Anthony Corletti cloud computing. startups. music. etc.
Posts with the tag ml:

## AI Infrastructure on Kubernetes

The rise in usage of cloud computing resources and container management platforms for executing AI (Artificial Intelligence) and ML (Machine Learning) workloads has led many engineers and companies to question the suitability and effectiveness of Kubernetes’ resource management and scheduling to meet the growing requirements of these workloads.

So why’s that? What patterns, architectures, and procedures has led these companies and engineers to this problem of scaling ML platforms on Kubernetes? And what kind of solution could we apply to help solve those problems?

## Linear Regression with Go

Linear regression is a common statistical data analysis technique.

It is used to determine the extent to which there is a linear relationship between a dependent variable and one or more independent variables, and is widely used in machine learning applications.

There are two types of linear regression, simple linear regression and multiple linear regression.

In simple linear regression a single independent variable is used to predict the value of a dependent variable. In multiple linear regression two or more independent variables are used to predict the value of a dependent variable. The difference between the two is the number of independent variables. In both cases there is only a single dependent variable.

In this post I’m going to introduce basic concepts on linear regression and build a simple linear regression example using go and spago.

## Partial Derivatives & Recursive Descent

Writing partial derivatives is a great way to understand some of the underlying features of machine learning and neural network libraries.

In this post I’ll explain how partial derivates are a necessary building block in understanding machine learning and neural networks, and how to write some python code to help bring partial derivates and recursive descent to life!

## Machine Learning with GoLearn

It’s really easy to build a K-nearest-neighbors implementation in go using golearn.

After searching for ways to start writing more go, especially ways that provide alternatives to familiar languages and frameworks, I’ve wanted to find machine learning libraries in golang because I tend to rely on python microservices and the rich ecosystem of ml libraries.

I’m writing this post using go1.14.4 so let’s dive in to installing golearn and writing up a quick example K-nearest-neighbors implementation.

Building lightweight ML applications with python, pandas, streamlit, and scikit-learn is a breeze.