Experiment tracking you will actually use
Log runs so you can compare, reproduce and explain results months later, with any tool
A taste of a lesson
My new model scores 84.1 and the old one 83.6 on validation. Is that a real improvement?
Maybe, but one run each cannot tell you. Training randomness alone can move scores by that much. Rerun both models with three to five different seeds, keeping data split and evaluation code identical, and compare the means and the spread. If the ranges overlap heavily, treat it as no clear difference. Also ask how many variants you tried before this one: if it was the best of many, the 84.1 is likely optimistic. Quick exercise: write down the seeds you will use and what spread would convince you the gain is real.
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
- List the fields every run needs to be reproducible
- Set up consistent naming, tags and notes for experiments
- Compare runs fairly using shared splits and multiple seeds
- Recognise and avoid cherry picking when reporting results
- Choose a tracking approach that fits your scale and privacy needs
Lesson plan
- 1 What a run must record Define the minimum fields that make a run reproducible and comparable. Start
- 2 Logging automatically Capture run details from code so nothing depends on memory. Start
- 3 Names, tags and notes Organise runs so you and others can find and understand them later. Start
- 4 Comparing runs fairly Decide whether a change really helped rather than reflecting noise. Start
- 5 Honest reporting and choosing a tool Report results without selection bias and pick tracking that fits your context. Start
Try asking
About this tutor
For students and practitioners whose experiments live in scattered notebooks, file names like final_v3_really and half remembered settings. You will decide what every run must record, from code and data versions to seeds and environment, set up naming and tagging that stays readable, and learn to compare runs fairly instead of picking the best looking one. The course is tool neutral: the same habits work in a spreadsheet, a simple log file or a dedicated tracking tool, and the tutor helps you choose based on your team and scale. You also practise writing short notes on why each run exists, which is the part most people skip and later regret.
Reviews
4.3
3 ratingsSample
- Rashid K.Sample
Writing a one sentence hypothesis per run felt silly at first. Two months later it is the most useful column I have.
- Mateus C.Sample
The seed variance exercise was a shock. Half the 'wins' in my thesis log disappeared when I reran them. Better to find out now.
- Ingrid H.Sample
Simple and sensible. I stayed with a spreadsheet and saved configs, and for the first time I can answer my supervisor's 'which data was this' question.
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...
See Malik's profile and tutorsMore like this
Other tutors on the same or nearby topics.