The purpose of our Big Data, Analytics and ML services is to plan, build, and implement Business Intelligence and Machine Learning infrastructure on premises or in the AWS Cloud so you can have the answers to the above questions in real time and when you need it. Architecting and deploying scripts and services to activate data pipelines, implementing data engineering stack necessary to land reliable data in the Data Lake for Analytics, Insights and ML, all enable clients to make decisions that improve business outcomes.
Sourcing data via AWS Kinesis, Apache Kafka or batch ingest from on-premises transaction systems or data warehouses like Oracle, Hadoop or AWS S3 buckets, we cleanse and enrich the data, and then load the data into a Data Lake, which provides the foundation to build higher level intelligence. Data is the new oil, but before it can be useful, it has to be refined. Our Data Pipeline services include:
Get a clear understanding of the business operations from out of the box and custom reports using various visualization notebooks (Tableau, Jupyter, Quicksight..) with easy to understand visuals that provide valuable insights tied to business outcomes.
Sourcing data from reliable Data Lakes or Data Warehouses, we build custom code or use the power of AWS Sagemaker to test and train ML models that provide probabilistic predictions and can provide end points that can be consumed to enable human or machine decision making. Our approach is ROI Based to ensure that we are going after the right use cases and that we also consider the ability with which it can be operationalized-- either with human supervision or automatically.
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