Glossary
Machine Learning as a Service (MLaaS)
What is Machine Learning as a Service (MLaaS)?
Machine Learning as a Service (MLaaS) provides tools and infrastructure through the cloud that enable businesses to develop, train, and deploy learning models without managing complex backend systems. MLaaS platforms offer services like data pre-processing, model training, and analytics, helping organizations harness machine learning capabilities quickly, efficiently, and at scale.
Benefits of MLaaS
MLaaS simplifies the machine learning process by offering pre-built algorithms, scalable computing resources, and integrated tools for data management and model evaluation. It allows organizations to innovate faster, reduce operational overhead, and focus on solving business problems rather than building infrastructure from scratch. Key benefits include:
Reduced costs: Avoids the expenses of developing and maintaining ML infrastructure.
Faster time-to-market: Accelerates the development and deployment of ML models.
Access to expertise: Leverages the knowledge and tools experienced MLaaS vendors provide.
Wasabi object storage for MLaaS
Machine learning projects require reliable, high-capacity storage for datasets and model outputs. Our object storage for ML delivers the speed, scalability, and cost-efficiency needed to power ML workloads while ensuring data security and accessibility. While Wasabi isn’t a direct MLaaS provider, it offers key benefits as a storage backend that can enhance MLaaS workflows. Wasabi storage benefits include:
Costs up to 80% less than the major hyperscalers with no egress fees, ideal for high-volume, data-intensive ML workloads
S3-compatible API, meaning you can drop Wasabi into ML pipelines with minimal configuration changes.
Immutable buckets that protect data from being modified or deleted, providing secure storage for critical datasets and model artifacts.
Storage with data center redundancy, allowing you to build robust ML architectures while offloading infrastructure management.