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deploying-a-machine-learning-model-to-the-cloud-using-aws-lambda-dr-benjamin-weigel.json
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deploying-a-machine-learning-model-to-the-cloud-using-aws-lambda-dr-benjamin-weigel.json
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{
"abstract": "**Agenda** : - Why you need a service for your model? - Short intro to\nAWS Lambda and API gateway - Constructing a service around your model -\nSetting up AWS & deployment environment - Using the serverless framework\nto deploy your service to AWS - Things you should be aware of\n\n| **Prerequisites** : You should be familiar with using\n `Docker <https://docs.docker.com/install/>`__ and have it installed.\n Please refer to the GitHub repo accompanying this workshop and\n| `**install the necessary software** :\n bweigel/ml\\_at\\_awslambda\\_pydatabln2018 <https://github.com/bweigel/ml_at_awslambda_pydatabln2018#pydata-%20attendees>`__.\n\nAlso, you should already have an `Amazon Web Services\n(AWS) <https://aws.amazon.com/>`__ account if you want to follow this\nsession.\n\nWe will be using the `free tier <https://aws.amazon.com/free/>`__ of\nAWS.\n\n**Slides for the talk:**\n`https://bweigel.github.io/pydata\\_bln\\_2018/ <https://bweigel.github.io/pydata_bln_2018/index.html#/>`__\n",
"copyright_text": null,
"description": "Take your machine learning model out of your desk drawer and show its\nbenefit to the world through a simple API using AWS Lambda and API\ngateway. This tutorial will bridge the gap between having a machine\nlearning model (e.g. in your Jupyter notebooks) and taking it to a level\nwhere others can benefit from it (i.e. through an API).\n",
"duration": 5235,
"language": "eng",
"recorded": "2018-07-06",
"related_urls": [
{
"label": "Conference schedule",
"url": "https://pydata.org/berlin2018/schedule/"
}
],
"speakers": [
"Dr. Benjamin Weigel"
],
"tags": [],
"thumbnail_url": "https://i.ytimg.com/vi/4ocbx9IeBMU/maxresdefault.jpg",
"title": "Deploying a machine learning model to the cloud using AWS Lambda",
"videos": [
{
"type": "youtube",
"url": "https://www.youtube.com/watch?v=4ocbx9IeBMU"
}
]
}