Voyage performance varies even between seemingly identical ships, and models built on vessel-type averages often miss the mark. Manta Marine Technologies (Manta) closes that gap with machine learning (ML) models trained on each vessel’s own operational history. In this post, we show how Manta delivers vessel-specific voyage optimization with machine learning on Amazon Web Services (AWS), helping shipowners reduce fuel consumption and improve voyage efficiency.
Differences in hull condition, trim, draft, machinery condition, loading patterns, and operational practices mean that when operators build models on vessel-type averages, those models often can’t capture how a specific vessel performs. For shipowners, this gap translates directly into unnecessary fuel consumption and cost.
Manta takes a vessel-specific approach. The company trains machine learning models on each vessel’s own operational history and combines them with forecast weather and ocean conditions. To support this approach at scale, Manta built its data infrastructure on AWS. Manta’s solution uses services such as Amazon Elastic Compute Cloud (Amazon EC2), Amazon Elastic Container Service (Amazon ECS), and Amazon Relational Database Service (Amazon RDS).
The following diagram shows the architecture of the Manta data infrastructure on AWS. The system receives vessel data and environmental forecasts in a secure, multi-Availability Zone environment. Manta’s solution processes this data, stores it, and uses it to train and run machine learning models.
Figure 1: Architecture of Manta’s data infrastructure on AWS
In the diagram, vessel data and environmental forecasts enter an Amazon Virtual Private Cloud (Amazon VPC) deployed across multiple Availability Zones. Inside the VPC, Manta uses Apache Airflow on Amazon EC2 to orchestrate data pipelines and machine learning workloads as Amazon ECS tasks on EC2 capacity, with a separate Auto Scaling group and capacity provider for each instance type. AWS Lambda serves as the entry point for requests from client applications. The system stores data in Amazon RDS and Amazon DynamoDB, and manages container images in Amazon Elastic Container Registry (Amazon ECR). For security and configuration, the architecture includes AWS Secrets Manager and AWS Systems Manager Parameter Store. The Airflow scheduler also drives the retraining loop: as new voyage data accumulates for a vessel, it launches a training task on the appropriate instance type, and the updated model is used for that vessel’s subsequent voyage optimizations. Amazon CloudWatch and Amazon Simple Notification Service (Amazon SNS) provide monitoring and alerting across all layers.
The challenge: Predicting how a specific vessel performs
Manta’s goal is voyage guidance that continuously learns from each vessel’s behavior and improves over time. This requires infrastructure that processes large volumes of operational vessel data—up to 3 billion data points in a single training run. The infrastructure also combines historical performance with weather and ocean forecasts. Manta’s solution must also run machine learning workflows, deliver guidance globally, and support continuous model improvement as new voyage data arrives.
The solution: A cloud-native data infrastructure built on AWS
Manta runs its data infrastructure as a cloud-native solution on AWS, deployed across multiple Availability Zones within an Amazon VPC.
At the core of the infrastructure, workflow orchestration with Apache Airflow runs on Amazon EC2. Airflow runs on a persistent EC2 instance because the orchestration layer needs a long-running server process. Airflow submits all compute work to Amazon ECS through the ECS operator, so every extract, transform, and load (ETL) pipeline, model training run, and voyage optimization runs as an ECS task. Manta runs those tasks on EC2 capacity rather than AWS Fargate because EC2 gives the team direct control over instance type, including GPU instances and specific chip generations. Manta defines one capacity provider, one Auto Scaling group, and one launch template per instance type instead of sharing a single pool. Model training and voyage optimization use different instance types. Per-instance-type capacity gives granular control over how each workload scales, running less urgent jobs on Spot capacity and time-sensitive jobs On-Demand. The infrastructure also uses AWS Lambda as the entry point for requests from client applications. A Lambda function triggers the corresponding Airflow directed acyclic graph (DAG) or returns already-processed results from the database. Manta manages container images in Amazon ECR, with separate images for Graviton and x86-64 instances, GPU workloads, orchestration, and machine learning operations. Manta stores data in Amazon RDS and Amazon DynamoDB.
For observability, Manta uses Amazon CloudWatch for monitoring and Amazon SNS for alerting. Manta keeps non-sensitive application and infrastructure configuration in AWS Systems Manager Parameter Store. For sensitive values such as database connection credentials, Airflow application secrets, and authentication tokens for third-party services, Manta uses AWS Secrets Manager. Those secrets are encrypted, and access to them is restricted through IAM permissions. Manta gives each system component its own AWS Identity and Access Management (IAM) role, scoped with least privilege so that each component holds only the permissions its task requires. The company also encrypts data with AWS Key Management Service (AWS KMS).
Because the infrastructure runs in the cloud, Manta continuously improves its models as new voyage data becomes available. Each completed voyage contributes operational data to future model training, and an Airflow scheduler retrains each vessel’s model periodically, based on how much new data that vessel has produced. This creates a learning cycle that makes recommendations increasingly relevant for that specific vessel.
Why AWS
With AWS, Manta has the scalability to support growing fleets and increasing data volumes. The company stores historical vessel performance data, processes weather and ocean forecasts, and trains and operates its machine learning models. It also delivers voyage recommendations to fleets operating worldwide and scales compute capacity on demand as workloads grow.
Cost efficiency is also a key factor. Manta runs its ETL pipelines, model training, and voyage optimization workloads on AWS Graviton-based instances. For one ETL pipeline that processes large datasets, Manta observed approximately 25–30 percent better price performance after moving to Graviton-based instances. More broadly, Graviton-based instances can offer up to 40 percent better price performance than comparable x86-based instance options on AWS
With AWS, Manta’s development teams can focus on improving optimization algorithms and customer outcomes instead of managing physical infrastructure. Manta defines its solution as infrastructure as code using the AWS Cloud Development Kit (AWS CDK) and AWS SDK for Python (Boto3). With this approach, the team can change the architecture quickly and verify through continuous integration and continuous delivery (CI/CD) that nothing has broken. It can also reproduce identical development, staging, and production environments.
That reproducibility also changes how the team experiments. Manta’s engineers develop and validate machine learning and data engineering work on their own machines, against large samples of vessel data pulled from the system. The only cost of an experiment is then retrieving the data. Because that work runs in the same container images the system uses in production, a validated algorithm reaches the full fleet without re-engineering and without infrastructure to provision. When an experiment outgrows a local machine, the team runs it at full scale on AWS instead of scoping it down. They submit the same container to the instance type the workload needs, whether that means GPU capacity or many parallel CPU tasks. Experimentation stays cheap when it can be and unconstrained when it needs to be. Scaling a validated result to every vessel is then a scheduling decision rather than a project.
The results: Guidance built around how each vessel actually performs
Through Manta’s solution, ship operators receive voyage-specific guidance built on the vessel’s own historical performance and relevant external data such as atmospheric and oceanic weather data. The system’s continuous improvement loop refines this guidance as new voyage data arrives, with each vessel’s model retrained as its own operational history grows.
Fleet managers gain guidance designed around how each vessel actually performs, rather than vessel-type averages or industry benchmarks. Pairing vessel-specific data with decades of maritime operational expertise helps customers run greener and more profitable operations.
Based on internal voyage simulations conducted across its active fleet, Manta expects fuel savings of approximately 2–4 percent per vessel. The company anticipates further gains as its models improve and new capabilities are added.
Conclusion
Manta built a cloud-native data solution on AWS to deliver vessel-specific voyage optimization. Apache Airflow on Amazon EC2 orchestrates data pipelines and machine learning workloads across EC2, Amazon ECS, and AWS Lambda. Manta stores data in Amazon RDS and Amazon DynamoDB and enforces security through least-privilege IAM roles and AWS KMS. Graviton-based instances keep the solution cost efficient as data volumes grow. Every completed voyage feeds a learning cycle that makes guidance increasingly tailored to each vessel.
To learn more about Manta’s voyage optimization solution, visit the Manta Marine Technologies website. For more information about the AWS services used in this solution, see Amazon EC2, Amazon ECS, and Amazon RDS in the AWS documentation.
For related reading, see Using machine learning to predict vessel time of arrival with Amazon SageMaker and How potential performance upside with AWS Graviton helps reduce your costs further.
