Sorites de produit, innovation, start-up

DevOps and SRE, Chapter 1: When Innovation Becomes Mainstream

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Abstract

Cloud-native applications are a type of complex system that depends on the continuous effort of software professionals that combines the best of their expertise to keep them running. In other words, their reliability isn’t self-sustaining, but is a result of the interactions of all the different actors engaged in their design, build, and operation.

Over the years the collection of those interactions has been evolving together with the systems they were designed to maintain, which have been also becoming increasingly sophisticated and complex. The IT service management model, once designed to maintain control and stability, is now fading and giving place to a model designed to improve velocity while maintaining stability. Although the combination of those things might seem contradictory at first, this series of articles tries to reveal the reasons why the collection of practices that today we know as DevOps and SRE (Site Reliability Engineering) are becoming the norm for modern systems.

Source de l’article sur DZONE

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Trust and Openness Are Key to Innovation

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Would you trust your team members in this scenario? Well, metaphorically speaking, you’d better if you want to be innovative.

Innovation thrives on openness. While it’s common to think that innovation largely consists of revolutionary breakthroughs, in reality, it is much more common for it to be a slow and iterative process of gradual improvements and remixing of existing technologies in new and novel ways. 

You may also like:  How to Shift Your Internal Culture Towards Innovation

As such, being open with your own insights, and others doing likewise, is crucial to the innovation process.

Source de l’article sur DZONE

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Tom’s Tech Notes: How to Innovate Better [Podcast]

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Welcome to our latest episode of Tom’s Tech Notes! This week, DZone.com’s research analyst Tom Smith chats with Dell Boomi CTO Michael Morton about how to innovate better. Learn who to approach about innovating, how to plan for it, and the importance of not just failing fast, but learning and implementing fast.

And, as always, you can find our podcasts on:

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Middle Managers, the Flow of Ideas, and Innovation

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It really does take a village, not just upper management.

Few people in an organization have been the focus of so much attention in innovation circles as middle managers. Depending on your point of view, they are seen as either an essential conduit by which information flows, or a barrier to the spread of ideas and knowledge.

Indeed, it’s a topic I myself touched upon when I looked at some new research from Wharton’s Ethan Mollick on the topic. Mollick suggested that middle managers are especially important in industries that require innovative employees such as biotech, computing, and media.

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Relationships at Work Are Everything

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Even at work, it’s all about who you connect with.

Malcolm Gladwell famously shed light on the role of ‘connectors’ in his best selling book The Tipping Point. He regarded connectors as, obviously, people who know a lot of people, but more importantly, people who can connect different worlds and spot things in one world that can be applied in another.

Or as Gladwell himself said, "connectors are people who link us up with the world. People with a special gift for bringing the world together."

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AI Adoption: Do the Benefits Outweigh the Challenges?

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After a decade of stop-and-go development, Artificial Intelligence has now begun to provide real, tangible value to the business world. McKinsey published an 80-page report titled "Artificial Intelligence: The Next Digital Frontier?" which provides a comprehensive analysis of the value that Artificial Intelligence (AI) creates for businesses.

The report points out that "wide application of Artificial Intelligence technology will bring great returns to businesses." This means that the disruptive nature of AI will continue to become more apparent in the future. Governments, enterprises, and developers should all be clear on this point. Moreover, the report raises some interesting points (all of which we will discuss later in this article):


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Will Chatbots Overtake Mobile Apps as a Medium of Client Engagement?

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For digital marketing enthusiasts, the theory that chatbots will probably overtake mobile apps soon is no shock. It is expected that chatbots will replace mobile apps as a medium of client engagement by 2020. According to a Gartner’s report, 85% of the interactions that happen between companies and their customers will soon be happening without any human intervention.

A chatbot, as we know, is a software program that conducts a conversation through a voice or text medium. Chatbots are a reasonably new phenomenon. Since 2016, chatbots started surfacing as a method of handling customer interactions in an automated manner. As different domains within any organization see the implementation of various automation tools, client servicing, and support has not been left behind either.


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What Errors Lurk in Infer.NET Code?

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Posting of projects sources by Microsoft is a good reason to perform their analysis. This time is no exception, and today, we will look at suspicious places found in Infer.NET code.

Briefly About the Project and the Analyzer

Infer.NET is a Machine Learning system developed by Microsoft specialists. Project source code has become recently available on GitHub, which gave rise to its check. More details about the project can be found here.


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Comparing bfloat16 Range and Precision to Other 16-bit Numbers

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Deep Learning has spurred interest in novel floating point formats. Algorithms often don’t need as much precision as standard IEEE-754 doubles or even single precision floats. Lower precision makes it possible to hold more numbers in memory, reducing the time spent swapping numbers in and out of memory. Since this where a lot of time goes, low precision formats can speed things up quite a bit.

Here I want to look at bfloat16, or BF16 for short, and compare it to 16-bit number formats I’ve written about previously, IEEE and posit.


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Drowsy Detection Using Facial Landmarks Extraction and Deep Neural Networks

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Introduction

The goal of this article is to explain how you can detect a drowsy person using facial landmarks as an input of a neural network, a 3D convolutional neural network, in this case, to sound an alarm to awake the user and to prevent some kind of accident.

The idea is to extract a group of frames from a webcam and then extract from them the facial landmarks, specifically the position of both eyes, then pass these coordinates to the neural model to get a final classification which will tell us if the user is awake, or falling sleep.


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