How We Work With Cloud-Native Data Science: An Interview With Phil Winder
Jun 2020, by Phil Winder, in Cloud Native, Data Science, Talk
A Simple Docker-Based Workflow for Deploying a Machine Learning Model
Apr 2020, by Phil Winder, in MLOps, Cloud Native
In software engineering, the famous quote by Phil Karlton, extended by Martin Fowler goes something like: “There are two hard things in computer science: cache invalidation, naming things, and off-by-one errors.” In data science, there’s one hard thing that towers over all other hard things: deployment.
Local Jenkins Development Environment on Minikube on OSX
Mar 2019, by Phil Winder, in Software Engineering, Cloud Native
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.
7 Reasons Why You Shouldn't Use Helm in Production
Jan 2019, by Phil Winder, in Cloud Native
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?
Bulding a Cloud-Native PaaS
Oct 2018, by Phil Winder, in Cloud Native, Case Study
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.
How Winder.AI Made Enterprise Cloud Migration Possible
Oct 2018, by Phil Winder, in Case Study, Cloud Native
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.
A Comparison of Serverless Frameworks for Kubernetes: OpenFaas, OpenWhisk, Fission, Kubeless and more
Sep 2018, by Phil Winder, in Cloud Native
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.
How to Test Terraform Infrastructure Code
Aug 2018, by Phil Winder, in Software Engineering, Cloud Native
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.
Cloud Native Data Science: Best Practices
May 2018, by Phil Winder, in Data Science, Cloud Native
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.
Cloud Native Data Science: Technology
May 2018, by Phil Winder, in Data Science, Cloud Native
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.