A step-by-step beginners’ guide in Python, to try Kafka with Spark Structured streaming using Network traffic data as an use case.

In this blog, we are going to build a basic prototype on how we can stream live network traffic to Apache Spark using Apache Kafka data feed platform.

In today’s Big Data world, it’s all about real-time processing of continuous and huge set of information and taking decisions / fetching insights instantaneously. Both - sources of data like IOT devices, telemetry devices etc., and the receivers of data like databases, analytics / visualization engines etc. , need efficient, fast…

An Exercise In Keras Recurrent Neural Networks And LSTM

In a previous blog, I had explained an example of Time Series Forecast in Python, using classical time series analysis methods like SARIMA. In this blog, I take up an example of training deep neural networks like RNN / LSTM in Keras, for forecasting Time Series.

A Time Series is typically defined as a series of values that one or more variables take over successive time periods. For example, sales volume over a period of successive years, average temperature in a city over months etc. If the series is about only…

An example using classical time series analysis methods (SARIMA)

*In this blog, I explain how a simple univariate time series forecasting can be done in python.*

A Time Series is typically defined as a series of values that one or more variables take over successive time periods. For example, sales volume over a period of successive years, average temperature in a city over months etc. If the series is about only one variable, it is called Univariate Time Series. If the series lists values of more than one variables over different points of time, it is called Multivariate Time Series…

In this article, we will try out unsupervised Machine Learning methods of clustering, for grouping hosts in a IP network in to different clusters based on the similarity in their IP addresses. Importantly, we will see the difference between K-Means and GMM (Gaussian Mixture Model) in their approaches in clustering, and compare their results. We will also see how we can find out the outliers/anomalies in the data using GMM more effectively/

Clustering is an unsupervised class of ML algorithms which separate and group given data points in to distinct groups of data in such a way that data points…

In this article, I will share an example on how we can deploy a locally trained Machine Learning model in cloud using AWS SageMaker service. By “locally trained” , I mean a ML model which is trained locally in our laptop ( i.e. outside AWS cloud ). I will take you through the various steps starting from training a model, to the deployment of the model in the AWS cloud and invoking the deployment from a local client to get predictions.

If we google for the ways to deploy a ML model in AWS, we will find quite a few…

Experienced Networking Software Developer on a Data / Machine Learning Journey https://www.linkedin.com/in/rajaramsurya/