named |
| Anomaly detection | 23 detectors with an automatic mode, and the reason each row was flagged |
Agents can already do a lot. What they lack is a reliable ML engine and the evidence to make good decisions. ParleML gives them both, in both directions:
Your choice of access. The agents inside ParleML are the real Codex, Claude Code and Gemini CLI. Sign them in with a ChatGPT, Claude or Google AI subscription, use an API key, or run a local model with Codex (Ollama or LM Studio) or Claude Code (Ollama). A tip: a long ML session reads a lot of results, so a flat monthly plan can cost far less than per-token billing.
| Agents inside ParleML | ParleML inside your agent |
|---|---|
| Codex and Gemini CLI in the standard image, Claude Code and Cursor in the extended image; local models with Codex (Ollama or LM Studio) or Claude Code (Ollama) | An MCP server with 26 tools in three permission levels: read, compute, builder |
| Sessions that frame, build, challenge and release models, with a live flow map | Interactive ParleML panels inside Claude Desktop, ChatGPT, VS Code and Cursor |
| Decisions go to people. Who answered, and when, is recorded | Sign in from claude.ai or ChatGPT, or use an API token; build tokens are bound to chosen workspaces |
| 17 shipped skills, plus your own skills, rules and hooks | The same skills for your own agent, exported from Settings → Skill packs |
Every object records the session and step that made it. Plans are reviewed before launch, can carry a stop rule, and a launch budget caps how many runs, and how large, an agent may start. You can work through the agent, the classic pages, or both.
A capable coding agent with scikit-learn and LightGBM can train you a model in minutes. What it doesn't give you on its own:
| A coding agent on its own | The same agent with ParleML |
|---|---|
| Writes new ML code every session, and you review that code | Calls tested tools and skills; you review results and decisions, not code |
| Explanations it improvises: a SHAP plot, if you ask | Interpretation computed for every model, in every kind of problem |
| Checks it may or may not remember | Leakage, the no-skill baseline, held-out tests for every rule and point-in-time checks, every time |
| Choices made silently in code | Decisions handed to you, with who answered and when on record |
| Results scattered over notebooks and chat history | Every dataset, experiment, model and decision kept in the workspace |
| Its context window fills with code and stack traces | Its context holds results and evidence, so it reasons further |
It's the same agent, with the same access. ParleML makes its work reliable, repeatable and reviewable.
The comparisons below are our reading of each product's public documentation, release notes and product pages, as of 18 October 2026. They are offered in good faith, to the best of our knowledge, to help you choose; they are not a test of the products. "Not found in public docs" means we found no mention, not that a product can't do it. Products change often, and we may have missed or misread something. If anything here is wrong or out of date, write to support@parleml.com and we will correct it as soon as we can confirm it. Product names are trademarks of their owners; ParleML is not affiliated with or endorsed by any of them. The sources are listed at the end of this section.
As of 18 October 2026, from each vendor's documentation, release notes and product pages:
Swipe sideways to see every product →
| ParleML | Driverless AI | DataRobot | Dataiku | |
|---|---|---|---|---|
| AI agents | Claude Code, Codex, Gemini CLI, Cursor or a local model run governed ML sessions on your own access; MCP with interactive panels | No built-in agent; GPT summaries of datasets and experiments (off by default); H2O's separate h2oGPTe agents can run experiments once an administrator connects them | Platform to host your company's agents; a data science agent, mainly for data preparation; open-source skills and an MCP server for coding agents | Its own building agent (Cobuild), Claude Code and Codex in its Code Studios, agent hub |
| Price | Free, including commercial use | Commercial licence; demo or trial on request | Commercial, no public price; 30-day trial | Commercial, no public price; free on-premises edition; 14-day cloud trial |
| Interpretation | The full standard set with ICE and ALE, plus archetypes, rules with Train and Test results, trust summary | SHAP, K-LIME, PDP, ICE, surrogate tree, disparate impact; ALE only as a custom explainer recipe | SHAP, XEMP, feature impact, partial dependence, fairness; ICE in a code accelerator; ALE not found in public docs | SHAP and ICE-based row explanations, partial dependence, error tree, subpopulations, counterfactuals, fairness; ALE not found in public docs |
| Model documentation | AutoDoc model card for every kind of experiment, written from the run's recorded evidence: up to 17 sections, from data quality and feature engineering to drift, limitations, monitoring plan and sign-off, with the workspace's intended use, and the model's risk level and approver from its release record. Mapped to the EU AI Act, SR 11-7, PRA SS1/23 and NIST AI RMF, with a list of the evidence still missing. PDF, Word, HTML or Markdown | AutoDoc | Compliance documentation with a template builder; states SR 11-7 support | Model Document Generator (Word); an EU AI Act readiness solution |
| Data checks | Leakage, no-skill floor and a signal check before training (TabICL; Chronos for forecasts); mislabelled-row review; column meanings such as email, phone, address and IP suggested for you to accept | Leakage detection, on by default | Target-leakage and data-quality checks | Column meanings such as email, IP, URL and country; after training, leakage warnings and a check against a dummy model |
| Ranking | Learning to rank | Not found in public docs | Not found in public docs | Not found in public docs |
| Recommendation | Calibrated next best product | Not found in public docs | Recommendation engine as a code accelerator (multilabel model) | SQL collaborative-filtering plugin |
| Basket rules | Built in, tested on held-out baskets | Not found in public docs | Not found in public docs | Packaged industry solution |
| Forecasting | Four backends, 28 named models plus AutoGluon's zoo: 19 statistical (AutoARIMA, AutoETS, AutoTheta, AutoCES, MFLES, TBATS, MSTL, Croston, ADIDA, IMAPA, TSB and baselines), LightGBM, XGBoost and CatBoost on lags, six Chronos-2 and Chronos-Bolt checkpoints, and AutoGluon-TimeSeries (DeepAR, TFT, Toto-2, tabular). Raced per series, with prediction intervals | Lag features on its standard models | ARIMA, ETS, TBATS, Prophet, LSTM, DeepAR, GBMs | AutoARIMA, Prophet, DeepAR, Transformer, MQ-CNN, baselines; XGBoost, random forest and ridge on lags |
| Anomaly detection | 23 detectors (ECOD, COPOD, isolation forest, LOF, one-class SVM, autoencoder, VAE and more), an automatic mode, and the reason each row was flagged | Isolation Forest | Isolation Forest, LOF, one-class SVM, MAD, Mahalanobis, autoencoders | Isolation Forest |
| Feature engineering | Counterparts for nearly all of Driverless AI's, plus domain columns, tag lists, multi-table recipes checked point in time | 50+ transformers | Multi-table feature discovery | Pairwise interactions, encodings; genetic feature generation as a plugin |
| Free text | Four kinds of text column, each with its own encoder; words behind each outcome | TF-IDF, linear model, CNN, GRU, BERT; per-row token explanations | TF-IDF, small transformers, CNN; text explanations | TF-IDF, hashing, sentence or LLM embeddings |
| Glass-box and scorecard models | Points-based weight-of-evidence scorecard, EBM, GLM, RuleFit and monotonic boosters, as templates | Weight-of-evidence transformers, monotonic constraints, GLM, RuleFit, decision tree | GA2M rating tables, Eureqa GAM, RuleFit, monotonic constraints | Linear models, decision tree, monotonic constraints on CatBoost; credit scoring as a packaged solution |
| Identifiers | Card numbers, IBANs, phone numbers, emails, names and IP addresses pseudonymised before any model sees them | Not found in public docs | Personal-data detection that flags columns to leave out (preview, self-managed installs); pseudonymisation not found in public docs | Not found in public docs for tabular models |
| Release sign-off | A release record with the approval, an independent challenge, conditions and limitations | Not found in public docs | Deployment approval policies an administrator sets up, with a log of policy events | Sign-off in Govern, a separate node not in every licence |
| Clustering | Seven: k-means, mini-batch k-means, DBSCAN, HDBSCAN, Gaussian mixture, agglomerative, BIRCH. Cluster count chosen for you; clusters named and explained | K-Means (experimental) | K-Means, Gaussian mixture, HDBSCAN; separate licence | Eight clustering algorithms |
| Models | 15 model variants: LightGBM (5 modes), CatBoost (3), XGBoost (2), random forest, extra trees, linear and two neural nets. Plus glass-box EBM, GLM, RuleFit and scorecard, and the TabICL foundation model. Stacked or deeply tuned ensembles | XGBoost, LightGBM, CatBoost, random forest, GLM, decision tree, RuleFit, FTRL, zero-inflated, TensorFlow and PyTorch models (including BERT and GrowNet) | GBMs, GLM, GAM rating tables, Keras, Eureqa, RuleFit, SVM, KNN | Linear, forests, LightGBM, XGBoost, CatBoost, TabICL, neural nets, SVM, KNN |
| Classification and regression | Yes, with calibration, thresholds, business value and class-imbalance treatment chosen by search | Yes | Yes | Yes |
| Regression prediction intervals | On by default: conformal intervals, by quantile regression where it fits, so the width adapts per row, at 80% and 95%, flagged when the coverage can't be trusted | Empirical intervals from holdout predictions | Built in for time series; for other regression, a conformal method in a code accelerator | A helper model fitted on the residuals, 95% by default |
| Production | Python scoring package that refuses to load in an incompatible version, REST scoring, MLflow including Unity Catalog, Treelite | Python, Java and C++ scoring | Deployment, monitoring, approvals | Deployment, monitoring, Govern |
| Images | No | Yes | Yes | Yes |
These are libraries you code against. ParleML is a platform built on the strongest of them, AutoGluon, so it isn't on this list. As of 18 October 2026, from each project's documentation, release pages and PyPI:
Swipe sideways to see every product →
| ParleML | H2O-3 AutoML | mljar-supervised | PyCaret | FLAML | |
|---|---|---|---|---|---|
| Status | Free platform, closed source | Apache-2.0; only 3.46.0.x releases since March 2024 | MIT; actively released | MIT; last stable release April 2024, 4.0 in alpha | MIT; actively released (Microsoft) |
| How you use it | Web app, REST API, agents and MCP; the engines as Python libraries | Python, R, or the Flow notebook | Python; MLJAR Studio, a desktop app with free and paid plans, adds an AI Data Analyst | Python (a dashboard is coming in 4.0, now in alpha) | Python |
| Problems | Everything in the other columns, plus ranking, recommendation and basket rules | Classification, regression; clustering and Isolation Forest outside AutoML | Classification, regression | Classification, regression, forecasting, clustering, anomalies | Classification, regression, forecasting, ranking, text tasks |
| Interpretation | The full standard set, plus archetypes, tested rules, error tree, ALE, trust summary | SHAP on tree models, PDP, ICE, fairness, one-call report | SHAP, permutation importance, fairness, Markdown reports | SHAP plots, PDP, fairness check | Feature importance from the underlying model; no SHAP or explanation tools found |
| Feature engineering | See the feature-engineering section | Target encoding | Golden features, feature selection | Preprocessing pipeline | Minimal |
| Agents | Built in, with your own subscription, key or local model | None found in public docs | Studio only; subscription plans have monthly prompt quotas | None found in public docs | None found in public docs |
| Scoring | Python package, REST, MLflow, Treelite | Java MOJO and POJO | Saved Python models | In 3.3: cloud and Docker deploy, transpile to Java, C or Go (removed in 4.0) | Python objects |
Each is good at something: H2O-3 scores in Java with no Python and scales on a cluster; mljar-supervised is clean, actively released and writes clear reports; PyCaret 3.3 deploys in one call; FLAML tunes fast and cheaply, and fine-tunes transformer models for text.
As we read their documentation, DataRobot focuses on a platform for building, hosting and governing your company's own agents (an LLM gateway, guardrails, evaluation, tracing), with open-source skills and an MCP server so coding agents can call it from outside.
Dataiku has its own building agent, Cobuild, run on Dataiku AI Services or your own models, and an agent hub for business agents. Dataiku Headless lets Claude Code, Codex and Cursor drive Cobuild from outside, coding CLIs run in its built-in IDE, and its MCP server exposes the agents you build there.
ParleML takes another road. It doesn't host your agents or sell its own. It makes the agents you already use good at machine learning:
No. No telemetry, no usage reporting, no update checks. ParleML also switches off the telemetry of the libraries and agent CLIs it runs on.
The only traffic that leaves your machine is what you choose: your agents' calls to the AI provider you signed them in to, a model download the first time you use a model the image doesn't include, and connections you add yourself. Use a local model for the agents and ParleML runs fully offline. How to set it up · Privacy.
Free: yes, including commercial use. Exported scoring packages may be sold and distributed with the required ParleML runtime unchanged. Selling ParleML or charging for access to its platform is not permitted. See the license.
Open source: not at the moment. We are considering it.
Agents: ParleML adds nothing on top. Sign the agents in with the ChatGPT, Claude or Google AI plan you already have and they run on it, or use an API key, or a free local model through Ollama (or LM Studio, with Codex). Everything outside agent sessions needs no AI at all.
docker run -d --name parleml -p 8000:8000 -v parleml-data:/app/data_root ghcr.io/parleml/parleml:0.8.0
Mac with Apple silicon: turn on Rosetta in Docker Desktop and add --platform linux/amd64. Details
Questions or problems? support@parleml.com