Data is the lifeblood that drives any successful AI & ML business strategy.
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Big Data Requires Big Infrastructure. At MavenCode, we build scalable AI & ML data pipelines for organizations.
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Let MavenCode help you make your organization’s move to the Cloud a reality.
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Let our experts guide you through the complicated and quickly changing
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Data Visualization is key component of any successful data acquisition, analysis, and monetization strategy.
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1
Discovery engagement to understand client environment, requirements, and goals.
2
Follow up consultation(s) to refine project requirements and milestones.
3
Architect and build data solution for client. Our consultants are available onsite as needed.
4
Deploy data solution in production and provide ongoing client support.
Every business wants to achieve growth at great scale and better serve its customers to build up their brand equity and achieve customer loyalty for life. The challenge of doing this at scale becomes enormous without the power of ML techniques ...
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The growing abundance of healthcare data today comes from a variety of different sources and forms, such as Electronic Health Records (EHRs), genomic sequencing, medical IoT devices, and many more. This makes the healthc...
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At its core the financial industry has always been driven by data in the form of transactions - ranging from a simple exchange of money or credit in a store for goods, all the way to multi-million dollar automated stock trades that execute ...
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Machine learning (ML) is transforming the ways in which oil and gas exploration is done. With ML techniques, researchers, geoscientists and reservoir engineers can leverage insights from advanced deep learning models to identify complex ...
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Working with BMW North America, we implemented a data processing pipeline for processing Telematics streaming data on AWS Cloud with EKS, ECR, EC2, and Kinesis.
Implemented a Data Pipeline for processing a large scale dataset using Google Cloud DataProc, BiqQuery, and Cloud Composer.
Implemented a data processing and analytics pipeline with Data lake on AWS S3 storage to process the daily batch with AWS EMR running multiple Spark Jobs orchestrated with Airflow.
Built a document processing pipeline leveraging AI on Google Cloud using Google GCP PubSub, Cloud Run and Cloud Functions.
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