Cloud & Data Engineering
Cloud and data work around ML workflows, data preparation, AWS services, feature pipelines, storage, and scalable processing.
AWS Certified Machine Learning Engineer - Associate.
Worked with AWS SageMaker workflows, Bedrock, Kendra, S3, Kinesis, Glue, Lambda, and Data Wrangler concepts.
Built data preparation, validation, augmentation, and evaluation workflows for ML projects.
Cloud as the ML Operating Layer
Cloud engineering for ML is about connecting data, compute, model artifacts, security, and observability. The model is only useful when the surrounding workflow can reproduce, deploy, monitor, and explain it.
My AWS work is strongest around ML lifecycle thinking: SageMaker-style training and deployment decisions, storage and data movement, serverless orchestration, monitoring, and choosing the right inference pattern for the workload.
Data Quality Before Model Quality
Most model issues start in the data. I pay attention to dataset construction, validation, class imbalance, leakage, missing values, label quality, and the difference between offline evaluation data and real operational data.
For computer vision and signature-verification work, this becomes especially important. Augmentation can help, but it should not hide dataset bias or create unrealistic examples that inflate validation metrics.
- Data preparation and validation
- Quality analysis before training
- Version-aware datasets and evaluation splits
- Synthetic data used carefully for robustness, not metric theater
Batch, Streaming, and Serverless Patterns
Not every ML workload should be an endpoint. Batch scoring, event-driven processing, streaming ingestion, and scheduled evaluation jobs often fit better.
The engineering judgment is in matching the data movement pattern to the model. A model that scores a daily portfolio should not necessarily live behind a low-latency API. A model serving real-time fraud checks probably should.
Practical Cloud Decisions
I try to choose the smallest managed service that satisfies the requirement. SageMaker endpoints, Lambda, container services, batch jobs, and vector databases each solve a different operational problem.
The mistake is using a platform because it is powerful. The better choice is a platform whose failure modes the team can understand and operate.