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For data science and ML teams

Get your models out of notebooks and into production

Putting a machine learning model into production means making its training reproducible, its releases tested and reversible, and its behavior monitored once real data reaches it. We build that path around the models your team already has, so a retrain becomes a routine release.

Free first conversation · A person replies within one business day · You own the code and accounts

01 Outcomes

What changes for you

  • Training anyone can rerun

    Data, code and settings are versioned together, so last quarter's model can be rebuilt and every result traced to the run that produced it.

  • Releases with a way back

    Each model is tested against agreed thresholds before it ships, served behind a stable interface and rolled back in one step if it misbehaves.

  • Drift you hear about first

    Accuracy, input drift, latency and cost are watched in production, so the model's owner usually hears about a problem before a business user does.

Mark where you are

Mark where your ML practice is today

For each area, pick the stage that sounds most like your team. The next step appears beside it, so you leave with a short list of what to do, not a score.

Mark the stage closest to how this works today.

Training

Next step

Deployment

Next step

Testing

Next step

Monitoring

Next step

Talk this through with us →

02 How it starts

How it starts

  1. Pick one model that matters

    We choose a model the business already relies on, or wants to, and map how it is trained, shipped and checked today.

  2. Build its path to production

    Versioned training, a registry, tests with agreed thresholds and an automated release, built in your cloud and your repositories.

  3. Watch it in production

    Monitoring for drift, accuracy, latency and cost goes live, with an owner and a runbook for each alert.

  4. Decide what to reuse

    You review the pipeline on one real model, then choose whether to extend it to the next models or stop there.

The usual engagement model: Fixed-scope implementation

03 Start

Pick the model to put into production first

Book a call with an engineer. Tell us how your models are trained and shipped today, and we will suggest the first step worth taking.

What happens next

  1. You pick a time, or send us a message.
  2. A person at Nexstella reads what you shared before we talk.
  3. You leave with a clear next step, even when that step is to wait.

Or write to [email protected]. Send a brief instead

Meet

Pick a time with an engineer

What is your work email? The calendar invite and the video link go there.

The invite goes to the calendar you work from. Personal address? Use the Contact tab . If you stop before picking a time, we send one reminder to this address, and nothing else.

30 minutes, video call with an engineer · Times show in your time zone

What happens next

  1. You pick a time, or send us a message.
  2. A person at Nexstella reads what you shared before we talk.
  3. You leave with a clear next step, even when that step is to wait.

Contact

Send us a message

Pick the topic that fits and tell us what you are working on. A person replies within one business day.

A person replies from a real address.

Phone (optional)

Choose the country code, then type the number without it.

The topic leads the subject line, so the right person picks it up first.

A few sentences is plenty. You do not need to know which technology you need.

We use what you send to reply and, if it leads somewhere, to scope the work. Read the privacy notice, or write to [email protected].

Want the full picture? Read MLOps, LLMOps and AI platform engineering.

Also written for you: Enterprise AI to production , AI features for SaaS .

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