English for Machine Learning Engineers: Key Vocabulary and Communication
Master the English vocabulary ML engineers use daily — from model training and inference to experiment tracking and team communication.
Machine learning engineers work at the intersection of mathematics, software engineering, and research. To communicate effectively in English — whether writing experiment notes, reviewing model cards, or discussing trade-offs in a team meeting — you need precise vocabulary. This guide covers the core terms used across the ML lifecycle.
Model Training Vocabulary
Understanding training vocabulary lets you discuss experiments clearly with colleagues and write meaningful documentation.
| Term | Meaning | Example usage |
|---|---|---|
| Epoch | One complete pass through the training dataset | ”After 50 epochs, the validation loss plateaued.” |
| Loss function | A measure of how far the model’s predictions are from the true labels | ”We switched from cross-entropy to focal loss to handle class imbalance.” |
| Overfitting | When a model learns training data too well and fails to generalise | ”The model was overfitting — validation accuracy was 15% below training accuracy.” |
| Regularisation | Techniques that reduce overfitting (L1, L2, dropout) | “Adding L2 regularisation brought the gap down significantly.” |
| Gradient descent | The optimisation algorithm used to minimise the loss function | ”We used stochastic gradient descent with a cosine learning-rate schedule.” |
| Hyperparameter | A configuration value set before training begins | ”Batch size and learning rate are hyperparameters, not learned parameters.” |
Key Distinctions
- A parameter is learned during training; a hyperparameter is chosen by you.
- Training loss tells you how well the model fits the training data; validation loss tells you whether it generalises.
- Early stopping means halting training when validation loss stops improving.
Inference Vocabulary
Once a model is trained, you deploy it. These terms appear in architecture discussions and performance reviews.
| Term | Meaning |
|---|---|
| Latency | The time it takes to return a prediction for a single request |
| Throughput | The number of predictions the system can handle per second |
| Batching | Grouping multiple requests together to process them more efficiently |
| Quantisation | Reducing model precision (e.g. float32 → int8) to improve speed and reduce memory |
| Serving infrastructure | The system (API, container, accelerator) that runs the model in production |
When discussing inference performance, engineers often talk about the p99 latency — the worst-case latency experienced by 99% of requests. This is more meaningful than average latency for user-facing systems.
Team Communication: Experiment Tracking and Research Vocabulary
| Term | Meaning |
|---|---|
| Model card | A document describing a model’s intended use, performance, and limitations |
| Experiment tracking | Logging hyperparameters, metrics, and artefacts for each training run |
| Ablation study | Systematically removing components to understand their individual contributions |
| Baseline | A simple reference model used to judge whether a new approach is actually better |
| Artefact | A file produced by a training run — a checkpoint, a tokeniser, an evaluation report |
Useful Phrases for ML Team Meetings
- “The ablation shows that removing the data-augmentation step hurt performance by 3 percentage points.”
- “Let’s set a strong baseline before we try anything more complex.”
- “Can you log your hyperparameters in the experiment tracker so we can reproduce this?”
Example Sentences
- “We ran 100 epochs before observing signs of overfitting, at which point we applied early stopping.”
- “The inference latency at p99 is 45 ms, which is within our SLA for real-time recommendations.”
- “After the ablation study, we confirmed that pre-training on domain-specific data accounts for the majority of the performance gain.”
- “Gradient descent with a warm-up schedule stabilised training and prevented the loss from diverging early on.”
- “The model card documents known limitations, including degraded performance on low-resource languages.”
Practice Exercise
Write two sentences describing a recent (real or imagined) experiment. Include at least one training term and one inference term. Focus on being precise rather than impressive — clarity is the goal in technical communication.