Today, global enterprises are investing in tools, technology and methodologies that deal with low-dimensional data, whether structured or unstructured, that can be analysed along few parameters. However, over a period, companies start gathering a lot of highly complex data, like images, videos, gifs etc. Deep Learning comes to the rescue to process and analyse this highly complex data.
From using past transactional and behavioural data to predict customers’ future habits, tastes and preferences and purchases to deciphering trends, contexts, attributes and much more, smart companies are using deep learning to deliver more than what their customers expect them to deliver.
Traditional machine learning requires humans to provide context for data — activity called feature engineering — so a machine can make better predictions. Deep learning uses a layered approach to make better decisions by constantly curating the data it is fed. It simplifies feature engineering in many ways, putting more work on machines, and ever more complex, self-learning models. Essentially, deep learning can assess and categorize data like our five human senses, and then make correlations like the human brain.
Our experts are trained on applying Random Forests, Naïve Bayes, Neural Networks and Support Vector Machine models to complex business data. Deep learning can change the way business targets its customers.
Machine Learning @ThinkBumblebee is about embracing a process of questioning, reasoning, discovery, and experimentation. Machine learning is great for situations where there are large data sets and cases to learn from. We at ThinkBumblebee have used Machine Learning algorithms across Classification, Regression, Clustering, Feature Selection / Extraction, Anomaly Detection and Neural Nets.
We are helping companies address a variety of problems across semantic search (social data), customer retention, next best action, customer / audience segmentation, anomaly detection in sales, multichannel attribution and more. Our work in Machine Learning is boosted by the capabilities that we have built in managing complex unstructured batch/streaming data. These capabilities in Big Data helps us in implementing Machine Learning models in the production environment.
With the rise of high complex, unstructured data and ever changing dynamic business environment, there is a huge need for Real Time Decisioning. Enterprises are now rapidly moving to add streaming analytics as a strategy for becoming more agile and responsive to data available in real-time.
At ThinkBumblebee, we have the capability to build and deploy streaming analytics applications for across industries, verticals, use cases and data formats. For us data sources could be any – clickstream, social, sensor, logs or any other event data, we are able to provide a unified, high-throughput, low-latency platform for handling real-time data feeds.
Using our experience in Apache Spark / Apache Apex and knowledge of widely penetrated Data Storage Systems like Amazon S3, MS Azure and NoSQL databases like Druid, Cassandra, Hbase, we provide comprehensive Big Data Analytics solutions across a variety of business needs.