Many #ML startups face challenges growing at scale and managing its infrastructure.
FinTech startup, @digits was able to mitigate these challenges with the early deployment of TensorFlow Extended.
Learn more ➡️ goo.gle/3gvm2LS
FinTech startup, @digits was able to mitigate these challenges with the early deployment of TensorFlow Extended.
Learn more ➡️ goo.gle/3gvm2LS
💡No. 1: Standardization
With TFX, @digits can increase code reusability and ramp up projects faster than ever before.
⚡Its continuous integration system automatically deploys its #ML models and tracks all changes in its Git repository.
With TFX, @digits can increase code reusability and ramp up projects faster than ever before.
⚡Its continuous integration system automatically deploys its #ML models and tracks all changes in its Git repository.
💡 No. 2: Growth
📈 With minimal code changes, @digits can easily swap out its Apache Beam configuration when its datasets grow and require more processing capabilities.
📈 With minimal code changes, @digits can easily swap out its Apache Beam configuration when its datasets grow and require more processing capabilities.
Since TFX is integrated with the ML Metadata Store, @digits can store all of its model details in one place. This allows them to easily generate model lineages without any additional overhead 👥
Want to learn more about how other startups can benefit from TFX?
Get the details here ↓ goo.gle/3gvm2LS
Get the details here ↓ goo.gle/3gvm2LS
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