This article delves into getting started running pods with controllers in Kubernetes.
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Pods are too simple to be useful on their own — they’re isolated instances of an application, and each pod is allocated to one node. If that node goes offline then the pod is lost and Kubernetes doesn’t replace it. You could try to get high availability by running several pods, but there’s no guarantee Kubernetes won’t run them all on the same node. …
This article covers
• Recognizing the signs of tightly coupled code
• Strategies for reducing coupling
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Loose coupling is what allows you to affect change in different areas of your code without worry you’ll break something elsewhere. It’s also what allows you to work on one feature as your coworker tackles another. It’s the foundation for other desirable characteristics like extensibility. Without loose coupling, maintaining your code can quickly grow out of hand. …
In case you missed it, here is Carl Gold’s live Twitch coding stream.
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This article describes how and when to deploy Fluentd.
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When considering Fluentd deployment for production we need to consider volume metrics i.e. the amount of log data needing to be captured, filtered, routed and stored. To start with let’s assume we’re working in an environment that doesn’t demand hyper scaling. Despite this, at the end of the day we’re dealing with an element of processing demand, but most importantly log entries are being consumed and stored which creates a lot of I/O…
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Want a job in Data Science?
This article discusses one popular way to get the skills that you’re going to need: attending a bootcamp.
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A bootcamp is an 8–15 week intensive course put on by companies like Metis and Galvanize. During the bootcamp you spend 8+ hours every day learning data science skills, listening to industry speakers, and working on projects. At the end of the course, you’ll usually present a capstone project to a room full…
In this article, we’ll layer some of the critical and more advanced functionality needed for most data platforms today. Without this added layer of sophistication, your data platform would work but it wouldn’t scale easily, nor would it meet the growing data velocity challenges. It would also be limited in terms of the types of data consumers (people and systems who consume the data from the platform) it supports, as they’re also growing in both numbers and variety.
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Let’s…
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Our first case study explores a medical decision-making task: breast cancer diagnosis. We will see how to use scikit-learn’s homogeneous parallel ensemble modules in practice. Specifically, we will train and evaluate the performance of three homogeneous parallel algorithms, each characterized by increasing randomness: bagging with decision trees, random forests and ExtraTrees.
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Breast-cancer diagnosis is the task of classifying patient tumor data into one of two classes: malignant vs. benign. This task (and similar medical tasks) are at the…
The Women-in-Tech Anthology contains four candid and insightful essays from women working across the tech industry. Filled with spirited anecdotes, hard-won lessons, and sage advice, each essay is a must-read for anyone looking to survive, thrive, and support their colleagues in tech. Alongside these unique essays, the anthology contains Manning book chapters written by accomplished women working at the top of their fields.
The Women-in-Tech Anthology begins with four essays from Anne Michels, Lizzie Siegle, Naomi Cedar, and Jamie Riedesel. …
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