Machine learning technologies enable organisations to gain rapid insights into the patterns which underlie ‘big data’. With such automated analytical models, it is possible to ensure that gainful patterns are derived from complex data sets which, in turn, enable companies to ‘mine’ profitable opportunities. These faster, more accurate results also facilitate the identification of substantial risks as well as important opportunities.
Machine Learning has since evolved from automated pattern recognition to machine learning algorithms which have vastly greater computational power. Such nascent technology is not only evolving swiftly but, with new deep learning based systems, data engineering and preprocessing, the costs of this new technology are rapidly falling to optimize the commercial advantage to a diverse range of clients.
Machine learning is presently deployed across a range of computing tasks where designing and programming explicit algorithms is otherwise unfeasible. Such examples of machine-based learning include Computational Biology, Drug Discovery & Design, Web Search and Recommendation Engines, Credit Fraud & Verification, Financial Forecasting, Text & Speech Recognition, Astronomy, Robotics, Social Networks, and Targeting Advertising.
Product sales Developing deeper insights about customers and sales prospects and revealing new opportunities for cross-selling and upselling among both current and prospective clients.
Customer retention Identifying those clients who are at risk of being lost and act quickly to retain their loyalty.
Collections Help companies to build robust dynamic models that are better able to segment delinquent borrowers, even identifying self-cure clients and proactively taking action to enhance their standing. This empowers companies to better tailor their accumulation strategies and to improve upon their on-time payment rates.
Treasury pricing Companies can capture 10 to 15 percent more revenue through optimized Treasury pricing. Advanced analytics can recognize quick-win opportunities to reduce price leakage and billing errors.
Customer care Improved digital customer experience, lower servicing costs, enhanced agent performance, more efficient capacity management, reduced risk, and elimination of waiting times.
TensorFlow Use cases
- Voice/Sound Recognition
- Mostly IoT, Security and UX/UI and Automotive uses Voice Recognition
- Telecoms – Handset Manufacturers uses voice search
- CRM industries uses Sentiment Analysis
- Flaw Detection or engine noise used in Automotive and Aviation
- Text-Based Applications
- We all know Google Translate, which supports over 100 languages translating from one to another. That evolved versions used for many cases like translating jargon legalese in contracts into plain language.
- Summarization of Text
- Image Recognition
- Time Series – TensorFlow Time Series algorithms used for analyzing time series data to extract meaningful statistics.
- Video Detection – TensorFlow neural networks also work on video data. And mainly employed in Motion Detection, Real-Time thread Detection in Gaming, Security, Airports and UX/UI fields
WEKA Use Cases
- Prototyping classifiers and filters before executing them in Java
- Resembling implementations in WEKA with applications from other languages
- Matching new loss functions using WEKA Experimenter and Theano
- Widening WEKA’s broad learning capability
- Building Python scripts that have the ability to be called using WEKA or independently
- Creating Python scripts with GUI
MLib Use Cases
- A Predictive analytics company that captures around Billions and billions of customer interactions and uses that data to build machine learning models that foretell customer intent across various channels – chat, online and voice uses Spark MLib for machine learning and automated engineering.
- Spark MLib is used for regular pattern mining and is an essence to the analytics platform of big data solution, and one Insight platform that is used by more than 100 customers across the world.
- A car manufacturing company’s Customer 360 Insights Platform leverages MLib library for prioritizing and categorizing and its customer’s social media communications in real-time.
- Spark MLib is an essential part of Open Table’s dining recommendations.
- Bank ‘s machine learning pipeline uses Spark MLib’s K-Means Clustering and Decision Tree Ensembles for anomaly detection.
- Online source provider uses Spark Streaming and Spark MLib to make user suggestions that best fit in its consumer tastes and buying histories.
Key benefits of Machine Learning
- Simplified Product Marketing and assistance with Accurate Sales Projections
- Facilitates Accurate Medical Predictions and Diagnoses
- Simplifies Time-Intensive Documentation in Data Entry
- Improves Precision of Financial Rules and Models
- Easy Spam Detection
- Enhances the Efficiency of Predictive Maintenance in the Manufacturing Industry
- Better Customer Segmentation and Accurate Lifetime Value Prediction
- Recommendation of the Right Product
Our Couchbase consulting services
- Software Lifecycle Management / Software Development Life Cycle (SDLC).
- PoC (proof of concept)
- Managed services