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.

Bulding a Cloud-Native PaaS

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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.

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How Winder.AI Made Enterprise Cloud Migration Possible

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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.

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A Comparison of Serverless Frameworks for Kubernetes

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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

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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

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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

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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

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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

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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?

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How to List all AMIs for each region in AWS

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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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Principal Component Analysis

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Dimensionality Reduction - Principal Component Analysis Welcome! This workshop is from Winder.ai. Sign up to receive more free workshops, training and videos. Sometimes data has redundant dimensions. For example, when predicting weight from height data you would expect that information about their eye colour provides no predictive power. In this simple case we can simply remove that feature from the data. With more complex data it is usual to have combinations of features that provide predictive power.

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