Machine Learning: intelligent experts at the service of the Company. His exponential increase in data and in the computing power available over the last decade. Machine learning involves allowing a computer to “learn” to predict certain variables. The two factors mentioned have made possible the applications providing useful insights to the business, thanks to the data accumulated by the company, at a cost and a reasonable calculation time.
Machine learning is a field of study for artificial intelligence. But you don’t need to want to develop an autonomous car.With huge resources to take advantage of it today, both in SMEs and in large companies. Under the right conditions, it makes it possible to solve insoluble complex problems. By programming “by hand” and this almost instantly. Based on the exploitation of data. It is based on and complements, classic statistical analysis and modeling of data analysis.
Why are you interested in Machine Learning?
Large group or SME, your company certainly has to gain by being interested. In the possibilities offered today by the application of machine learning. Whatever the company, there always comes a time when the complexity of the problems encountered. And the multiplicity of factors that impact them make it impossible. To discern in a simple way the optimal answer. At the same time, consciously or not. Any company accumulates customer, technical, commercial, marketing or other data. Which can make it possible to rationalize, support or further develop. The competence of the company’s trades. By completing the processes resulting from the experience or common sense.
The promise made by Maven Digital Top machine learning development Company in Dubai is to provide a solution to complex problems faster, more accurately, and more scalable than could be programmed manually. It allows us to prioritize among this multitude of factors. Which are the most important and to predict the result. Of the interaction of these factors on the output that interests us. Once validated and implemented. It suffices to present new data to the “machine learning” tool to obtain the prediction. And therefore the decision, automatically.
How to build a solution based on Machine Learning?
In the context of relatively light applications, two main types of algorithms. Associated with two different prerequisites in terms of the available data. Deep learning, mainly based on neural networks. Where the computer “learns itself to learn”. Here we are dealing with artificial intelligence itself. As necessary for the autonomous car or robotic vision. For the first two types presented. Which are the only ones of practical scope at present for most companies. Obviously data is needed from which the machine will “learn”. According to the very vivid expression. “garbage in, garbage out” (coming into waste, waste output). The quality of data provided will be crucial to determine the quality of the predictions made.
The interaction with the business stakeholders
In this step, the interaction with the business stakeholders will be essential to capitalize. On their knowledge of the parameters of importance in the context of the problem. We will only be able to predict an output if the input variables allow us to estimate it. In the most frequent case, that of supervised learning. A minimum retreat will be necessary in order to have observed a reasonable number of the different. Possible output values to be supplied to the algorithm. Indeed, the learning will be done by the machine by finding the best set of parameters to predict. These observed and known outputs from the values of the associated input variables. In the case of unsupervised learning.
Most often by “clustering” within the framework of a search for segmentation or structures of similarities. There is no known output and the algorithm will itself choose this. New data are defined as when the parameters allowing the model to obtain the desired precision. It is then possible to deploy it and make it accessible to all those likely to use it in the company. The role of IT resources is preponderant in this step. Finally, in the time that follows, since decisions should be taken in relation to the use of the “machine learning” tool, the data is likely to change.
How to recognize a winning Machine Learning application opportunity?
First of all, here is an illustration of the range of concrete applications of machine learning applied by a leader in data mining. Machine Learning Example Given their flow of data, these machine-learning also rely on Big Data. But big data is by no means a necessity to take advantage of machine learning, improve decision-making and accumulate gains, through the levers of a high frequency of use or the time factor. These methods are now accessible and are those which ensured the success of the pioneers.
Appearance of new insights
The appearance of new insights, optimized decision-making and the time savings associated with machine-learning allow, beyond the immediate gains, to promote innovation by freeing up time for your executives. They can devote more to the development of new services, possibly in the directions indicated by these new tools. Among the ingredients of a successful implementation. We will of course have to be interested in the questions for. Which we can expect to have quality data, in sufficient quantity to set up a powerful “machine learning” tool. Beyond these preconditions, it will be a question of estimating the expected gains if the company had the tool with the desired specifications.
For example, what is the gain compared to the current operation. If the sales teams had every Monday morning, to prepare their phoning. Of the customer listing, sorted by decreasing probability of termination and also estimating the expected “lifelong value” for this customer if we weren’t losing it in the short term? This type of question, associated with the. Technical feasibility of the tool to be implemented in each case. Provides a first overview of the opportunities and the expected cost / benefit ratios.
However, Machine Learning: intelligent experts at the service of the Company. Once in place, the tool must also win the support of end users, at the business level, so that their appropriation of the tool brings real added value. Before the deployment phase, it will be important to have established a dialogue both with these end users, in order to define the ergonomics of the tool so that it is an additional comfort and not a hindrance to its use, and with IT departments if they are the ones who will have to carry out the implementation.
They will then be the guarantors of optimal use of the tool and the full realization of its potential value. In an ever-growing ocean of data, collected by consumer or via connected objects, predictive machine-learning analysis provides the navigation tools essential for companies to reach their destination safely. With the glimpse of the future offered, it becomes possible to respond in a more relevant, safer, more efficient and cost-effective manner to future challenges.