Everything in Cloud Platforms, page 2
Design petabyte-scale distributed training
Object storage with columnar formats, distributed preprocessing, a data-parallel framework with efficient sharded loading, and managed orchestration.
Design auto drift detection and retraining
Capture inputs and predictions, compute data and concept drift metrics on a schedule, alert on threshold breach, and trigger a retraining and redeploy pipeline.
How would you speed up slow single-GPU training?
Vertical scaling to bigger or multi-GPU instances, then data-parallel or model-parallel distributed training across nodes.
How would you build CI/CD for an ML model?
Data and model versioning, automated training plus evaluation gates, model registry, deployment with monitoring and retraining triggers.
Inference performance bottlenecks on Lambda
Cold starts loading the model, memory and CPU limits, no GPU, and package size dominate; mitigate with provisioned concurrency, loading the model once outside the handler, smaller models, and right-sized…
Deploying a real-time inference endpoint
Package the model artifact and inference code in a container, choose instance type and autoscaling, configure the endpoint with health checks, and plan safe rollout like canary plus monitoring.
Feeding large object-store data into training
Stream data instead of copying it all to disk, use streaming/pipe modes, shard and prefetch in parallel, and pack many small images into larger files.
Pre-built AI service vs custom model
Choose a managed service for speed, no ML expertise, and common tasks; build custom for domain-specific needs, control, or cost at scale.
Schema evolution without rewriting history
Use a table format with metadata-level evolution, add a new column rather than mutating the old, and reconcile types at read time; avoid rewriting petabytes.
The small files problem in data lakes
Too many tiny files inflate metadata and per-file overhead, slowing queries; caused by streaming micro-batches and over-partitioning; fix with compaction and table formats like Iceberg, Delta, or Hudi.
Idempotency in data ingestion pipelines
Idempotency means re-running a step yields the same result with no duplicates; it matters because retries and at-least-once delivery are inevitable; achieve it with deduplication keys or upserts.
Handling late data in streaming windows
Use event-time windows with watermarks to bound lateness, allow a grace period before finalizing, and route data later than that to a side output.
Diagnosing and fixing data skew in Spark
This is data skew, caused by uneven key distribution concentrating rows on few partitions; mitigate with salting, broadcast joins, repartitioning, or adaptive execution.
Partitioning order events in a data lake
Partition by the columns queries filter on, typically date hierarchy and category, balancing granularity to avoid too many tiny files.
CSV vs JSON vs Parquet for analytics
CSV and JSON are row-based, human-readable, and bulky; columnar Parquet/ORC compress well and read only needed columns; choose columnar for analytics.
ETL versus ELT in cloud data platforms
ETL transforms before loading into the target; ELT loads raw first then transforms in the warehouse, leveraging cheap storage and elastic compute.
Data lake versus data warehouse
Lakes store raw, schema-on-read data of any type cheaply; warehouses store curated, schema-on-write structured data for fast SQL; choose a lake for varied raw data and ML.
Resilient stateful batch on Spot Instances
Externalize state and checkpoint to durable storage, react to interruption and rebalance notices to drain gracefully, diversify instance pools.
Designing a multi-account cloud chargeback model
Account-per-team or mandatory cost-allocation tags enforced by SCPs and tag policies, plus a pipeline over the cost and usage report grouped by tag/account.
Savings Plans vs Reserved Instances for mixed compute
Compute Savings Plans cover EC2, Fargate, and Lambda flexibly; EC2 Instance Plans and RIs trade flexibility for slightly deeper discounts.
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