KFServing - Learn Data Science with Travis

Travis chat

KFServing

KFServing is a Kubernetes-native platform designed for serving machine learning models. It provides a robust framework for deploying, managing, and scaling machine learning models in production environments. KFServing simplifies the process of serving models by offering features such as automatic scaling, rollout, and monitoring.

One of the key benefits of KFServing is its support for multiple frameworks, allowing users to deploy models built with TensorFlow, PyTorch, Scikit-learn, and others. This flexibility makes it easier for data scientists and machine learning engineers to integrate their preferred tools into a unified serving architecture.

Additionally, KFServing includes capabilities for advanced features like A/B testing, canary deployments, and model versioning, which are essential for maintaining and improving model performance over time. By leveraging Kubernetes, KFServing ensures high availability and resilience, making it a suitable choice for organizations looking to operationalize their machine learning workflows effectively.

If you have specific questions about KFServing or need more detailed information, feel free to ask!

Kafdrop

Towards Data Science

As a messaging platform, Kafka needs no introduction. Since its inception, it has virtually rewritten the book on event streaming and has catalyzed the adoption of the now household design patterns —…...

KLDivLoss

PyTorch documentation

The Kullback-Leibler divergence loss. For tensors of the same shape y pred , y true y_{\text{pred}},\ y_{\text{true}} y pred , y true , where y pred y_{\text{pred}} y pred is the input and y tru...

Introducing K-FAC

Towards Data Science

In this article, I summarize Kronecker-factored Approximate Curvature (K-FAC) (James Martens et al., 2015), one of the most efficient second-order optimization method for deep learning. The heavy…

What the f?

Towards Data Science

I know you are eager to find out what this ‘f’ word actually is. Stay with me, we will get to it very soon. One thing I can tell you right away is that regardless of your familiarity with Machine…

KNN

Analytics Vidhya

K-Nearest Neighbors is simple algorithm. The elegance of this algorithm lies in it’s simplicity. Despite it’s various drawback’s such as high compute time in high dimension data it is still widely…

The Four F’s

Towards Data Science

I teach a 3rd year undergraduate course on data science. It is not your typical course of lectures, practicals and tutorials. Lectures are few and far between and it is more about the practice of…

Queue FI-FO Fun

Level Up Coding

While implementing optimizations, software developers may encounter performance issues that extend beyond a particular function or module. A potential problem in architecture, where two modules are ti...

Beating the Odds

Towards Data Science

This is the first installment in my weekly sports themed series. Each week I’ll demonstrate applications of data science and seek to provide thoughtful analysis and insight into the games we love to…

Using

The Python Standard Library

Using importlib.metadata New in version 3.8. Changed in version 3.10: importlib.metadata is no longer provisional. Source code: Lib/importlib/metadata/__init__.py importlib.metadata is a library that...