Why this matters
Managed ML services let you train, deploy, and monitor models without building all the infrastructure yourself. As a Machine Learning Engineer, you will often need to: ship a model behind an API in days (not weeks), run scheduled batch predictions at scale, standardize experiments and model versions across teams, and meet security and cost constraints. Knowing the basics across major clouds helps you choose the right tool quickly and avoid costly rework.
- Real tasks you will face: pick a hosting option for a new model, configure autoscaling for unpredictable traffic, set up a training job that plugs into cloud storage, register models for approval, and enable monitoring to catch data drift.
Concept explained simply
Managed ML services are cloud platforms that package common MLOps needs—data access, training, model registry, deployment, and monitoring—so you can focus on the model and product, not servers.
Mental model
Think of managed ML like a set of LEGO blocks:
- Data blocks: storage, feature store
- Build blocks: notebooks, training jobs, AutoML
- Assembly line: pipelines, schedules
- Showroom: model registry
- Delivery: batch jobs and real-time endpoints
- Quality control: monitoring, alerts
Vendor names map (quick reference)
- AWS: SageMaker (Studio, Training, Processing, Feature Store, Pipelines, Model Registry, Endpoints, Model Monitor)
- GCP: Vertex AI (Workbench, Training, Pipelines, Feature Store, Model Registry, Endpoints, Model Monitoring, Batch Predictions, AutoML)
- Azure: Azure Machine Learning (Compute, Notebooks, Pipelines, Feature Store, Model Registry, Online/Batch Endpoints, Data/Model Monitoring, Automated ML)
Core building blocks you should recognize
- Storage and data access: Read training and inference data from object storage (e.g., buckets, blob storage). Control with IAM/roles.
- Training jobs: Containerized runs with specified compute (CPU/GPU), input data paths, hyperparameters, and output artifacts.
- AutoML: Service that searches models/architectures/hyperparameters for you—good baselines or when speed matters.
- Notebooks/IDE: Hosted environments with preinstalled libraries; good for exploration and quick POCs.
- Pipelines/Orchestration: Define steps (ingest → train → evaluate → register → deploy) with reproducibility and scheduling.
- Model registry: Central catalog of models with versions, lineage, and approval status.
- Deployment: Real-time endpoints (low latency APIs) vs. batch jobs (large offline scoring). Choose by latency and volume.
- Monitoring: Track performance, data drift, and service health; set alerts to catch issues early.
- Security & governance: Roles, network boundaries (VPC), encryption, audit logs.
- Cost model: Pay for compute, storage, and network. Idle endpoints and oversized instances are common cost leaks.
Worked examples
Example 1: Real-time vs batch
Use case: Recommend top 5 products on a product page.
- Requirements: latency < 150 ms, traffic spikes during sales.
- Choice: Real-time endpoint with autoscaling. Batch is too slow for per-request personalization.
- Bonus: Warm min instances to absorb spikes, set max to cap spend.
Example 2: Simple training job
- Put training data in cloud object storage (e.g., gs://, s3://, or Azure Blob).
- Choose compute (e.g., 1 GPU if deep learning, CPU for tree models).
- Specify container and entry point (train.py); pass hyperparameters.
- Artifacts (model.pkl) saved to output path; register in Model Registry.
Example 3: AutoML baseline
Goal: Fast baseline on a tabular churn dataset.
- Upload CSV, select target, enable class weighting, limit training time (e.g., 30–60 minutes).
- Review leaderboard; export best model; deploy to a low-cost endpoint or run daily batch scoring.
Example 4: Choosing a feature store
Team shares customer features across projects.
- Use Feature Store to define feature views, backfill historical values, and serve online features for low-latency inference.
- Benefit: Consistency between training and serving, less feature duplication.
Hands-on exercises
These mirror the exercises below (with solutions in the collapsible sections of each exercise on this page).
Exercise 1 (Design): Map a use case to managed components
- [ ] Pick a cloud (AWS, GCP, or Azure)
- [ ] Select components for data, training, registry, and deployment
- [ ] Write a one-paragraph plan and list assumptions
Exercise 2 (Decision): Deployment and cost fit
- [ ] Given traffic and latency, choose real-time vs. batch
- [ ] Propose instance size and autoscaling bounds
- [ ] Note two monitoring metrics and a rollback trigger
Common mistakes and how to self-check
- Mistake: Defaulting to real-time endpoints for everything. Self-check: Do users need sub-second responses? If not, batch may be cheaper and simpler.
- Mistake: Oversized instances. Self-check: Run a load test and check CPU/GPU utilization; target 50–70% under typical load.
- Mistake: Skipping model registry. Self-check: Can you name the exact model version in prod with metadata? If not, adopt a registry.
- Mistake: No monitoring for data drift. Self-check: Do you track input feature distributions over time? Add alerts for drift thresholds.
- Mistake: Hard-coding storage paths and creds. Self-check: Use environment variables, roles/IAM, and parameterize data locations.
Who this is for
- Machine Learning Engineers starting with cloud ML platforms
- Data Scientists moving from notebooks to deployed services
- MLOps practitioners aligning teams around common tooling
Prerequisites
- Basic Python and ML familiarity (training, validation, metrics)
- Comfort with containers or at least understanding of entry points and dependencies
- Intro knowledge of cloud concepts: storage buckets, IAM/roles, regions
Learning path
- Understand managed ML building blocks (this page)
- Deploy a simple model to a real-time or batch endpoint
- Add model registry and promotion workflow (staging → prod)
- Automate training/inference with a pipeline and schedule
- Enable monitoring and set rollback criteria
Practical projects
- Real-time sentiment API: Deploy a small text classifier with autoscaling and latency SLO.
- Daily batch churn scoring: Schedule inference over a data warehouse export, store predictions in a table, and email summary metrics.
- Registry + canary rollout: Register a new model version, route 10% of traffic, monitor metrics, then promote or rollback.
Next steps
- Pick one cloud vendor and build a minimal end-to-end path: training → registry → deploy → monitor.
- Add cost alarms and a weekly report of utilization and throughput.
- Create a team playbook documenting which services to use for common scenarios.
Mini challenge
Design a one-page deployment decision tree for your team: given latency, throughput, update frequency, and data sensitivity, recommend batch vs. real-time, instance types, and required monitoring checks. Keep it vendor-agnostic.
Quick Test
Take the quick test to check your understanding. Everyone can take it for free; if you log in, your progress and score are saved.