How to Use Machine Learning in Mobile Apps

Partner Post - Konstant Infosolutions Top Mobile App Development Company

Posted: January 24, 2017

Summary: Machine Learning is being hailed as the future of technology but while it can bring about tremendous value addition to mobile application there could also be several ‘false gifts’. As a developer, you need to understand the differences to make a mark.

Machine learning, a subfield of Artificial intelligence, has the power to make your mobile applications more responsive and curated to the needs of your users. It’s about discovering the patterns buried deep within disparate data sets and tuning it to over-match human analysis and thinking. For mobile app developers, machine learning promises the potential of applying critical business analysis into applications and refining everything from hyper-personalized content to the customer experience. We already have cloud service providers like Microsoft, Google, and Amazon offering machine learning solutions with their apps.

Machine learning engages multidisciplinary applications and finds implementation in business, technology, and science. The broadest spectrum of opportunity is obviously robotics that is using more of cognitive technologies to build machines that understand humans, assist them in their chores and even entertain them.

Using program tools and voice commands, machine learned robots are just a few tap away from the smart phone to carry out a job. Another promising platform for machine learning is the field of data mining and link non-obvious but interesting connections with significant data sets. Machine learning both offers the tools and the algorithms to help the relationship.

Consequently, machine learning can be useful in predicting future trends, financial crashes and bubbles. Custom software built around the continual learning process can analyze all kinds of information, ranging from social media activity to credit rating and pop recommendations right into the user’s device.
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Currently, machine learning applications and tools are getting widely popular with e-commerce brands. Retailers like Amazon and eBay are using ML algorithms to manage several aspects of their business, including:

Product Search

Machine learning tools/facilities like query understanding, ranking, favorites and expansion can help users with more relevant information when they are searching for products. Behavioral data with searches can be used to create sub groups of goods to better match the intent of the user. Search history, semantic outcomes and a user’s portrait too can make a difference in their product search experience.

Product Recommendation

Recommendations are built around filtering methods, site content analysis, purchase patterns, user behavior and also the business logic, a brand implements. Recommendations using this will surely make the answers more relevant.

Forecasting Trends

Every e-commerce brand needs to continuously understand changing trends and react quickly with matching products and services. However, between past season sales and the upcoming trends, there lies a huge difference. Big Data and Machine learning can however aggregate these trends and use sales information from different sources (social media, digital reports, blogs, etc.) to make predictions in real-time.

Fraud Detection/Prevention

Estimates suggest that the e-commerce industry experiences frauds reaching the mark of $32 billion and the growth is a huge 38% every year. Machine learning can, however, help build defense systems that improve ongoing monitoring and trigger alarms. Using features like image recognition, shipping cost estimation, product tagging automation, wallet management, logistics optimization, business intelligence and more, brands can look into a more calculated future.

So, how should developers gear up for the machine learning technology?

  • The more the data mining, the more likely the accuracy of predictions and results. Consequently, machine learning should use all data available and not just sub-samples.
  • Since there exist several ML methods; the key to success with an app is finding the most appropriate one. The simpler it is, the easier and more accurate the results are.
  • Focus needs to be on the methods of data collection and an inappropriate process can hurt the end goal.
  • A specialized data scientist can be a huge addition to the project as he is likely to make more informed choices.
  • A better understanding of data features and updates will impact the predictability and the brand’s learning process.
  • Finally, machine learning algorithms too need to go through careful testing. Regardless of the market, you are working for; the quality of the algorithms defines the timing and costs of projects.

At this point in time, we are at the very beginning of a machine learning environment and to fully understand and unlock new potentials, it will definitely take some more time.

However, if you would like to get more insights on the topic, contact our technology experts.

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