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Cloud Data & AI
Data Engineering & AI/ML Services
Data engineering and AI/ML services including data pipelines, warehouses, lakehouse architectures, and machine learning platform setup on cloud providers.
Get Started75+
Data Pipelines Built
45%
Cost Savings Avg
10PB+
Data Processed
99.5%
Model Accuracy
Service Category
Data Engineering & AI
Ideal For
Companies building data pipelines, warehouses, ML platforms, or needing to modernize data infrastructure for analytics and AI.
Timeline
4 – 12 weeks
Why Choose MicrocosmWorks for Data Engineering & AI/ML?
Data is only valuable when it flows reliably, is properly transformed, and reaches the right systems at the right time. Our data engineering team builds the foundational infrastructure — pipelines, warehouses, lakehouses, and ML platforms — that enables your organization to make data-driven decisions and deploy AI models at scale on AWS, GCP, or Azure.
Our Data Engineering & AI/ML Capabilities
- Data Pipeline Development — Build reliable ETL/ELT pipelines using Airflow, dbt, Spark, or cloud-native services that process data at any scale.
- Data Warehouse & Lakehouse — Architect modern data platforms on Snowflake, BigQuery, Redshift, or Databricks with proper modeling and governance.
- Real-Time Streaming — Implement event-driven architectures using Kafka, Kinesis, or Pub/Sub for real-time analytics and ML feature serving.
- ML Platform Setup — Build MLOps platforms with experiment tracking, model registries, feature stores, and automated training pipelines.
- Data Quality & Governance — Implement data quality checks, lineage tracking, cataloging, and access controls for trusted, compliant data.
- AI Model Deployment — Deploy ML models to production with serving infrastructure, A/B testing, monitoring, and automated retraining pipelines.
- Analytics Infrastructure — Set up BI tools, dashboards, and self-service analytics for business teams with proper semantic layers.
Data & AI Technology Stack
We build data platforms using Apache Spark, Airflow, dbt, Kafka, and Flink for processing and orchestration. For storage, we work with Snowflake, BigQuery, Redshift, Delta Lake, and Iceberg. Our ML stack includes MLflow, Kubeflow, SageMaker, Vertex AI, and custom platforms built on Kubernetes with GPU support for training and inference.
Who This Is For
This service is for companies that need to build or modernize their data infrastructure — from startups setting up their first analytics pipeline to enterprises building ML platforms. If your team struggles with data silos, unreliable pipelines, or difficulty deploying ML models, we provide the engineering expertise to solve these challenges.
Our Process
1
Discovery
Assess your data sources, current infrastructure, analytics needs, and ML/AI objectives.
2
Architecture
Design the data platform architecture with pipeline topology, storage layers, and ML infrastructure.
3
Implementation
Build data pipelines, deploy warehouses, configure ML platforms, and set up monitoring.
4
Optimization
Tune query performance, optimize pipeline costs, implement data quality checks, and validate ML models.
5
Operations
Hand off with documentation, train data teams, and provide ongoing support for pipeline reliability.
Technology Stack
Data Processing
Apache SparkAirflowdbtFlink
Storage
SnowflakeBigQueryRedshiftDelta Lake
ML Platforms
MLflowSageMakerVertex AIKubeflow
Streaming
KafkaKinesisPub/SubSpark Streaming
Industries We Serve
E-CommerceFinancial ServicesHealthcareMediaRetailLogisticsTelecomManufacturing
Ready to Build Your Data & AI Platform?
Let our data engineers build reliable pipelines and ML infrastructure that turn your data into a competitive advantage.
Frequently Asked Questions
We build end-to-end data pipelines for ML workflows including feature engineering, data labeling pipelines, training data management, feature stores, and automated data quality validation to ensure your models are fed clean, reliable data.
Our data engineering and AI/ML pipeline development services are available at $30-$50/hour, with rates varying based on the complexity of your data infrastructure and ML workflow requirements.
Yes, we implement feature stores using tools like Feast, Tecton, or custom solutions on top of Redis and BigQuery, enabling your ML team to share, discover, and serve features consistently across training and inference.
We implement automated data validation using Great Expectations or Deequ, schema enforcement, drift detection, and statistical profiling at every stage of the pipeline to catch data quality issues before they degrade model performance.
Yes, we build complete MLOps pipelines including model versioning with MLflow, automated retraining triggers, A/B testing infrastructure, and model serving on Kubernetes with autoscaling based on inference load.