Machine Learning
Machine learning can significantly help organizations to use application / device data efficiently to derive business benefits.Machine Learning can play a key role in impacting behavourial analytics, customer engagement and price optimization.
Globally organizations gather larger quantity of data from multiple channels. Cost of storing these data and analyzing them independently are significantly high. Enterprises have realized that the high scale of data means that machine learning is the only way to drive efficiencies through automation.
What is Machine Learning
Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can change when exposed to new data. The process of machine learning is similar to that of data mining. Both systems search through data to look for patterns. However, instead of extracting data for human comprehension -- as is the case in data mining applications -- machine learning uses that data to detect patterns in data and adjust program actions accordingly. Machine learning algorithms are often categorized as being supervised or unsupervized. Supervised algorithms can apply what has been learned in the past to new data. Unsupervised algorithms can draw inferences from datasets.Machine-learning algorithms are redefining the future of business applications toward a higher level of intelligence.
Facebook's News Feed uses machine learning to personalize each member's feed. If a member frequently stops scrolling in order to read or "like" a particular friend's posts, the News Feed will start to show more of that friend's activity earlier in the feed. Behind the scenes, the software is simply using statistical analysis and predictive analytics to identify patterns in the user's data and use to patterns to populate the News Feed. Should the member no longer stop to read, like or comment on the friend's posts, that new data will be included in the data set and the News Feed will adjust accordingly.
How does Machine Learning transform industries
Machine learning is likely to improve areas like web search, consumer financial services, fraud detection, predicting data center outages, recommending fixes, Healthcare and consumer engagement.Success of Machine learning depends on building efficient predictive analystical models that uses library of machine learning algorithms to analyze the data and provide the required predictive analytical statistics. For example., Using Apache Spark stream processing module in conjuction with MLib Algorithms can help in analyzing data from multiple sources and providing deeper and more accurate predictions.
Key trends in include:
• New investments in customer data platforms that improve customer engagement through recommendations
• Intelligent automation and conversational tools
• Increased use of machine learning in litigation
• Increased use in chat bots with machine learning capability by Mobile application providers
• Telecom operators can leverage larger volume of data that they collect to gain competitive advantage
• Storage service providers can leverage machine learing to reduce storage management overhead
• Cybersecurity
Machine Learning can play a effective role in healthcare. Customer, practioners, providers, claim, Clinical, Physiological, biometric data can be extracted from various sources and the analytics can be customized based on the consumers role. This will ensure the right set of analytical statistics are available to the departments to arrive at accurate and objective decisions.
Credit lending decisions systems traditionally use decision models along with reason codes which are not always accurate. Use of machine learning will help in arriving at accurate credit lending decisions.
Social media interactions of the consumer can be analyzed to predict the consumer buying behaviour.
Machine Learning Frameworks
We can consider using the following frameworks for building Machine learning capability in applications,
Frameworks for DeepLearning:
- TensorFlow (+Keras), MXNet, caffe, CNTK, Nervana Neon
Frameworks for generic machine learning:
- scikit-learn, Spark MLLib, mlpack, Accord.NET Framework, WEKA
Frameworks for cloud
- Amazone Machine Learning
- Google Cloud Machine Learning
- Azure ML Studio
- Nervana Cloud® The Platform for Deep Learning
Framework for streams:
- Apache Flink
Conclusion
Machine learning allows for purer data-driven solutions than conventional approaches. In the past, common-sense business rules were used in combination with more straightforward mathematical models to make decisions based on data. Heuristic rules derived from human experience can be a powerful addition to modeling logic, but they also come with the baggage of human bias and judgment errors.Machine learning models are able to shoulder more of the intellectual work that humans did in the past, allowing decisions to be made more directly from the data, and leading to potentially more accurate and objective conclusions.
