Machine learning is a subset of artificial intelligence. The simplest way to describe it is that the solution incorporates the use of smart algorithms that analyze data, identify patterns, learn and then make decisions with minimal intervention. Incorporating machine learning into existing applications or building new ones can have far-reaching impacts in various segments.
Machine learning development is more or less like agile software development but there are differences. The purpose is predefined by the client working with the machine learning solutions provider to arrive at a concept stage with flexible options based on testing outcomes. It is an iterative process in which adjustments need to be made as one goes along. High-quality training data is at the core because machine learning relies on clean, unambiguous sufficient data. Even then, the trial outcome may differ when it comes to real world data so adjustments are common to the machine learning development process. Too much data can confuse and too little can generate wrong results. Experimentation is part of the process and outcomes can help refine final algorithms.
A typical Machine learning development company will have design engineers, artificial intelligence experts and data scientists working as a team on perfecting a solution. They may use various methods such as gradient boosting trees algorithm or support vector machine algorithm, compare accuracy and balance the error rate. Errors can arise due to a functional structure, not defining right parameters for the machine learning model and insufficient data. Optimization is carried out by frequent tweaking back and by knowing precise desired outcomes for the customer and his area of operation. Even when a model is finalized, real world testing with real data can throw up some surprises that need to be addressed so choosing experts in machine learning is essential. The company must have data preparation capability, expertise in algorithms, modeling, automation and iterative processes and scaling as well as security. Development in machine learning needs to be highly tailored to suit various segments.
– Financial services need machine learning to identify insights from data and to prevent fraud. Stockbrokers can employ it to help identify investment opportunities or for banks to identify high-risk clients.
– Healthcare can leverage machine learning along with wearable devices to assess patient’s health and improve diagnosis.
– Marketing and sales benefit in that websites can recognize patterns of buyers and personalize shopping experiences.
– The government can use machine learning solutions to derive insights, improve efficiency, prevent fraud and identity theft and deliver better services.
– Oil & gas and manufacturing segments benefit from using machine learning to identify faults before they can develop and cause breakdowns in production and to monitor sensors.
– Transportation benefits in that data analysis and modeling in machine learning can help better utilization of resources and plan routes.
Each segment needs a custom approach and would benefit from retaining specialists in machine learning for a tailored solution.
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