Solutions

Built for the teams that ship models

The same product, used differently by different teams. Each section below describes the problem as the team usually states it, how maclnote is set up for it, and what changes.

Data science teams

Stop losing the run that worked

A team of five data scientists, each with a folder of notebooks named final_v2_really.ipynb, a shared drive of CSVs and a Slack thread that is the only record of which model is in production.

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The problem

  • Results cannot be reproduced two weeks later.
  • Nobody knows which data a model was trained on.
  • Hand-off to engineering is a zip file and a meeting.
  • Experiments are compared by memory.

With maclnote

  • Every cell run is a tracked, reproducible experiment.
  • Dataset versions are attached to runs automatically.
  • Registered models carry everything engineering needs.
  • Runs are compared side by side and shared by link.
Outcome. Reproducible work by default, a single answer to "what is in production", and hand-offs that take a link instead of a meeting.
ML engineering teams

Support twenty data scientists without building the tools yourself

A small engineering team that keeps shared GPU notebooks running, stores experiment results, holds the approved models and hosts the prediction APIs for twenty data scientists, and spends most of its time keeping those pieces talking to each other.

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The problem

  • Experiment results, approved models and prediction APIs live in separate systems with separate logins.
  • Upgrading one breaks how it talks to the others.
  • Every request for a GPU or a new deployment is a ticket.
  • "Which data was this model trained on?" takes days to answer.

With maclnote

  • Training runs, models and prediction APIs in one product with one set of permissions.
  • Runs in your own cloud account, with your single sign-on.
  • Data scientists attach GPUs and deploy models themselves, within limits you set.
  • Training data, run, model and API on one lineage page.
Outcome. The engineering team stops maintaining plumbing and starts setting rules: GPU quotas, who must approve a model before it serves predictions, allowed regions and cost limits, enforced by the product.
Research labs

Thousands of runs, every artifact kept

A research group running large hyperparameter sweeps across a GPU cluster, with results scattered across log files, spreadsheets and a wandb project that ran out of storage.

Ask about academic pricing

The problem

  • Sweeps are launched by hand and tracked by convention.
  • Checkpoints and plots go missing before the paper is written.
  • Reviewers ask for the exact setup behind a figure.
  • Onboarding a new student takes a month.

With maclnote

  • Sweeps launched from a cell, tracked as one group.
  • Artifacts stored with the run, retained as long as you choose.
  • Every figure links to its run, notebook, commit and data version.
  • New members open a shared notebook with compute attached.
Outcome. Results that can be reproduced by someone else, which is the point of research, and less time spent finding which of forty checkpoints produced the good number.
Startups

A model in production this week, not this quarter

Two engineers and a founder with a churn model that works in a notebook. It needs to be answering prediction requests by Friday and still be accurate, and known to be accurate, in three months.

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The problem

  • No one to build a prediction API, accuracy monitoring or automated retraining.
  • Shortcuts taken under deadline become permanent.
  • The model's accuracy drops as the customer base changes and nobody notices.
  • GPU costs are a surprise at month end.

With maclnote

  • Prediction API deployed in one click, scaling with traffic, every request logged.
  • Move to your own cloud later without changing the notebook.
  • Feature drift and accuracy alerts to Slack from the first day.
  • GPU time billed by the minute with a cap per project.
Outcome. A monitored, versioned prediction API run by a two-person team, and a path to your own cloud when you are ready for it.
Regulated industries

Lineage, approvals and audit for every model

A bank, insurer or healthcare provider where every model in production must be explainable to a regulator, approved by a second line of defence, and traceable to its training data on request.

Read about security

The problem

  • Model risk reviews rely on documents assembled by hand.
  • Training data cannot be reproduced after the fact.
  • Approvals live in email.
  • Data cannot leave a specific region or network.

With maclnote

  • Model cards generated from the run, with lineage attached.
  • Dataset versions retained under your retention policy.
  • Required reviewers per model stage, recorded in the audit log.
  • Self-hosted or single-tenant in your region and VPC.
Outcome. Model governance that is a by-product of doing the work, not a separate project, and audit responses that take an afternoon instead of a quarter.
Universities & courses

A GPU notebook for every student

A machine learning course or bootcamp where students need real compute, instructors need to see their work, and IT does not want to run a cluster.

Ask about education plans

The problem

  • Student laptops cannot train the models in the syllabus.
  • Submissions arrive as zip files that do not run.
  • Instructors cannot see where students got stuck.
  • Free-tier notebooks disconnect mid-training.

With maclnote

  • Each student gets GPU notebooks with a compute budget.
  • Assignments are submitted as tracked runs that reproduce.
  • Instructors see every run, metric and notebook in one view.
  • Runtimes persist for the length of the job.
Outcome. Students train real models on real GPUs, instructors grade runs they can re-execute, and nobody administers a cluster.

Which one sounds like your team?

Tell us on a 30-minute call and we will set up maclnote to match, with your own notebook and training data in it.

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