Cloud-Native Data Science Blog

Have you ever wondered what cloud-native actually is? Are you confused about how to use data science to improve your business? Check out these informative blogs to help you get started.

Fast Time-Series Filters in Python

Time-series (TS) filters are often used in digital signal processing for distributed acoustic sensing (DAS). The goal is to remove a subset of frequencies from a digitised TS signal. To filter a signal you must touch all of the data and perform a convolution. This is a slow process when you have a large amount of data. The purpose of this post is to investigate which filters are fastest in Python.

A Comparison of Reinforcement Learning Frameworks: Dopamine, RLLib, Keras-RL, Coach, TRFL, Tensorforce, Coach and more

Reinforcement Learning (RL) frameworks help engineers by creating higher level abstractions of the core components of an RL algorithm. This makes code easier to develop, easier to read and improves efficiency.

But choosing a framework introduces some amount of lock in. An investment in learning and using a framework can make it hard to break away. This is just like when you decide which pub to visit. It’s very difficult not to buy a beer, no matter how bad the place is.

Announcement: New Reinforcement Learning Book with O'Reilly

I’m excited to announce that I have agreed with O’Reilly Media to write a new book on Reinforcement Learning. The contracts have just been signed and I’ve started the writing process. It is likely to take around a year to be released so I’m hoping that it will be ready around Summer 2020.

Google Releases AI Platform with help from Winder Research

At their Cloud’s Next 19 conference, Google has announced the launch of an expanded AI platform. For a number of years Google has been expanding it’s portfolio to compete with AI products from Azure and AWS. But this is the first time that the platform can be considered “end-to-end”.

DevOps and Data Science: DataDevOps?

I’ve seen a few posts recently about the emergence of a new field that is kind of like DevOps, but not quite, because it involves too much data. Verbally, about two years ago and in blog form about a year ago, I used the word DataDevOps, because that’s what I did. I develop and operate Data Science platforms, products and services. But more recently I have read of the emergence of DataOps.

Local Jenkins Development Environment on Minikube on OSX

Developing Jenkinsfile pipelines is hard. I think my world record for the number of attempts to get a working Jenkinsfile is around 20. When you have to continually push and run your pipeline on a managed Jenkins instance, the feedback cycle is long. And the primary bottleneck to developer productivity is the length of the feedback cycle.

Scikit Learn to Pandas: Data types shouldn't be this hard

Nearly everyone using Python for Data Science has used or is using the Pandas Data Analysis/Preprocessing library. It is as much of a mainstay as Scikit-Learn. Despite this, one continuing bugbear is the different core data types used by each: pandas.DataFrame and np.array. Wouldn’t it be great if we didn’t have to worry about converting DataFrames to numpy types and back again? Yes, it would. Step forward Scikit Pandas.

7 Reasons Why You Shouldn't Use Helm in Production

Helm is billed as “the package manager for Kubernetes”. The goal was to provide a high-level package management-like experience for Kubernetes. This was a goal for all the major containerisation platforms. For example, Apache Mesos has Mesos Frameworks. And given the standardisation on package management at an OS level (yum, apt-get, brew, choco, etc.) and an application level (npm, pip, gem, etc.), this makes total sense, right?

Using Data Science to block hackers

Executive Summary Winder Research was engaged by Bitsensor to research and implement Data Science algorithms that could automate the detection and classification of web attackers. After gathering data, researching a Machine Learning solution and implementing Cloud-Native software, we delivered three new features: Tool classification - detect which automated tools were being used to perform the attack Attacker grouping - provide the capability of detecting distributed attacks by the same attacker Killchain classification - establish the phase of an attack (e.

Bulding a Cloud-Native PaaS

Executive Summary Winder Research worked with its partner, Container Solutions, to deliver core components of the Weave Cloud Platform-as-a-Service (PaaS). Kubernetes and Terraform implementations on Google Cloud Platform Delivered crucial billing components to track and bill for per-second usage Helped initiate, architect and deliver Weave Flux, a Git-Ops CI/CD enabler Client Weaveworks makes it fast and simple for developers and DevOps teams to build and operate powerful containerized applications.

EMail

web@WinderResearch.com

Registered Address

Winder Research and Development Ltd.,

Adm Accountants Ltd, Windsor House,

Cornwall Road,

Harrogate,

North Yorkshire,

HG1 2PW,

UK

Registration Number

08762077

VAT Number

GB214263735
© Winder Research and Development Ltd. 2016-2018; all rights reserved.