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.”

Writing Python in Java syntax is possible with a semi-automatic tool. Programming code translation tools pick up about 75% of dynamically typed language. Conversion of Python to a statically typed language like Java requires some manual translation. The modern Java IDE can be used to infer local variable type definitions for each class attribute and local variable.


Translation of Syntax
Both Python and Java are OO imperative languages with sizable syntax constructs. Python is larger, and more competent for functional programming concepts. Using the source translator tool, parsing of the original Python source language will allow for construction of an Abstract Source Tree (AST), followed by conversion of the AST to Java.

Python will parse itself. This capability is exhibited in the ast module, which includes skeleton classes. The latter can be expanded to parse and source each node of an AST. Extension of the ast.NodeVisitor class enables python syntax constructs to be customized using translate.py and parser.py coding structure.

The Concrete Syntax Tree (CST) for Java is based on visit to the AST. Java string templates can be output at AST nodes with visitor.py code. Comment blocks are not retained by the Python ast Parser. Conversion of Python to multi-line string constructs with the translator reduces time to script.


Scripting Python Type Inference in Java
Programmers using Python source know that the language does not contain type information. The fact that Python is a dynamic type language means object type is determined at run time. Python is also not enforced at compile time, as the source is not specified. Runtime type information of an object can be determined by inspecting the __class__.__name__ attribute.

Python’s inspect module is used for constructing profilers and debugging.
Implementation of def traceit (frame, event, arg) method in Python, and connecting it to the interpreter with sys.settrace (traceit) allows for integration of multiple events during application runtime.

Method call events prompt inspect and indexing of runtime type. Inspection of all method arguments can be conducted. By running the application profiler and exercising the code, captured trace files for each source file can be modified with the translator. Generating method syntax can be done with the translator by search and addition of type information. Results in set or returned variables disseminate the dynamic code in static taxonomy.

The final step in the Python to Java scrip integration is to administer unsupported concepts such as value object creation. There is also the task of porting library client code, for reproduction in Java equivalents. Java API stubs can be created to account for Python APIs. Once converted to Java the final clean-up of the script is far easier.

Related:

 What Are The 10 Most Famous Software Programs Written in Python?

Python, a Zen Poem

Being treated like a twelve year old at work by a Tasmanian-devil-manager and not sure what to do about it? It is simply a well-known fact that no one likes to be micro managed. Not only do they not like to be micro managed, but tend to quit for this very reason. Unfortunately the percentage of people leaving their jobs for this reason is higher that you would imagine. Recently, an employee retention report conducted by TINYpulse, an employee engagement firm, surveyed 400 full-time U.S. employees concluded that, "supervisors can make or break employee retention."

As companies mature, their ability to manage can be significant to their bottom line as employee morale, high staff turnover and the cost of training new employees can easily reduce productivity and consequently client satisfaction.  In many cases, there is a thin line between effective managing and micro managing practices. Most managers avoid micro managing their employees. However, a decent percentage of them have yet to find effective ways to get the most of their co-workers.  They trap themselves by disempowering people's ability to do their work when they hover over them and create an unpleasant working environment. This behavior may come in the form of incessant emailing, everything having to be done a certain way (their way), desk hovering, and a need to control every part of an enterprise, no matter how small.

Superimpose the micro manager into the popular practice of Agile-SCRUM methodology and you can imagine the creative ways they can monitor everything in a team, situation, or place. Although, not always a bad thing, excessive control, can lead to burnout of managers and teams alike.  As predicted, agile project management has become increasingly popular in the last couple of decades in project planning, particularly in software development.  Agile methodology when put into practice, especially in IT, can mean releasing faster functional software than with the traditional development methods. When done right, it enables users to get some of the business benefits of the new software faster as well as enabling the software team to get rapid feedback on the software's scope and direction.

Despite its advantages, most organizations have not been able to go “all agile” at once. Rather, some experiment with their own interpretation of agile when transitioning.  A purist approach for instance, can lead to an unnecessarily high agile project failure, especially for those that rely on tight controls, rigid structures and cost-benefit analysis.  As an example, a premature and rather rapid replacement of traditional development without fully understating the implications of the changeover process or job roles within the project results in failure for many organizations.