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Cloud Data & AI
AWS Data Engineering & AI/ML (SageMaker)
AWS data engineering and AI/ML services with SageMaker. Build data pipelines, train models, and deploy ML at scale with AWS-native data and AI services.
Get Started75+
Data Pipelines Built
45%
Cost Savings Avg
10PB+
Data Processed
99.5%
Model Accuracy
Service Category
AWS Data & AI Engineering
Ideal For
Data-driven companies building analytics platforms, ML pipelines, or GenAI features on AWS.
Timeline
4 – 10 weeks
Why Choose MicrocosmWorks for AWS Data & AI?
AWS offers the broadest set of data and ML services, but choosing the right ones and connecting them effectively requires deep expertise. We design end-to-end data platforms on AWS — from ingestion pipelines and data lakes to model training with SageMaker and real-time inference endpoints — all with proper governance and cost controls.
Our AWS Data & AI Capabilities
- Data Lake Architecture — Design S3-based data lakes with Lake Formation governance, Glue catalogs, and Athena for serverless analytics.
- ETL Pipeline Development — Build scalable data pipelines using Glue, Step Functions, and Kinesis for batch and real-time data processing.
- SageMaker ML Platform — Set up end-to-end ML workflows: data labeling, model training, hyperparameter tuning, and model deployment with SageMaker.
- Real-Time ML Inference — Deploy models as real-time endpoints, batch transform jobs, or serverless inference with auto-scaling and A/B testing.
- Data Governance — Implement data quality checks, lineage tracking, access controls, and compliance tagging across the data platform.
- GenAI Integration — Integrate Bedrock foundation models and custom fine-tuned models into production applications with RAG patterns.
AWS-Specific Technology Stack
We build on AWS's data ecosystem: S3 and Lake Formation for storage, Glue and Kinesis for processing, Redshift and Athena for analytics, SageMaker for ML, and Bedrock for generative AI — all orchestrated with Step Functions and monitored with CloudWatch and SageMaker Model Monitor.
Who This Is For
Data-driven companies looking to build analytics platforms, ML pipelines, or GenAI features on AWS. Whether you're starting your data journey or scaling an existing ML operation, we bring the architecture expertise to maximize ROI from your data investments.
Our Process
1
Data Assessment
Inventory data sources, assess quality, define analytics requirements, and identify ML opportunities.
2
Platform Architecture
Design data lake architecture, pipeline topology, ML workflow, and governance framework.
3
Pipeline Implementation
Build ingestion pipelines, transformation jobs, data quality checks, and catalog management.
4
ML Development
Train models, optimize hyperparameters, deploy inference endpoints, and implement monitoring.
5
Production Operations
Establish MLOps practices, data pipeline monitoring, model retraining triggers, and cost governance.
Technology Stack
Data & Storage
S3Lake FormationRedshiftAthenaGlue
ML & AI
SageMakerBedrockComprehendRekognition
Streaming & ETL
KinesisStep FunctionsGlue ETLEventBridge
Governance
Lake FormationCloudWatchDataBrewData Quality
Industries We Serve
FinTechHealthcareRetailAd TechLogisticsManufacturing
Ready to Build on AWS Data & AI?
Let's architect your data platform and ML pipeline on AWS — from raw data to production models.
Frequently Asked Questions
MicrocosmWorks specializes in SageMaker for model training and deployment, Glue and EMR for ETL, Redshift and Athena for analytics, Kinesis for streaming, and Step Functions for ML pipeline orchestration across the full data engineering lifecycle.
AWS SageMaker and data engineering consulting is available at $30-$50/hour, covering model training pipeline setup, endpoint deployment, feature stores, and integration with your existing data infrastructure.
Yes, we build production ML pipelines using SageMaker Pipelines with automated data preprocessing, distributed training, hyperparameter tuning, model evaluation, model registry, and A/B testing deployment with real-time and batch inference endpoints.
Absolutely. MicrocosmWorks designs S3-based data lakes with Glue crawlers, ETL jobs, and Data Catalog, implements Lake Formation for governance, and builds feature engineering pipelines that feed directly into SageMaker training jobs.
Yes, we deploy custom and open-source LLMs on SageMaker using Deep Learning Containers, configure inference endpoints with model parallelism for large models, and integrate with AWS Bedrock for hybrid architectures combining proprietary and foundation models.