AI, Machine Learning, Reinforcement Learning, and MLOps Articles

Learn more about AI, machine learning, reinforcement learning, and MLOps with our insight-packed articles. Our AI blog delves into industrial use of AI, the machine learning blog is more technical, the reinforcement learning blog is industrially renowned, and our mlops blog discusses operational ML.

Using Data Science to block hackers

Published
Author
Dr. Phil Winder
CEO

Executive Summary

Winder.AI 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.g. reconnaissance, exploitation, etc.)

Client

Bitsensor is a startup in the Netherlands that specializes in protecting public-facing websites and applications. They distribute their web-application firewall product to a range of customers throughout Europe. The goal is to provide an outstanding out-of-the-box experience that can protect exposed services from hackers, with little setup.

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Bulding a Cloud-Native PaaS

Published
Author
Dr. Phil Winder
CEO

Executive Summary

Winder.AI 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. They minimize the complexity of operating workloads in Kubernetes by providing automated continuous delivery pipelines, observability and monitoring. Weaveworks also contributes to several open source projects, including Weave Scope, Weave Cortex and Weave Flux. It was one of the first members of the Cloud Native Computing Forum. Founded in 2014, the Company is backed by Google Ventures and Accel Partners. For more information, visit www.weave.works.

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How Winder.AI Made Enterprise Cloud Migration Possible

Published
Author
Dr. Phil Winder
CEO

Executive Summary

  • Truly global company, tens of thousands of staff across tens of regions.
  • Problem: Colossal amounts of data, lack the computational flexibility to remain competitive.
  • Solution: Cloud data platform leveraging Microservices, Serverless object storage and database technologies.
  • Benefits: 4x faster, more memory and number of gpus compared to best on-premise hardware. 10x quicker time to market. 10 Petabytes of data.

A very large enterprise in the oil and gas industry asked Winder.AI to help them migrate mission critical workflows to the cloud and create competitive differentiators through the application of Data Science and providing AI consulting.

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A Comparison of Serverless Frameworks for Kubernetes

Published
Author
Dr. Phil Winder
CEO

The term Serverless has become synonymous with AWS Lambda. Decoupling from AWS has two benefits; it avoids lock in and improves flexibility.

The misnomer Serverless, is a set of techniques and technologies that abstract away the underlying hardware completely. Obviously these functions still run on “servers” somewhere, but the point is we don’t care. Developers only need to provide code as a function. Functions are then used or consumed via an API, usually REST, but also through message bus technologies (Kafka, Kinesis, Nats, SQS, etc.).

This provides a comparison and recommendation for a Serverless framework for the Kubernetes platform.

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How to Test Terraform Infrastructure Code

Published
Author
Dr. Phil Winder
CEO

Infrastructure as code has become a paradigm, but infrastructure scripts are often written and run only once. This works for simplistic infrastructure requirements (e.g. k8s deployments). But when there is a requirement for more varied infrastructure or greater resiliency then testing infrastructure code becomes a requirement. This blog post introduces a current project that has found tools and patterns to deal with this problem.

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Cloud Native Data Science: Best Practices

Published
Author
Dr. Phil Winder
CEO

Following the Cloud Native best practices of immutability, automation and provenance will serve you well in a CNDS project. But working with data brings its own subtle challenges around these themes.

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Cloud Native Data Science: Technology

Published
Author
Dr. Phil Winder
CEO

Technology choices in data-driven products are, as you would expect, largely directed by the type and amount of data. The first and most crucial decision to make is whether the data will be processed in a batch or streaming fashion.

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Cloud Native Data Science: Strategy

Published
Author
Dr. Phil Winder
CEO

Data Science has become an important part of any business because it provides a competitive advantage. Very early on, Amazon’s data on book purchases allowed them to deliver personalised recommendations whilst customers were browsing their site. Their main competitor in the US at the time was Borders, who mainly operated in physical stores. This physicality prevented them from seamlessly providing customers with personalised recommendations [1]. This example highlights how strategic business decisions and data science are inextricably linked.

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Life and Death Decisions: Testing Data Science

Published
Author

Abstract

We live in a world where decisions are being made by software. From mortgage applications to driverless vehicles, the results can be life-changing. But the benefits of automation are clear. If businesses use data science to automate decisions they will become more productive and more profitable.

So the question becomes: how can we be sure that these algorithms make the best decisions? How can we prove that an autonomous vehicle will make the right decision when life depends on it? How can we prove that data science works?

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How to List all AMIs for each region in AWS

Published
Author
Dr. Phil Winder
CEO

A current project required a list of Amazon Machine Images (AMIs) for all regions for use in terraform. I couldn’t find a script to do this for me, so here you will find one that uses the aws cli, jq and a bit of Bash.

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