9NOVEMBER 2020Neural networks use supervised learning and were originally developed to emulate the human brain, which uses an extremely large network of neurons to process information. A simple neural network consists of a single layer of neurons that connects input data to output data.Deep learning systems are neural networks with many layers, and the learning performed by them is called "deep learning."What are some successful applications of ML?Now that you have some familiarity with ML and AI, let us look at a few examples of successful applications of these technologies:· Customer profile analysis to understand and retain the most loyal customers,as well as target new customers· Fraud and anomaly detection using clustering techniques· Medical diagnosis, self-driving vehicles, and facial recognition using deep learning· Recommender systems using reinforcement learning What are some best practices for implementing ML systems?· A successful implementation of an ML system requires a common understanding of the objectives between business and technology teams. There must be an agreed upon set of metrics to evaluate and measure the performance of the system. · Special attention should be paid if the ML systems are expected to provide an explanation of their decision-making process. Typically, ML systems are not very good at providing this detail. Experience suggests that in such cases, it is good to include a human expert during the overall process so that the ML system plays a support role in identifying best possible options, but the human experts make the final decision. · There must be proper guidelines for when ML systems should be retrained with new data. Depending on the domain, the training data used for building a ML system may not continue to be relevant after a period of time due to changes in other external factors. Therefore, it is important to have well-defined criteria for retraining the system with new data. This can be done as frequently as daily (e.g., setting price values) or after specific events (e.g., when new products or versions are introduced into the market). It is important to ensure appropriate support from the underlying IT and governance processes.There are also potential organizational challenges worth watching:· A common challenge that many enterprises face is lack of a cohesive leadership to drive ML and AI practices. Often, you find too many leaders racing against each other. It is important to have an AI strategy at an organization level that is aligned around enterprise data strategy, information security, governance, and compliance requirements.· The next challenge is selecting the right use-case to implement. A few ideas to explore include looking for areas with high revenue but low efficiency, or business processes experiencing common errors. Additional factors to consider include availability of relevant data, business champions, and willingness to learn and adapt.· The demand for AI talent will always be high compared to the availability of skilled resources within an organization. Consider bringing in outside talent by partnering with schools or universities, developing training classes and courses, and encouraging on-the-job training.Today, there is a lot of buzz around ML and AI, and many C-level executives across many industries are very interested in building out a successful practice for these technologies. If you are just beginning your ML and AI journey, don't fret, you're not alone. Many of your colleagues are also just entering this burgeoning field. As you move forward with introducing ML and AI to your organization, consider starting small, identifying and building a few use-cases, and learning and growing your technologies from there. Srinivas Krovvidy
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