Learn vocabulary for ML experiment tracking: runs, hyperparameters, metrics, artifacts, and comparison language.
0 / 10 completed
1 / 10
In ML experiment tracking, a 'run' refers to:
A run is one experiment execution — MLflow, W&B, and similar tools record hyperparameters, metrics, and artifacts for each run for comparison.
2 / 10
Which phrase best describes a hyperparameter in the context of experiment tracking?
Hyperparameters (learning rate, dropout, epochs) are configured before training — unlike model parameters (weights) which are learned during training.
3 / 10
An 'artifact' in MLflow or Weights & Biases is:
Artifacts are versioned outputs of a run — model weights, dataset snapshots, confusion matrix plots — tracked alongside metrics for reproducibility.
4 / 10
Communicating that a new experiment 'improved the baseline by 2.3 F1 points' means:
Improvement over baseline is expressed as an absolute difference in the metric (F1 points, accuracy percentage points) — not a multiplicative factor unless explicitly stated.
5 / 10
What is a 'sweep' in the context of hyperparameter optimisation?
A sweep (W&B) or hyperparameter search (MLflow) systematically explores hyperparameter combinations to find the best configuration.
6 / 10
Reviewer: 'The experiment log shows a high number of failed runs. Can you elaborate on the error rate and any specific parameters that might be contributing?'
Which statement best captures the reviewer's request regarding the experiment data?
The reviewer specifically asks for a detailed breakdown of run failures, emphasizing timestamps and parameter values—this demonstrates an interest in understanding *why* the runs failed, not just a general performance metric. Options B and C are dismissive of the critical error information, while option D is completely inappropriate for a code review context.
7 / 10
Alice (Data Scientist): 'I've just launched a new experiment using the wandb.init() command. I'm tracking metrics like loss and accuracy, and logging artifacts. I want to make sure the team is aware of this ongoing work.'
Which Slack message would be most appropriate for Alice to send to her colleagues?
Alice's message needs to provide sufficient context for her colleagues to understand what she's doing and how they can track the experiment. Including the command used (wandb.init()), metrics tracked, and the run ID are crucial details—option 1 is too vague. Option 3 is overly enthusiastic without providing specifics, and option 4 is completely inappropriate.
8 / 10
The MLflow Tracking API returns the following JSON response for a completed experiment:
The `run_id` is a crucial element in MLflow Tracking, acting as a unique identifier for each individual experiment run. This allows you to track and compare different versions of the same experiment. Options B, C, and D refer to other aspects of the experiment or its results.
PR Description: 'This PR configures MLflow tracking to automatically log metrics and artifacts for all new experiments. We've also added a script to visualize the experiment runs using the MLflow UI.'
Which of the following phrases best completes this PR description?
The description needs to clearly state the *benefit* of this configuration. Option 0 highlights the key advantage – improved model comparison—which is a common use case for experiment tracking. The other options are either misleading or directly contradict the purpose of the PR.
10 / 10
Ben (ML Engineer): 'I spent yesterday setting up a new experiment to optimize the learning rate for our image classification model. I'm using Weights & Biases to track the hyperparameter sweeps and visualize the results.'
Which statement best reflects Ben's update during the daily stand-up?
Ben's stand-up update needs to concisely communicate what he was working on and how it relates to the team's overall goals. Specifically mentioning the experiment, the tracked metric (learning rate), and W&B provides relevant information for his colleagues.
What will I practise in "ML Experiment Tracking Language"?
This module focuses on ML Language — real workplace phrasing you'll use on the job. It contains 10 scenario-based multiple-choice questions with instant feedback.
Is this exercise free to use?
Yes. Every exercise on CoderSlingo, including this one, is free to use with no account or sign-up required.
How many questions does this exercise have?
This module includes 10 questions. Each one gives an immediate right/wrong result plus a full explanation of the correct phrasing.
What happens if I answer a question incorrectly?
You'll see the correct answer highlighted straight away, along with a plain-English explanation of why it's right and why the other options don't fit — mistakes are part of the learning here.
Can I retry the exercise if I want a better score?
Yes — use the 'Try again' button on the results screen to reset your score and go through the questions again. There's no limit on attempts.
Who is this ML Language exercise for?
It's aimed at IT professionals with working English who want to sound more natural and precise around ml language — useful whether you're preparing for real conversations at work or just building confidence with the vocabulary.
Do I need an account to track my progress?
No account is needed. Your progress through the exercise is tracked locally in your browser for the current session, and you can replay the module at any time.
How is this different from reading a blog article?
This exercise is an interactive drill that tests and reinforces specific phrasing through multiple-choice questions with instant feedback, while blog articles explain concepts and vocabulary in prose. The two work well together.
Where can I find more ML Language exercises?
See the ML Language hub for more modules like this one, or browse the full Exercises page for other IT-English topics.
Can I complete this exercise on my phone?
Yes — every exercise on CoderSlingo is fully responsive and works on phones and tablets, so you can practise anywhere.