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.”

Before we go to the list do you know what makes software skills most sought-after and hence more valuable than most of the other job skills? It is simply because unlike any other skill, software skills are global and are not at all location specific! With the evolution and penetration of internet technologies, the physical distance between the client and service provider hardly matters. So, with more advancement in technology, it is indeed going to rain opportunities on the right skilled developer. I’ll take the liberty to reproduce the following quotes here to prove my claims statistically:

Demand for “cloud-ready” IT workers will grow by 26 percent annually through 2015, with as many as 7 million cloud-related jobs available worldwide.

---IDC White Paper (November 2012).

In the United States, the IT sector is experiencing modest growth of IT jobs in general, with the average growth in IT employment between 1.1 and 2.7 percent per year through 2020.

---U.S. Bureau of Labor Statistics.

It is said that spoken languages shape thoughts by their inclusion and exclusion of concepts, and by structuring them in different ways. Similarly, programming languages shape solutions by making some tasks easier and others less aesthetic. Using F# instead of C# reshapes software projects in ways that prefer certain development styles and outcomes, changing what is possible and how it is achieved.

F# is a functional language from Microsoft's research division. While once relegated to the land of impractical academia, the principles espoused by functional programming are beginning to garner mainstream appeal.

As its name implies, functions are first-class citizens in functional programming. Blocks of code can be stored in variables, passed to other functions, and infinitely composed into higher-order functions, encouraging cleaner abstractions and easier testing. While it has long been possible to store and pass code, F#'s clean syntax for higher-order functions encourages them as a solution to any problem seeking an abstraction.

F# also encourages immutability. Instead of maintaining state in variables, functional programming with F# models programs as a series of functions converting inputs to outputs. While this introduces complications for those used to imperative styles, the benefits of immutability mesh well with many current developments best practices.

For instance, if functions are pure, handling only immutable data and exhibiting no side effects, then testing is vastly simplified. It is very easy to test that a specific block of code always returns the same value given the same inputs, and by modeling code as a series of immutable functions, it becomes possible to gain a deep and highly precise set of guarantees that software will behave exactly as written.

Further, if execution flow is exclusively a matter of routing function inputs to outputs, then concurrency is vastly simplified. By shifting away from mutable state to immutable functions, the need for locks and semaphores is vastly reduced if not entirely eliminated, and multi-processor development is almost effortless in many cases.

Type inference is another powerful feature of many functional languages. It is often unnecessary to specify argument and return types, since any modern compiler can infer them automatically. F# brings this feature to most areas of the language, making F# feel less like a statically-typed language and more like Ruby or Python. F# also eliminates noise like braces, explicit returns, and other bits of ceremony that make languages feel cumbersome.

Functional programming with F# makes it possible to write concise, easily testable code that is simpler to parallelize and reason about. However, strict functional styles often require imperative developers to learn new ways of thinking that are not as intuitive. Fortunately, F# makes it possible to incrementally change habits over time. Thanks to its hybrid object-oriented and functional nature, and its clean interoperability with the .net platform, F# developers can gradually shift to a more functional mindset while still using the algorithms and libraries with which they are most familiar.

Related F# Resources:

F# Programming Essentials Training

We are not all equally motivated. Some people have more self-drive than other people. This is why we find that some people always end up at the top even when the odds are against them. An employee, with this realization, through the Human Resource department, should be able to design efficient career development systems. For this system to work, the employer must understand the nature of the business environment in which they are operating.

Why Train Employees?

The purpose of training employees is to enable them to grow with time and increase their efficiency. The business world is quite dynamic, nothing stays the same for long. Training one’s employees allows them to keep abreast with the ever changing technological advancements and many other factors that are relevant to his/her line of work. Employees cannot be expected to solve all their employer’s expectations with static skills and techniques. Even the most updated technology becomes obsolete at some point.
People are the biggest assets in organizations. For an establishment to flourish, it is important that the employer understands certain key things that help spur their development.

In a report from the Harvard Business Review, “The Impact of Employee Engagement of Performance,” the most impactful employee drivers are: