Cloud Skills for ML Engineers

Start with cloud concepts that transfer across providers: regions and availability zones, virtual machines or managed compute, object storage, identity and access management, networks, logs, and billing controls. Learn how permissions are granted to users and services, and prefer least privilege over broad administrator credentials. Understand where datasets and model artifacts live, how they are versioned, and which services can access them. Containers package an application with dependencies and make environments more repeatable. Learn how to build an image, configure environment variables, expose a service, and keep secrets outside the image. For ML, distinguish training jobs from online inference: training may need accelerators and large storage, while prediction services often need low latency, scaling, and health checks. Batch inference can have a different cost and reliability profile. A small cloud project should move through data ingestion, training, evaluation, artifact storage, and a simple deployment. Automate repeatable steps and record the code, data version, configuration, and model artifact. Add tests before deployment and monitor both service behavior and model inputs. A successful offline metric does not establish that the deployed pipeline is correct or that serving data matches training data. Cost control belongs in the technical workflow. Set budgets or alerts where supported, use temporary resources, remove unused storage, and verify the billing impact before running large jobs. Free tiers and prices vary by provider, region, account, and date; check current official pricing instead of trusting an old tutorial. Do not try to learn every cloud service at once. Select the provider used by a target team or project, then build transferable foundations. For some roles, Kubernetes or distributed training matters; for others, a managed batch job, container, and clear access policy are enough. Demonstrate the decision-making and operational checks behind the deployment.