Are you struggling with your cloud data management costs and architecture?

Are you looking for ways to accelerate your data engineering capacity?

By leveraging an age-old common tactic of generating SQL statements at runtime, structuring Dynamic SQL can accelerate the development of data pipelines.

Watch this Data Collab Lab to learn more.

Learn how Azure ML supports Open Source ML Frameworks and MLflow in AzureML.

Take a walk through a ScikitLearn and Pytorch example to show the built in support for ML frameworks.

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The most popular dataset on Kaggle is  Credit Card Fraud Detection. It’s an easy to understand problem space and impacts just about everyone. Fraud detection is a practical application that many businesses care about.  There’s a also something intrinsically cool about stopping crime with AI.

Here’s an interesting article on how to implement a fraud detection system with TensorFlow, PySpark, and Cortex.

While it would be cool to just build an accurate model, it would be more useful to build a production application that can automatically scale to handle more data, update when new data becomes available, and serve real-time predictions. This usually requires a lot of DevOps work, but we can do it with minimal effort using Cortex, an open source machine learning infrastructure platform. Cortex converts declarative configuration into scalable machine learning pipelines. In this guide, we’ll see how to use Cortex to build and deploy a fraud detection API using Kaggle’s dataset.