TurboML is a machine learning platform that's reinvented for real-time. What does that mean? All the steps in the ML lifecycle, from data ingestion, to feature engineering, to ML modelling to post deployment steps like monitoring, are all designed so that in addition to batch data, they can also handle real-time data.
Highlights
TurboML empowers your machine learning workflows with real-time predictions, real-time features, and continual learning. Adapt models instantly to live production data for use cases like ETA prediction and fraud detection, ensuring your models stay accurate and effective.
Transform raw data into fresh, real-time features using TurboML's streaming computation and aggregation capabilities. Improve experimentation and context-based model updates without the need for complex pipelines.
TurboML ensures your models stay updated with the freshest data, improving business outcomes like fraud detection, user engagement, click-through rates, and recommendations. Proven success across industries with use cases from TikTok, LinkedIn, and Airbnb.
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Pricing is based on the duration and terms of your contract with the vendor, and additional usage. You pay upfront or in installments according to your contract terms with the vendor. This entitles you to a specified quantity of use for the contract duration. Usage-based pricing is in effect for overages or additional usage not covered in the contract. These charges are applied on top of the contract price. If you choose not to renew or replace your contract before the contract end date, access to your entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
This listing uses a single pricing dimension: TurboML Units. You buy a set number of Units through your contract, and that allocation covers your use of the platform. Pricing scales with the number of Units you commit to under your agreement. There are no separate tiers, instance sizes, or add-on charges to choose between. All platform capabilities, including data ingestion, feature engineering, model deployment, and monitoring, draw from the same Unit allocation. To size the right number of Units for your workload, contact the vendor.
Top-of-mind questions for buyers
What does one TurboML Unit map to for billing purposes?
The pricing table defines a Unit only as an amount allocated under your contract. It does not map to a fixed resource like a model, seat, or host. All platform work draws from your allocated Units. To learn how Units convert to your specific usage, contact the vendor.
What platform activities consume my allocated Units?
Your Units cover the full real-time ML lifecycle in one allocation. This includes pulling or pushing data into the platform, defining and computing features, training and deploying models, running inference, and monitoring model metrics. All these activities draw from the same Unit pool rather than billing separately.
What happens if my workload needs more than my allocated Units?
Your contract sets a fixed number of Units, and platform use draws from that allocation. There are no separate overage tiers or add-on charges in the pricing table. If your workload grows beyond your allocation, contact the vendor to adjust the number of Units under your agreement.
docs.turboml.com
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Vendor refund policy
TurboML operates as a pay-as-you-go service, and you will be invoiced based on your actual usage.
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