MLOps with the autopilot

SageMaker Autopilot and Snowflake External Function: a gold medal integration
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Video Talk
Date and Time: 24/09/2021

Creating Machine Learning (ML) pipelines can often be complicated because of the tools and in-depth technical knowledge needed to create and deploy the right prediction models.

AutoML systems provide a black-box solution to these problems, looking for the right way to process and select features, choosing an algorithm, and fine-tuning the hyperparameters of the entire pipeline.

Being able to recall these autoML systems also from non-canonical model development environments, such as a DWH query shell, is possible, for a wide range of users, to harness the power of predictive analytics even without a deep machine learning experience.

Main topics

In this webinar, we talked about how it becomes increasingly important and strategic, within a business process that involves an advanced analysis of data, the reduction of the gap between the testing environment, and the use of the chosen analysis model.

The integration of AWS sagemaker’s Autopilot functions into Snowflake allows you to reduce this gap by bringing a suitable analysis environment close to the organized data to experiment, create and then make accessible the models designed for a given business case.

Finally, we saw in a demo this winning integration between AWS and Snowflake working on the Olympic forecasts.

Key elements

  • MLOps
  • Serving ML model
  • AWS SageMaker Autopilot
  • Snowflake External function
  • Demo: Olympic forecasts

Target audience

Data Scientist
Data & Machine Learning Engineer




MLOps con il pilota automatico – Quantyca, AWS & Snowflake

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