Since the last few years, the technology industry sees drastic changes in operations, Artificial intelligence and automation are the key focus of many organizations these days. Top-level meetings are full of jargon like Machine learning, Deep Learning, and automated solutions. With these changes in the trend of the market, it is also important to understand and absorb that research-based solutions required a different sort of management typically. From top to the bottom level of the hierarchy there’s a need to realize that their previous assumptions with technology products are up for a test. Some significant methodological adaptations involved while managing artificial intelligence-based solutions that I am discussing here,

Understand the probabilistic aspect of solutions, so here’s the first major hard block. Machine learning is not engineering either its solutions conclude that way, Machine Learning always and always works on some probability, that sometimes are calculated in terms of Precision and Recalls of the output. There are many different ways to calculate the accuracy of machine learning solutions like confusion matrix, F1 measure, G measure (Fowlkes–Mallows), Matthews correlation coefficient, etc. It’s important to note here that if your solution is giving 80% accuracy that does not means it is only going to act on 80% of inputs and ignore rest 20%. But since real-world applications required outcome for each input, you may want to code for fallbacks for inputs. Now that’s visible to code for fallbacks, what’s important here is the accuracy measured for a particular class of data, you will have to make sure that this accuracy of 80% covers maximum tonnage of your inputs.

We all understand the fact that Machine learning is not software engineering, the team of Machine learning experts or some preferred to be called Data scientist is also different in many aspects. Remember there’s no fix programming language for Machine learning some algorithms work better when coded in python, some work better if coded in other programming languages like R or Scala. So unlike software engineering, you should not want to restrict your research development to a specific programming language. The outcome of your machine learning research development can be a combination of R scripts, Python notebooks or some other script files it then needed to be code with the help software engineers to complete the software application.

Since machine learning is newer when it comes to available helping softwares, and during the development period experts need to analyze an enormous amount of data (sometimes in terabytes), it tends to take more time than traditional software deliveries. It’s often considered non-deterministic when you try to finalize its delivery timelines. So you may want to plan your deliverables to other stack holders keeping this fact in mind.

Another aspect that needs to factorize is, there’s no fixed architecture or methodology a machine learning development would follow, it depends on the statistical compatibility of the data under analysis. So as you uncover the information layers of the data that are being analyzed, you discover the possible approach for the development. The step by step approach and validations on each phase could be a possible approach that can be acquired when you are managing timelines for such application development. Since this is important for the business to gain confidence in the Application, delivery of a working proof of concept could be beneficial.

In the current scenario, not all problems can be solved by Machine learning efficiently. But the field is so promising, most of the marketing experts convince their clients that Machine learning is some sort of magic of mathematics that can do all the stuff which is being done by humans. In theory that seems possible but practically machine learning needs to mature to that level of confidence. There are many road blocks which tend to divert the complete flow of the development. First or for most important aspect is the structure of the data that’s under analysis for the problem statement. In my experience, data that are being used by many experts during the algorithms evaluations is of excellent quality, or easy to draw statistic inferences, but while solving real-world problems, the actual information could be hidden under the unrealistic layers of statistical inferences. Since the information in available data is a base of any machine learning algorithm it’s highly important you capture meaningful data for your analysis.

At last, we all know Artificial intelligence technologies have enormous potentials that cannot be easily quantified. The technology leaders supporting business with their top-notch understanding of software engineering would need to support the very nature of such particular solutions. In some cases, managers might need to be vigilant about the idiosyncrasy of business while adapting such technologies. Since artificial intelligence is just started its journey with business, the technical managers leading the effort to solve problems need to embrace the caveats of this pioneer in computer science technologies.