Machine learning, end to end

Train, compare and deploy machine learning models from one notebook.

maclnote is where your team trains models on real GPUs, keeps the hyperparameters, learning curves and validation metrics of every run, versions the data each model was trained on, and puts the best model behind a prediction API that watches its own accuracy.

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PyTorch, TensorFlow, scikit-learn, XGBoost Free for individuals Cloud or your own GPUs
A maclnote notebook showing a training loop, a live loss curve, a neural network diagram and recorded accuracy per epoch
5 minFrom sign-up to your first recorded training run
40+ML frameworks, data sources and deployment targets
1 clickFrom best validation score to a live prediction API
NightlyDrift check of every deployed model against its training data
The product

Training the model is the easy part

Knowing which of your two hundred runs was actually best on held-out data, which rows it was trained on, whether it is overfitting, and how accurate it still is six months after deployment is the hard part. maclnote records all of that for every model, automatically, as you work.

Train on real GPUs, in a notebook

Jupyter-compatible notebooks with a GPU attached in seconds. Fit a gradient-boosted model on ten million rows or fine-tune a transformer, then release the GPU when training finishes.

Every training run recorded

Hyperparameters, learning curves, validation metrics per fold, confusion matrices and the random seed are captured from every fit. Compare two hundred runs in a table instead of from memory.

Training data, versioned

Each run knows the exact rows and features it trained on, and the split it was validated on. Retrain a model a year later on the same data, or trace a bad prediction back to the examples behind it.

Evaluate, approve, deploy

Compare candidate models on held-out metrics and per-segment accuracy, approve the winner with its evaluation report attached, and deploy it as a prediction API or nightly scoring job in one click.

Accuracy and drift after deployment

Live feature and prediction distributions are compared with the training data every night. When true labels arrive, accuracy, precision, recall and AUC are computed and plotted against the drift history.

An assistant that has read your experiments

Ask why validation loss started climbing at epoch 12, which features separate your two best runs, or what changed between the deployed model and the one before it. Answers cite your runs.
How it works

Four steps from training data to a monitored model

No migration project. Connect the table you already train on, open your notebook, and the first training run is recorded before the coffee is cold.

Connect your training data

Point maclnote at the warehouse table, bucket or file you train from. Each time a notebook reads it, the exact rows are fingerprinted and versioned, so every model knows what it learned from.

Train and experiment

Fit models the way you already do, in PyTorch, scikit-learn, XGBoost or anything else in Python. Hyperparameters, learning curves and validation metrics are recorded for every run, and sweeps run in parallel on GPUs.

Evaluate and approve

Compare runs on held-out metrics, calibration and accuracy per customer segment. Approve the best model with its evaluation report, training data version and code attached, so a reviewer sees everything on one page.

Deploy and watch accuracy

One click gives you a versioned prediction API or a scheduled scoring job. Feature drift is checked nightly, accuracy is computed as labels arrive, and rolling back to the previous model takes seconds.

Integrations

Works with the frameworks and data you already use

Train with any Python machine learning library, read training data from the warehouses and buckets where it already lives, and deploy where your engineers already run services. See all integrations.

PyTorch TensorFlow scikit-learn Hugging Face XGBoost MLflow import Snowflake BigQuery Amazon S3 Databricks GitHub Slack
Solutions

Built for the teams that train models

Whether you are three people with one churn model or fifty data scientists with hundreds, the problems are the same: reproducing a result, agreeing which model is best, and knowing how accurate the deployed one still is.

Data science

Data science teams

Stop losing the run that hit 0.91. Every training run is reproducible, comparable on held-out metrics and shareable by link.

 ML engineering

ML engineering teams

Give twenty data scientists GPUs, experiment history and prediction APIs without building and maintaining the tools yourself.

 Research

Research labs

Sweep thousands of hyperparameter configurations, keep every checkpoint, and hand a reviewer the exact notebook and data behind a figure.

 Startups

Startups

Put a model behind a prediction API this week, with accuracy monitoring, on a two-person team.

 Regulated

Regulated industries

Training data lineage, model approval workflows and audit logs for every model, in your own cloud.

 Education

Universities & courses

Give every student a GPU notebook and grade training runs you can re-execute, not zip files.
Pricing

Free to start, priced per seat

Unlimited training runs and models on every plan. You pay for the people who train models and the GPU minutes they use, not for how much they experiment.

Free

For individuals and side projects.

$0 / month
  • 1 user, unlimited notebooks
  • Experiment tracking and comparison
  • Shared CPU compute
  • 1 deployed model
Get started

Team

For teams shipping models together.

$39 / seat / month
  • Real-time collaborative notebooks
  • GPUs on demand, billed by the minute
  • Dataset versioning and lineage
  • Model registry, review and deployment
  • Monitoring and drift alerts
Start 14-day trial

Enterprise

For large ML teams and regulated industries.

Custom
  • Self-hosted or private cloud
  • SSO, SCIM and audit log export
  • Data residency and BYO model keys
  • 99.9% SLA and 24/7 support
Talk to sales

Compare every feature and read the FAQ →

Bring a model you are training to a 30-minute call

We will open your notebook in maclnote, train it with the run recorded, compare two configurations side by side, and deploy the better one as a prediction API. Live.

Book a demo See pricing