From Data to Deployment: The ML Development Lifecycle (and How to Put Models in Production on AWS) | AIF-C01

If you can explain the ML lifecycle and map each stage to the right AWS service—especially deployment options like managed endpoints vs self-hosted APIs—you’ll score easy points on AIF-C01 and make better real-world architecture choices.

Why the ML lifecycle matters (for AIF-C01 and for real projects)

Ever notice how ML questions on certification exams feel less like “pick the right algorithm” and more like “what would you actually do at work”? That is on purpose. On AIF-C01, you are expected to describe the ML development lifecycle, meaning the repeatable end-to-end loop from problem framing to deployment to monitoring.

Why should you care beyond the exam? Because the teams that struggle with ML usually do not struggle with “training a model.” They struggle with everything around it: messy data, unclear success metrics, brittle deployments, and models that quietly get worse after launch.

The lifecycle is your mental map. If you can say, “We are in the data prep stage, so we need X,” or “We are in deployment, so we need Y,” you stop guessing and start designing.

For the test, this shows up as “connect the stage to the right AWS service or capability.” For real projects, it shows up as fewer late-night rollbacks because the model endpoint ran out of memory.

Also, this is not a random trivia topic. It is explicitly called out in the exam objectives as a task you should be able to do.

The ML development lifecycle in plain language (the “end-to-end story”)

If ML feels mysterious, think of it like opening a small restaurant. You do not start by buying ovens. You start by deciding what you are serving and how you will know customers are happy.

The ML development lifecycle starts the same way: define the problem. What is the decision the model will help make, and what does “good” look like? For example, “reduce fraud chargebacks by 10% without blocking good customers” is better than “detect fraud.”

Next comes data collection and preparation. This is where you gather the inputs (transactions, user behavior, images, text), clean them up, and shape them into something a model can learn from. In practice, this is often the longest phase because real data is messy in surprisingly creative ways.

Then you train a model, which is basically “let the model practice.” After training, you evaluate it using holdout data and the right metrics (accuracy, precision/recall, RMSE, latency, cost). This is where you decide if it is ready, or if you need different features, more data, or a different approach.

After that comes deployment: putting the model somewhere it can be used, like behind an API or in a batch job. Then you monitor in production, because the world changes, data drifts, and yesterday’s great model can become today’s liability.

The key detail: this is a loop, not a straight line. Every production issue you see feeds the next round of data, training, and improvement.

What you need to know (AIF-C01 checklist + AWS service mapping)

A sneaky thing about AIF-C01 is that you can score well without being a deep ML mathematician. The exam is more about fluency: can you describe the stages, the trade-offs, and the AWS “usual suspects” that fit each stage?

Here is a practical checklist you can keep in your head while studying:

Problem framing and metrics

• Define the business goal and the failure modes (false positives, false negatives).
• Pick metrics that match reality (fraud, churn, forecasting, recommendations).

Data ingest, storage, and prep

• Land raw data in Amazon S3.
• Use AWS Glue for cataloging and ETL when you need structured transforms.
• Use AWS Glue DataBrew when you want a visual, low-code way to clean and profile data.
• Use Amazon Redshift when analytics warehousing is part of the workflow.

Build and train

• Use Amazon SageMaker Studio or notebooks when you want a managed ML workbench.
• Use managed training jobs when you want scalable compute without hand-building servers.

Evaluate and iterate

• Track experiments (datasets, parameters, results) so you can reproduce “the good run.”
• Decide if you need a different threshold, different features, or more data.

Deploy and serve predictions

• Know the big production patterns: real-time API, asynchronous requests, and batch scoring.

Monitor and improve

• Watch operational metrics (latency, errors, saturation) and model quality signals.
• Expect retraining, because production data rarely stays still.

If you can talk through this checklist and name reasonable AWS services at each step, you are studying the right thing.

How to use a model in production: managed API services vs self-hosted APIs (and when to choose each)

Deployment is the moment ML stops being a science project and starts being a product. And almost every production choice boils down to one question: do you want AWS to run the “model server” for you, or do you want to run it yourself?

Option 1: Managed API service (fastest path) Think of this like ordering catering instead of cooking for 200 people. With Amazon SageMaker managed hosting endpoints, you deploy a model and invoke it through API calls, without managing the underlying fleet day-to-day. This is the cleanest answer when the exam says “managed endpoint” or “managed API.”

When managed endpoints shine:

• You want a straightforward real-time inference API for an app.
• You want built-in scaling and a standard operational model.
• You want your ML team to focus on models, not Kubernetes upgrades.

Where you still have to think:

• Instance type choices (CPU vs GPU), cost controls, rollout strategy, and security.
• Latency targets and traffic patterns, which influence whether you use always-on compute or something more elastic.

Option 2: Self-hosted API (more control, more responsibility) This is like running your own kitchen. You can serve the same “dish” (predictions), but you control the menu, the appliances, and the staffing.

Self-hosting usually means containerizing your model server and running it on Amazon ECS, Amazon EKS, or plain EC2. You do this when you need tighter control over the runtime, custom networking, specialized GPU setups, or you already have a strong container platform in place.

When self-hosting is a great idea:

• You have a custom inference stack (specific libraries, custom routing, specialized hardware needs).
• You need consistent behavior across clouds or on-prem environments.
• You want to integrate deeply with existing microservices and deployment pipelines.

A quick way to choose If your main risk is “we need this in production next month,” pick managed endpoints. If your main risk is “we must control every knob for compliance, portability, or specialized performance,” self-host.

In both cases, the model is not “in production” until your application can call it reliably, your team can update it safely, and you can see when it starts misbehaving.

Practical scenarios you can visualize (real-time, batch, async, serverless, edge)

It helps to picture inference (getting predictions) like ordering food, because the “how” depends on when you need it.

Real-time: You tap “Order,” you want the answer now. This fits interactive apps like fraud checks at checkout or a chatbot response.

Batch: You place a big catering order for tomorrow. This fits overnight scoring, like predicting churn risk for every customer once a day.

Async: You order something that takes a while, and you come back when it is ready. This fits long-running requests like video analysis or large document processing.

Serverless and spiky traffic: Some days you get 10 requests, then suddenly 10,000. In those cases, a serverless style can be attractive because you are not paying for idle time.

Edge: Sometimes you want the prediction close to the user, either for latency or disconnected environments, like smart retail kiosks or IoT scenarios.

If you can match the workload pattern to the serving pattern, you will avoid most beginner architecture mistakes.

Monitoring + exam tips: what people miss (and what to do next)

The most common beginner mistake is thinking deployment is the finish line. In ML, deployment is the start of the part that can hurt you.

In production, monitor two categories of signals:

Operational health

• Latency, error rates, timeouts, CPU and memory saturation.
• Cost and scaling behavior, so you do not “accidentally” build a very expensive API.

Model health

• Input drift (the data the model sees changes over time).
• Output drift (predictions shift in weird ways).
• Quality drift (accuracy or business KPIs degrade).

Here are exam-style gotchas people miss:

• “Serverless” does not mean “no trade-offs.” It often means different limits, different scaling behavior, and cold-start considerations.
• Managed hosting reduces ops work, but you still own the responsibility for data quality, evaluation, and safe rollouts.
• Self-hosting gives flexibility, but it also gives you more ways to break things (and more things to patch).

Quick recap The lifecycle is: define, prepare data, train, evaluate, deploy, monitor, repeat. For production, be ready to describe two paths: managed endpoints for speed and simplicity, and self-hosted APIs for maximum control.

Next step: take one sample use case you know (fraud, recommendations, forecasting) and practice explaining the lifecycle out loud. If you can explain it clearly, you can usually answer the exam question.