Putting an ML model into production using Feast and Kubeflow on Azure (Part I)

• boto3

  36 8,703 9.7 Python

  AWS SDK for Python

  

Before we can run this step, we need to create the S3 client. The code below does that using the resource function of the client. You can look at the source code here and here to get an understanding of what parameters to supply to create it and what the resource function returns.

• pipelines

  2 3,446 9.8 Python

  Machine Learning Pipelines for Kubeflow

  

Kubeflow Pipelines comes with a pre-defined KFServing component which can be imported from the GitHub repo and reused across the pipelines without the need to define it every time. KFServing is Kubeflow's solution for "productionizing" your ML models and works with a lot of frameworks like Tensorflow, sci-kit, and PyTorch among others.

• InfluxDB

  www.influxdata.com featured

  Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

  

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