Using Kubernetes for Machine Learning Frameworks

This video will explain why and how Kubernetes is well suited for training and running your machine learning models in production. Specifically it will show how to setup a variety of open source machine learning frameworks such as TensorFlow, Apache MXNet and Pytorch on a Kubernetes cluster.

This video will explain why and how Kubernetes is well suited for training and running your machine learning models in production. Specifically it will show how to setup a variety of open source machine learning frameworks such as TensorFlow, Apache MXNet and Pytorch on a Kubernetes cluster.

Kubernetes provides isolation, auto-scaling, load balancing, flexibility and GPU support. These features are critical to run computationally and data intensive and hard to parallelize machine learning models. Declarative syntax of Kubernetes deployment descriptors make it easy for non-operationally focused engineers to easily train machine learning models on Kubernetes.

Attendees will learn training, massaging and inference phases of setting up a Machine Learning framework on Kubernetes. Attendees will leave with a GitHub repo of fully working samples.

kubernetes machine-learning tensorflow python

How To Plot A Decision Boundary For Machine Learning Algorithms in Python, you will discover how to plot a decision surface for a classification machine learning algorithm.

You got intrigued by the machine learning world and wanted to get started as soon as possible, read all the articles, watched all the videos, but still isn’t sure about where to start, welcome to the club.

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Python For Machine Learning | Machine Learning With Python, you will be working on an end-to-end case study to understand different stages in the Machine Learning (ML) life cycle. This will deal with 'data manipulation' with pandas and 'data visualization' with seaborn. After this an ML model will be built on the dataset to get predictions. You will learn about the basics of scikit-learn library to implement the machine learning algorithm.

Python for Machine Learning | Machine Learning with Python, you'll be working on an end-to-end case study to understand different stages in the ML life cycle. This will deal with 'data manipulation' with pandas and 'data visualization' with seaborn. After this, an ML model will be built on the dataset to get predictions. You will learn about the basics of the sci-kit-learn library to implement the machine learning algorithm.