Gigatron: A pure bleeding edge monorepo for enterprise machine learning development

The data science can be defined as the convergence of Computer Science, programming, mathematical modeling, data analytics, academic expertise, traditional AI research, and applying the statistical techniques through scientific programming tools such as Python, R, TensorFlow, Java, on an ecosystem of SQL, NoSQL, GraphDB, streaming computing platforms such as Apache Spark, Apache Kafka, Apache Storm, Apache Nifi, Apache Flink, Apache Geode, and linked data to extract new knowledge discovery through data patterns and provide new insights from distributed computing platform from the tsunami of big data. Though, many times it is possible to define the statistical language models, it's difficult to implement through object-oriented programming languages. Therefore, it is critical to wear the hats of an advanced programmer, infrastructure architect to provide web scale performance with in-memory computing and apply traditional research with machine learning and deep learning algorithms to create novel architectures unique to each enterprise and avoid one-size fits all approach. Real-time analysis is all the rage in the data science industry. Therefore, leveraging in-memory computing ecosystems can provide faster execution results to the corporations.