MLOps for a team of one
Put a model into use responsibly with the few practices that matter when you work alone
A taste of a lesson
I built a churn model in a notebook. My manager wants scores in our CRM weekly. Do I need a full serving system?
Probably not. Weekly scores suggest a scheduled batch job: load the latest customer data, apply the same preprocessing you used in training, score everyone, and write the results to the CRM. Move the notebook code into a script, pin the model file and data snapshot it uses, and log how many customers were scored and the score distribution each week. If the distribution suddenly shifts, you have an early warning. Keep last week's model ready to rerun. Quick check: what would you compare each week to notice that something in the input data changed?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Describe the stages of a model's life from data to retirement
- Apply a minimum set of versioning, tracking and evaluation habits to one project
- Choose the simplest deployment that meets the real need
- Set up basic monitoring with a few meaningful alerts
- Write a one page model note covering purpose, limits, retraining and rollback
Lesson plan
- 1 The life of a model Map your model's journey from data to retirement and spot where it can break. Start
- 2 Version everything that changes the model Make any model you deploy reproducible from recorded code, data and settings. Start
- 3 Evaluation you can trust Keep an honest holdout set and a baseline to compare every new model against. Start
- 4 The simplest deployment that works Pick between batch jobs, a small service or embedding in an existing app. Start
- 5 Monitoring with a few good alerts Watch traffic, errors, latency and a quality signal without drowning in alerts. Start
- 6 Write it down and grow carefully Document the model on one page and add automation only where repetition hurts. Start
Try asking
About this tutor
For solo developers, analysts and researchers who have a model that works in a notebook and need to put it into real use without a platform team. You will walk through the life of a model from data to monitoring and learn the minimum practices that prevent the most common failures: versioned code and data, a simple experiment log, a protected holdout set, a plain and boring deployment, and a dashboard with a couple of alerts. Just as important, you learn what to skip until you need it. Each lesson ends with one small change you can make to your own project that week.
Reviews
4.7
3 ratingsSample
- Kofi B.Sample
The 'can you rebuild last month's model' test was humbling. I could not. Now I can. Wanted a bit more on scheduling options but it stays tool neutral on purpose.
- Andrea P.Sample
I am the only data person at a small nonprofit. This talked me out of three tools I did not need and into a batch job and a spreadsheet log that actually work.
- Sunita R.Sample
Short, practical lessons with one change per week. The one page model note is now required on my team.
About the teacher
MLOps without the ceremony: tracking, versioning, monitoring and responsible deployment
9 tutors 322 lessons taught Sample
I teach the habits that keep machine learning systems trustworthy after the notebook: tracking experiments, versioning data and models, testing, monitoring, handling incidents and documenting models honestly. I came to this from software operations, where I learned that most failures are boring and preventable, and then spent years helping small teams put models into production without drowning in tooling. I...
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