A fast pass over the ML fundamentals and SageMaker services this track assumes as background and the AIP-C01 track above it never re-teaches.
Fundamentals at a glance
| Thing | What it is | Reach for it when |
|---|---|---|
| Regression | Labelled data, continuous target | Predicting a number: price, demand, a delivery time |
| Classification | Labelled data, categorical target | Predicting a label: fraud or not, which tier |
| Clustering | Unlabelled data, grouping | Finding structure with no target to predict |
| Semi-supervised learning | A small labelled set plus a large unlabelled one | Labels are expensive and scarce, but raw data is plentiful |
| Self-supervised learning | Labels derived from the data itself | Foundation-model pre-training; no human labelling at all |
| Reinforcement learning | A reward signal from an environment | An agent learns by acting and being scored, not from fixed examples |
| RLHF | Human preference rankings train a reward model | Aligning a model’s responses to what people actually prefer |
| Transfer learning | A pre-trained model adapted with a small labelled set | You want a head start rather than training from nothing |
| Overfitting | Train loss falls, validation loss rises | Too much capacity or too few examples for the model to generalise |
| Underfitting | Both losses stay high | Too little capacity, too few epochs, or too low a learning rate |
| Data drift | The input distribution shifts over time | Same relationship between input and target, different inputs arriving |
| Concept drift | The input-to-target relationship changes over time | The same inputs now deserve a different answer |
| Training-serving skew | A pipeline mismatch between training and serving | Degradation shows up immediately, not gradually like the other two |
The SageMaker suite at a glance
| Service | Job | Reach for it when |
|---|---|---|
| Ground Truth | Labels training data | You need a labelled dataset built before training starts |
| Augmented AI (A2I) | Routes production predictions to a human reviewer | A live prediction needs a person to check it before it acts |
| Clarify | Bias metrics before and after training, plus SHAP explainability | You need to measure bias in data or a model, or attribute a prediction to its features |
| Model Monitor | Watches a live endpoint for drift | You need continuous production monitoring, not a one-off check |
| Model Cards | Human-written documentation: intended use, limits, eval results | You need to record and communicate what a model is for |
| Model Registry | A version catalogue with approval status | You need to gate what gets promoted into a CI/CD pipeline |
| Feature Store | One ingestion, two reads | You need the same features served at millisecond latency and pulled for training history |
| Pipelines | ML CI/CD orchestration | You need to automate the steps from data to deployed model |
| JumpStart | A model hub you deploy onto your own endpoints and instances | You want a pre-built model but need instance choice or your own VPC |
| Canvas | No-code ML | A business user needs to build a model without writing code |
| Data Wrangler | Visual data preparation | You need to clean and transform data without a notebook |
| Trainium | Purpose-built training silicon | You are training a large model and want the cost-efficient chip for it |
| Inferentia | Purpose-built inference silicon | You are serving a large model and want the cost-efficient chip for it |
| Real-time inference | An always-on endpoint | Traffic is steady and needs low, consistent latency |
| Serverless inference | Scales to zero between requests | Traffic is intermittent and cold starts are acceptable |
| Asynchronous inference | A queue in front of the endpoint | Payloads are large or processing is slow, and nobody is waiting live |
| Batch transform | Scores a whole dataset offline, no endpoint | You are scoring data once with no live traffic at all |
Deployment strategies at a glance
| Strategy | Shape | Reach for it when |
|---|---|---|
| Shadow | A copy of live traffic runs the new version with zero user exposure | You want a pure comparison before anyone is at risk |
| Canary | A small percentage of real users hit the new version | You want to expand gradually once the small slice looks healthy |
| Blue/green | Two full parallel environments, one cutover | You want an instant, whole-scale switch and an instant rollback |
| A/B | Traffic is deliberately split to compare outcomes | You are running an experiment, not a rollout |
Decision rules
- If you have hyperparameters (epochs, learning rate, batch size), those are set before training; if you have inference parameters (temperature, top-p, top-k), those are set per request. Confusing the two is a classic distractor.
- Work the ML lifecycle as a loop, not a line: business goal, collect, prepare and explore, engineer features, train, tune, evaluate, deploy, monitor, and back to collect when monitoring finds a gap.
- If you want zero user exposure while comparing a new model, use shadow; if you want to expand gradually into real traffic, use canary; if you want an instant whole-scale switch with an instant rollback, use blue/green; if you are deliberately comparing outcomes rather than rolling something out, that is an A/B test.
- If the target is a number, that is regression; if it is a category, that is classification; if there is no target at all, that is clustering.
- If labels are scarce but raw data is not, use semi-supervised learning rather than waiting for a fully labelled set.
- If you are pre-training a foundation model, the labels come from the data itself; that is self-supervised learning, and nobody hand-labels anything.
- If you want a model to prefer certain responses by human judgement, that is RLHF, and it is a training technique, not an evaluation method.
- If validation loss rises while training loss keeps falling, you are overfitting; cut epochs, add data, or regularise.
- If both losses stay stubbornly high, you are underfitting; add capacity, train longer, or raise the learning rate.
- If a model that was fine yesterday is wrong more often today and the inputs still look the same shape, suspect concept drift before blaming the pipeline.
- If a model is wrong from the day it deployed, suspect training-serving skew, not drift; drift takes time to appear, skew does not.
- If you need training data labelled, use Ground Truth; if you need a live prediction checked by a person before it acts, use A2I. They sit on opposite sides of deployment.
- If you need to measure bias or explain a prediction, use Clarify; if you need to watch a live endpoint keep behaving the same way, use Model Monitor.
- If you need to document what a model is for, write a Model Card; if you need to gate what gets promoted to production, use the Model Registry. Documentation is not a deployment gate.
- If a model needs to serve at millisecond latency and also be pulled for training history, use Feature Store’s online and offline stores from one ingestion.
- If you want a pre-built model but need to pick the instance type or keep it inside your own VPC, use JumpStart; a fully managed API surface gives you neither.
- If a business user needs a model with no code, point them at Canvas, not a notebook.
- If traffic is steady, use a real-time endpoint; if it is intermittent, use serverless and accept the cold starts; if payloads are large or slow, use asynchronous inference; if there is no live traffic at all, use batch transform.
Traps
- Confusing self-supervised learning with unsupervised learning. Self-supervised still trains against a target; the target is just derived automatically from the data, not hand-labelled.
- Treating RLHF as an evaluation technique. It trains a reward model from preference data; it does not measure anything after the fact.
- Assuming drift is always gradual. Training-serving skew is a pipeline bug, not a drift phenomenon, and it shows up on day one.
- Mixing up which drift is which: data drift is the inputs changing shape; concept drift is the correct answer for the same inputs changing. Same symptom, different cause.
- Picking Ground Truth when the question is about a live prediction, or A2I when the question is about building a training set. The clue is always which side of deployment the data sits on.
- Treating a Model Card as something that blocks a release. It documents; the Model Registry’s approval status is what actually gates a pipeline.
- Assuming Clarify and Model Monitor do the same job because both mention bias. Clarify measures it in the data before training and in the model after; Model Monitor watches for it continuously once the model is live.
- Reaching for a real-time endpoint out of habit when traffic is genuinely bursty and idle time is long; that is what serverless is for, cold starts and all.
- Forgetting that batch transform has no persistent endpoint at all; it is not a cheaper real-time option, it is a different shape of job.
- Treating an A/B test as a rollout mechanism. It is an experiment to compare outcomes; shadow, canary, and blue/green are the ways you actually roll something out.
- Reaching for blue/green when the real ask is gradual exposure, or canary when the real ask is an instant whole-scale cutover; the shape of the risk decides which one fits.
Say it in one line
- Labelled and continuous means regression, labelled and categorical means classification, unlabelled means clustering.
- Self-supervised learning derives its own labels from the data; that is how foundation models pre-train.
- RLHF trains a reward model from human preference rankings; it is a training step, not a measurement.
- Overfitting shows as rising validation loss against falling training loss; underfitting shows as both staying high.
- Data drift is the inputs changing, concept drift is the correct answer changing, and training-serving skew is a pipeline bug that bites from day one.
- Ground Truth labels training data; A2I reviews production predictions. Different side of deployment, different job.
- Clarify measures bias and explains predictions; Model Monitor watches a live endpoint keep behaving the same way over time.
- Model Cards document; the Model Registry’s approval status gates what actually ships.
- Feature Store serves the same features at millisecond latency online and as training history offline, from one ingestion.
- Pipelines orchestrate ML CI/CD; JumpStart deploys a pre-built model onto your own instances; Canvas is no-code; Data Wrangler is visual prep.
- Trainium trains, Inferentia infers.
- Real-time is always-on, serverless scales to zero with cold starts, asynchronous queues large payloads, and batch transform scores offline with no endpoint at all.
- Hyperparameters are set before training; inference parameters are set per request.
- The ML lifecycle is a loop: goal, collect, prepare, engineer, train, tune, evaluate, deploy, monitor, and back to collect.
- Shadow exposes nobody, canary exposes a slice, blue/green cuts over wholesale, and A/B is an experiment rather than a rollout.