Let’s face it, fad or not, companies are starting to ask themselves how they could possibly use machine learning and AI technologies in their organization. Many are being lured by the promise of profits by discovering winning patterns with algorithms that will enable solid predictions… The reality is that most technology and business professionals do not have sufficient understanding of how machine learning works and where it can be applied.  For a lot of firms, the focus still tends to be on small-scale changes instead of focusing on what really matters…tackling their approach to machine learning.

In the recent Wall Street Journal article, Machine Learning at Scale Remains Elusive for Many Firms, Steven Norton captures interesting comments from the industry’s data science experts. In the article, he quotes panelists from the MIT Digital Economy Conference in NYC, on businesses current practices with AI and machine learning. All agree on the fact that, for all the talk of Machine Learning and AI’s potential in the enterprise, many firms aren’t yet equipped to take advantage of it fully.

Panelist,  Michael Chui, partner at McKinsey Global Institute states that “If a company just mechanically says OK, I’ll automate this little activity here and this little activity there, rather than re-thinking the entire process and how it can be enabled by technology, they usually get very little value out of it. “Few companies have deployed these technologies in a core business process or at scale.”

Panelist, Hilary Mason, general manager at Cloudera Inc., had this to say, “With very few exceptions, every company we work with wants to start with a cost-savings application of automation.” “Most organizations are not set up to do this well.”

Millions of people experienced the frustration and failures of the Obamacare website when it first launched. Because the code for the back end is not open source, the exact technicalities of the initial failings are tricky to determine. Many curious programmers and web designers have had time to examine the open source coding on the front end, however, leading to reasonable conclusions about the nature of the overall difficulties.

Lack of End to End Collaboration
The website was developed with multiple contractors for the front-end and back-end functions. The site also needed to be integrated with insurance companies, IRS servers, Homeland Security servers, and the Department of Veterans Affairs, all of whom had their own legacy systems. The large number of parties involved and the complex nature of the various components naturally complicated the testing and integration of each portion of the project.

The errors displayed, and occasionally the lack thereof, indicated an absence of coordination between the parties developing the separate components. A failed sign up attempt, for instance, often resulted in a page that displayed the header but had no content or failure message. A look at end user requests revealed that the database was unavailable. Clearly, the coding for the front end did not include errors for failures on the back end.

Bloat and the Abundance of Minor Issues
Obviously, numerous bugs were also an issue. The system required users to create passwords that included numbers, for example, but failed to disclose that on the form and in subsequent failure messages, leaving users baffled. In another issue, one of the pages intended to ask users to please wait or call instead, but the message and the phone information were accidentally commented out in the code.

While the front-end design has been cleared of blame for the most serious failures, bloat in the code did contribute to the early difficulties users experienced. The site design was heavy with Javascript and CSS files, and it was peppered with small coding errors that became particularly troublesome when users faced bottlenecks in traffic. Frequent typos throughout the code proved to be an additional embarrassment and were another indication of a troubled development process.

NoSQL Database
The NoSQL database is intended to allow for scalability and flexibility in the architecture of projects that will use it. This made NoSQL a logical choice for the health insurance exchange website. The newness of the technology, however, means personnel with expertise can be elusive. Database-related missteps were more likely the result of a lack of experienced administrators than with the technology itself. The choice of the NoSQL database was thus another complication in the development, but did not itself cause the failures.

Another factor of consequence is that the website was built with both agile and waterfall methodology elements. With agile methods for the front end and the waterfall methodology for the back end, streamlining was naturally going to suffer further difficulties. The disparate contractors, varied methods of software development, and an unrealistically short project time line all contributed to the coding failures of the website.

When making a strategic cloud decision, organizations can follow either one of two ideologies: open or closed.

In the past, major software technologies have been widely accepted because an emerging market leader simplified the initial adoption.  After a technology comes of age, the industry spawns open alternatives that provide choice and flexibility, and the result is an open alternative that quickly gains traction and most often outstrips the capabilities of its proprietary predecessor.

After an organization invests significantly in a technology, the complexity and effort required steering a given workload onto a new system or platform is, in most cases, significant. Switching outlays, shifting to updated or new software/hardware platforms, and the accompanying risks may lead to the ubiquitousness of large, monolithic and complex ERP systems – reason not being that they offer the best value for an organization, but rather because shifting to anything else is simply – unthinkable.

There’s no denying that these are critical considerations today since a substantial number of organizations are making their first jump into the cloud and making preparations for the upsetting shift in how IT is delivered to both internal and external clientele. Early adopters are aware of the fact that the innovation brought about by open technologies can bring dramatic change, and hence are realizing how crucial it is to be able to chart their own destiny.

Google is one of the most popular websites in the entire world that gets millions of views each day. Therefore, it should come as no surprise that it needs a strong and reliable programming language that it can rely on to run its searches and many of the apps that Google has created. Because of this, Google uses Python to ensure that every time a user uses one of their products, it will work smoothly and flawlessly. That being said, Google uses Python in a variety of different ways, outlined below.

Code.Google.Com
Since its creation, Google has always used Python as part of its core for programming language. This can still be seen today considering the strong relationship the two have with one another. Google supports and sponsors various Python events, and Python works to better itself so that Google remains on top of cutting edge material. One way that they do this is by working with code.google.com. This is the place where Google developers go to code, learn to code and test programs. And with it being built on Python, users can experience exactly what it is that they should expect once they start using the real site.

Google AdWords
Google AdWords is a great way for people to get their websites out there, through the use of advertising. Each time a person types in a certain string of keywords, or if they have history in their cookies, then they’ll come across these AdWords. The way that these AdWords are broadcasted to online web surfers is built on the foundation from Python. Python also helps clients access their AdWord accounts, so that they can tailor where they want their advertisements to go.

Beets
If you have loads of music, but some of it is uncategorized or sitting in a music player without a name or title, Beets is for you. This Google project uses Python and a music database to help arrange and organize music. The best part about Beets is that even if it doesn’t run exactly the way that you want, you can use a bit of Python knowledge to tailor it to be more specific to your desires.

Android-Scripting
Not only does Google run off Python, but Android also has its own value for the language. Whether you are someone who is just creating your own app for your phone or if you are someone who is looking to create the next app that gets downloaded multiple millions of times, you can use Python and Android-Scripting to create an app that does exactly what you want it to do.

YouTube
YouTube one just started as a video viewer on its own, but is now a billion-dollar company that is owned by Google. YouTube uses Python to let users view and upload video, share links, embed video and much more. Much like Google itself, YouTube relies heavily on Python to run seamlessly for the amount of traffic it gets daily.

Python is not your average coding language. Instead, it is a valuable and integral part of some of the biggest websites in the world, one of which is Google. And the resources listed here are just a fraction of what Google uses Python for in total.

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