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DeepSea Technologies Tech Stack
AI platform for vessel performance monitoring and route optimization
DeepSea builds machine-learning models to optimize ship operations—engine performance, routing, fuel consumption—using real-time sensor data. The tech stack is heavy on data infrastructure (Databricks, Delta Lake, Spark, MLflow) with Python-first ML development, suggesting a mature data-science operation. Active migration to AWS EKS and work on streaming architecture signal infrastructure modernization to handle real-time ship telemetry at scale, while pain points around data quality and sensor anomalies indicate the core challenge: noisy maritime sensor inputs feeding prediction models.
Tech Stack 38 technologies
Core StackDatabricks Delta Lake Apache Spark MLflow AWS Python Pandas PySpark NumPy React Redux Bootstrap Webpack Node.js Rust C++ GraphQL Parquet JSON Azure SQL SciPy React Redux Redux Toolkit Git Gulp npm SASS C REST+5 more
AdoptingAWS EKS
What DeepSea Technologies Is Building
◆Challenges
- Migrating legacy systems
- Reducing resource scaling costs
- Scalable backend infrastructure
- Data quality issues in ship sensor data
- Model performance impacted by data anomalies
- Migrating components between stacks
- Transitioning infrastructure to container orchestration
- Expanding automation capabilities
▲Active Projects
- Ai model training data infrastructure
- Real-time streaming architecture
- Data lifecycle architecture
- Intuitive products and internal tools
- Frontend integration with backend ai algorithms
- Route&speed optimization solutions
- Savings analysis internal tools
- Train ml models for ship performance prediction
- Develop python tools for workflow automation
- Design data pipelines for real-time ship data
Hiring Activity
Steady10 roles · 4 in 30d
Department
Seniority
Company intelligence
