
When this happens...

Automatically do this!
Enable Integrations or automations with these events of Nyckel and TimescaleDB
Import data to your Nyckel classifier
Classify image or text data
Detect Objects center in images
Train your Nyckel classifier by adding more data
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Gain insights into how viaSocket functions through our detailed guide. Understand its key features and benefits to maximize your experience and efficiency.

Unlock your team's potential with 5 straightforward automation hacks designed to streamline processes and free up valuable time for more important work.

Workflow automation is the process of using technology to execute repetitive tasks with minimal human intervention, creating a seamless flow of activities.
To start, connect both your Nyckel and TimescaleDB accounts to viaSocket. Once connected, you can set up a workflow where an event in Nyckel triggers actions in TimescaleDB (or vice versa).
Absolutely. You can customize how Nyckel data is recorded in TimescaleDB. This includes choosing which data fields go into which fields of TimescaleDB, setting up custom formats, and filtering out unwanted information.
The data sync between Nyckel and TimescaleDB typically happens in real-time through instant triggers. And a maximum of 15 minutes in case of a scheduled trigger.
Yes, viaSocket allows you to add custom logic or use built-in filters to modify data according to your needs.
Yes, you can set conditional logic to control the flow of data between Nyckel and TimescaleDB. For instance, you can specify that data should only be sent if certain conditions are met, or you can create if/else statements to manage different outcomes.
Nyckel is a platform that provides machine learning as a service, enabling users to easily integrate AI capabilities into their applications without needing extensive knowledge in data science or machine learning. It offers tools for creating, training, and deploying machine learning models with ease.
Learn MoreTimescaleDB is a powerful time-series database designed for fast ingest and complex queries, making it ideal for handling time-series data efficiently. It extends PostgreSQL, providing scalability and performance enhancements specifically for time-series workloads.
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