Solving business problems with Machine Learning
Machine Learning and Artificial Intelligence have gained prominence in the recent years with Google, Microsoft Azure and Amazon coming up with their Cloud Machine Learning platforms. But surprisingly we have been experiencing machine learning without knowing it. The most primary use cases are Image tagging by Facebook and ‘Spam’ detection by email providers. Now Facebook automatically tags uploaded images using face (image) recognition technique and Gmail recognizes the pattern or selected words to filter spam messages. Let’s take a look at some of the important business problems solved by machine learning.
Problems solved by Machine Learning
1. Manual data entry
Inaccuracy and duplication of data are major business problems for an organization wanting to automate its processes. Machines learning (ML) algorithms and predictive modelling algorithms can significantly improve the situation. ML programs use the discovered data to improve the process as more calculations are made. Thus machines can learn to perform time-intensive documentation and data entry tasks. Also, knowledge workers can now spend more time on higher-value problem-solving tasks. Arria, an AI based firm has developed a natural language processing technology which scans texts and determines the relationship between concepts to write reports.
2. Detecting Spam
Spam detection is the earliest problem solved by ML. Four years ago, email service providers used pre-existing rule-based techniques to remove spam. But now the spam filters create new rules themselves using ML. Thanks to ‘neural networks’ in its spam filters, Google now boasts of 0.1 percent of spam rate. Brain-like “neural networks” in its spam filters can learn to recognize junk mail and phishing messages by analyzing rules across an enormous collection of computers. In addition to spam detection, social media websites are using ML as a way to identify and filter abuse.
3. Product recommendation
Unsupervised learning enables a product based recommendation system. Given a purchase history for a customer and a large inventory of products, ML models can identify those products in which that customer will be interested and likely to purchase. The algorithm identifies hidden pattern among items and focuses on grouping similar products into clusters. A model of this decision process would allow a program to make recommendations to a customer and motivate product purchases. E-Commerce businesses such as Amazon has this capability. Unsupervised learning along with location detail is used by Facebook to recommend users to connect with others users.
4. Predictive maintenance
Manufacturing industry can use artificial intelligence (AI) and ML to discover meaningful patterns in factory data. Corrective and preventive maintenance practices are costly and inefficient. Whereas predictive maintenance minimizes the risk of unexpected failures and reduces the amount of unnecessary preventive maintenance activities.
For predictive maintenance, ML architecture can be built which consists of historical device data, flexible analysis environment, workflow visualization tool and operations feedback loop. Azure ML platform provides an example of simulated aircraft engine run-to-failure events to demonstrate the predictive maintenance modeling process. The asset is assumed to have a progressing degradation pattern. This pattern is reflected in asset’s sensor measurement. In order to predict future failures, ML algorithm learns the relationship between sensor value and changes in sensor values to historical failures.
Read the full and updated story about few more business use cases of Machine Learning, which includes—
✓ Medical Diagnosis
✓ Customer segmentation and Lifetime value prediction
✓ Financial analysis
✓ Image recognition (Computer Vision)
8 problems that can be easily solved by Machine Learning
Machine Learning and Artificial Intelligence have gained prominence in the recent years with Google, Microsoft Azure…
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