WHY IT MATTERS: Digital Transformation

If only everything worked like this! Why are we still sending files around via email? Why aren’t there multiple branching versions of everything? Why do we pretend that there’s any canonical version of anything? (Because we have to make money.) Git acknowledges a long-held, shared, and hard-to-express truth, which is that the world is ever-shifting and nothing is ever finished.

Finally, though, the biggest surprise with this project was just how long it took - and related, the amount of compute resources it needed. The first surprise was in terms of calendar time. My original estimate was that as a side project it would take about 3 months. It actually took around 8 months. (And the original estimate was supposed to be pessimistic!) Some of that was down to underestimating how many hours each stage would take, but a big chunk of the underestimate was failing to anticipate other things coming up outside the project. It’s hard to say how well this generalises, but for side projects, taking your original (already pessimistic) time estimates and doubling them might not be a bad rule-of-thumb.

Some IT leaders are growing weary of the phrase “digital transformation.” Find here 10 articles on the subject to help grasp some wisdom:

1. How Landauer CIO built a revenue-generating mindset in IT

2. SunTrust CIO's formula for speed relies on cloud, DevOps

3. Increasing IT’s speed: 3 levers CIOs can use

4. Teaching IT to embrace change: 9 execs share advice

5. Matson CIO: The most painful, gut-wrenching part of leading transformation

6. How hybrid cloud aids digital transformation

7. Budgeting smarts for digital transformation: 4 tips

8. Digital transformation: 3 strategies to manage change

9. Digital transformation: 5 principles for the retail industry

10. What absolutely won’t work when it comes to digital transformation

Blockchain technology is hot right now! I've devised a 2 month study plan to help you learn how blockchains and cryptocurrency works. This is the kind of curriculum I'd create for myself to learn, but I'm open sourcing it for you guys since I love you. We'll start with cryptography, move on to Bitcoin, then Ethereum, other cryptos, and finally Decentralized Applications.

Curriculum for this video: https://github.com/llSourcell/Learn_Blockchain_in_2_months 

Apache PredictionIO is an open source Machine Learning Server built on top of a state-of-the-art open source stack for developers and data scientists to create predictive engines for any machine learning task.

For most companies, agile is confined to software development—but it doesn’t need to be. Increasingly, forward-thinking companies are taking advantage of the same agile techniques that have transformed software development. Now they are successfully deploying these techniques in other core business units, from marketing to human resources to finance. When companies implement agile across their entire organizations, ways of working improve dramatically. Agile methods are more collaborative and creative and can be more efficient than other business models. But companies must first understand why their current business structures need to change.

Digital transformation - extending and exposing business processes outside the organisation - by implementing a digital strategy – a statement about the organisation’s digital positioning, operating model, competitors and customer and collaborator needs and behaviour through the delivery of digital solutions defined in a digital architecture – a future state application, data and technology view to achieve digital operating status - is potentially (very) complex. Digital architecture does not exist in isolation entirely separate from an organisation’s overall enterprise architecture. Digital architecture must exist within the within the wider enterprise architecture context. Enterprise architecture provides the tools and the approaches to manage the complexity of digital transformation. 

This guide is for anyone who is curious about machine learning but has no idea where to start. I imagine there are a lot of people who tried reading the wikipedia article, got frustrated and gave up wishing someone would just give them a high-level explanation. That’s what this is. The goal is be accessible to anyone — which means that there’s a lot of generalizations. But who cares? If this gets anyone more interested in ML, then mission accomplished.

This technology is called face recognition. Facebook’s algorithms are able to recognize your friends’ faces after they have been tagged only a few times. It’s pretty amazing technology — Facebook can recognize faces with 98% accuracy which is pretty much as good as humans can do!

Amazon has casually unveiled what could turn into a fundamentally different way to build software.

In late August we rolled out our Growth Framework, a set of documents and tools that described what we value at Medium, how to progress, and how we measure and reward that progress.

We want to create a better programming experience for learning and teaching programming. This paper describes Seymour, a live programming environment that features two visualizations of a program’s execution. One shows the execution’s details and another depicts it at a high level. These two visualizations come together to create a compelling user experience that we plan to test and improve with student feedback.

Google Data Center FAQ & Locations. Google data centers are the object of great fascination, and the intrigue about these facilities is only deepened by Google's secrecy about its operations. We've written a lot about Google's facilities, and thought it would be useful to summarize key information in a series of Frequently Asked Questions.

Creating and maintaining a platform for reliably producing and deploying machine learning models requires careful orchestration of many components—-a learner for generating models based on training data, modules for analyzing and validating both data as well as models, and finally infrastructure for serving models in production. This becomes particularly challenging when data changes over time and fresh models need to be produced continuously. Unfortunately, such orchestration is often done ad hoc using glue code and custom scripts developed by individual teams for specific use cases, leading to duplicated effort and fragile systems with high technical debt. We present the anatomy of a general-purpose machine learning platform and one implementation of such a platform at Google. By integrating the aforementioned components into one platform, we were able to standardize the components, simplify the platform configuration, and reduce the time to production from the order of months to weeks, while providing platform stability that minimizes service disruptions. We present the case study of one deployment of the platform in the Google Play app store, where the machine learning models are refreshed continuously as new data arrive. Deploying the platform led to reduced custom code, faster experiment cycles, and a 2% increase in app installs resulting from improved data and model analysis.

The next step is to ensure everyone at BCG understands this new model, not just IT. "We have some smart people across the company who will ask someone they know in some startup to develop a new app for them," Kuppens says. "We tell them, 'Fine, but make sure to do the 3x3. Is it secure and serviceable? Will it break our architecture? What is the user experience? Can we run this on AWS or Azure? What is the expected uptime?'" If the new application or data business case passes the basic 3x3 demand-clearing assessment, the business can move forward with IT’s oversight.

Technical debt is something that every technology company, large or small, public or private, has to deal with. Technical debt accumulates in a dizzying variety of ways as you build software. Sometimes it accrues through explicit, individual compromise: you deliberately choose to do something in a non-sustainable way in order to get something to market more quickly and tell yourself that you'll clean things up later. Sometimes technical debt accrues because building technology is hard, people make mistakes, and even the cleverest engineers can't predict the future which means that sometimes you build the wrong things. And sometimes technical debt accrues because your teams think mostly about how to ship stuff more quickly, not about the messes that they are making in service of their haste. Individually, it can be difficult to explain why you need to take the extra time to build something "the right way" when what seems like the whole world is anxiously waiting for you to be done.

All Stacklist 100+ Launch Tools��

API-Security-Checklist - A checklist of the most important security countermeasures when designing, testing, and releasing your API.

Developers have built more than 15,000 skills with the Alexa Skills Kit. Explore the stories behind some of these innovations, then start building your own skill. If you’re serious about getting into building voice UIs, we’d like to help you explore. 

Since launching Magic Pocket last year, we’ve been storing and serving more than 90 percent of our users’ data on our own custom-built infrastructure, which has helped us to be more efficient and improved performance for our users globally.

Motivation Imagine you’re an engineer working on a new product feature that is going to have a high impact on the end user, like the Dropbox Badge. You want to get quick validation on the functionality and utility of the feature.

OVER a couple of days in February, hundreds of thousands of point-of-sale printers in restaurants around the world began behaving strangely. Some churned out bizarre pictures of computers and giant robots signed, “with love from the hacker God himself”. Some informed their owners that, “YOUR PRINTER HAS BEEN PWND’D”.