Machine learning use cases:
technology domains, industries & trends

1. Identify high-impact pain points
   Identify problems, inefficiencies, and areas for improvement, looking for business processes with high manual labor or data complexity.
2. Validate ML suitability
   Since ML implementation requires time, effort, and resources, make sure that an ML-based solution is more suitable than simple automation.
3. Review existing ideas
   Evaluate your ML solution ideas more closely in terms of implementation complexity, data availability and quality, and the value it can bring to your business. Then, assess whether you can scale up the solution in the future and what resources you would need for its maintenance. Next, assess the regulatory, risk, and transparency aspects of the potential use cases.
4. Assess your ML maturity
   To identify suitable ML use cases, assess your organization’s current readiness for ML implementation. Consider factors like the established degree of automation in processes and systems, the quantity and quality of data available for training ML models, and the availability of relevant talent like ML engineers and data scientists.
5. Prioritize use cases
   Single out specific tasks the ML solution will handle. This stage should be approached gradually, with multiple iterations to reconsider potential use cases based on the discovery of hidden implementation bottlenecks. For instance, you may realize that it’s worth implementing several more flexible ML open-source tools rather than a single custom solution.

Machine learning can be divided into three categories based on the approach used to train a ML model:

• Supervised learning
  The model is trained with labeled data, namely data points tagged with labels that describe what this data represents. For instance, a labeled dataset can contain thousands of emails tagged as “spam” or “not spam”. This helps the algorithm understand the relationship between input (emails) and output (spam/not spam) and apply such findings to spot future cases of email spam. Common supervised learning algorithms include CNNs (convolutional neural networks), kNN, random forest, support vector machines, and linear regression.
• Unsupervised learning
  The ML algorithm processes unlabeled data, autonomously identifies shared features or differences, and groups data points accordingly (e.g., customers clustered into segments based on similar purchasing patterns). K-Means clustering, PCA, autoencoders, and Apriori are popular unsupervised learning algorithms.
• Reinforcement learning
  Machine learning systems undergo multiple training iterations using a trial-and-error approach. In each iteration, the system attempts to perform a task (e.g., autonomous vehicles reaching a certain location), receiving rewards for correct actions (including avoiding obstacles) and penalties for poor performance (such as collisions). Key algorithms used in reinforcement learning are Q-learning, policy gradient, and SARSA.