Machine Learning

Pragma Edge provides an integrated platform for B ig Data and Machine Learning (ML) utilizing the best practices in Data Integration, Search and Discovery, Collaboration, and knowledge management that are the foundation of building Big Data and Machine Learning. Pragma Edge Delivers platform and consulting services to build your Big Data and Machine Learning implementations.

Pragma Edge Machine Learning utilizes IB M Watson and Blue Mix Watson, which can understand all forms of data, interact naturally with people and learn and reason, at scale.

Understand

With Watson, you can analyze and interpret all of your data, including unstructured text, images, audio, and video.

Reason

Data ingestion in zeppelin can be done with Hive, HBase and other interpreter provided by the zeppelin.

Learn

With Watson, you can utilize machine learning to grow the subject matter expertise in your apps and systems.

Interact

With Watson, you can create chat bots that can engage in dialog.

Watson Conversation: Quickly build, test and deploy bots or virtual agents across mobile devices, messaging platforms, or even on a physical robot to create natural conversations between your apps and users.

Along with IBM Watson Pragma Edge ML leverages Apache Spark, Apache Spark powers a stack of libraries including SQL and Data Frames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application.

Apache Spark combined with Neural networks and deep learning currently provide the best solutions to many problems in many areas including Risk Management, Customer Promotions, User behavior , image recognition, speech recognition, and natural language processing. Data Mining, Data Processing, Data Science with algorithms are core behind Pragma Edge Machine Learning to enable neural networks and deep learning.

Pragma Edge will build neural network for your business, extracting your data and derive hidden asserts and build competitive advantage that can help you strive in your business.

Choose a decision that has representative historical data and a measurable cost of error. Evaluate data quality, model results and operational handling of uncertain outputs before expanding beyond the pilot.

Start with the decision or workload the data must support. Identify source systems, update frequency, data quality and access restrictions before selecting the processing pattern. Connect ingestion, transformation and downstream use through an explicit ownership model so missing or inconsistent information can be investigated.

Validate data completeness, schema changes, late arrivals and repeat processing. Where machine learning is included, define the evaluation set and the business meaning of an incorrect result. Review access controls and sensitive fields throughout the flow, including the data used for development and troubleshooting.

Bring representative datasets, source specifications, existing pipelines and the intended use case. Agree a first deliverable with measurable quality and freshness requirements. The engagement can produce a data-flow design, transformation rules, validation checks and an operational handover tied to the selected platform and deployment.