Michael Perlin is Senior Consultant at INNOQ. Since more than fifteen years he has been working on multiple topics around Software Development, DevOps and Machine Learning. https://speakerdeck.com/mperlin 2019-03-05 05:53:24 -0500 When deploying your model into production, you have to care about configuring and maintaining the runtime environment, scaling, monitoring and more – all tasks, which are more related to DevOps than to ML. In some contexts, you can achieve your goals in a much simpler way by establishing a "serverless" approach. We’ll take a look at the cloud services "AWS Lambda", "Google Cloud Functions", and "Azure Functions" and show how they enable running ML inference. When deploying your model into production, you have to care about configuring and maintaining the runtime environment, scaling, monitoring and more – all tasks, which are more related to DevOps than to ML. In some contexts, you can achieve your goals in a much simpler way by establishing a "serverless" approach. We’ll take a look at the cloud services "AWS Lambda", "Google Cloud Functions", and "Azure Functions" and show how they enable running ML inference. Mon, 04 Mar 2019 00:00:00 -0500 https://speakerdeck.com/mperlin/run-ml-as-cloud-function https://speakerdeck.com/mperlin/run-ml-as-cloud-function