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Within machine learning, the hardest aspect often becomes deploying to production, until the time comes to address the issue. Applied at scale, this issue can hinder deployment, and at the worst, kill the project entirely.

In this video, we will learn:
– State of Machine Learning
– Why do ML projects & teams struggle to reach production?
– Working as a single data scientist at a startup
– ML project management
– Demo – Building a CI/ CD ML Pipeline
– Monitoring (tentative)
– Cloud best practices

Application: http://skirunrecommender.com/
Code: https://github.com/gregwchase/ski-recommender/tree/ski-recommender-v2

Deploying AI/ML based applications is far from trivial. On top of the traditional DevOps challenges, you need to foster collaboration between multidisciplinary teams (data-scientists, data/ML engineers, software developers and DevOps), handle model and experiment versioning, data versioning, etc. Most ML/AI deployments involve significant manual work, but this is changing with the introduction of new frameworks that leverage cloud-native paradigms, Git and Kubernetes to automate the process of ML/AI-based application deployment.

#MLOps #Machinelearning #CICD

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