Scott Mason - Solution Architect
Scott has designed and built Architectures that
include:
Cosmos DB sites that provide user personalized offers and recommendations - Click Link
Predicting Customer Value - Click Link
Large-scale Custom NLP (Natural Language Processing) - Click Link
Databricks solutions that allow superior ML solutions- Click Link
Cosmos DB sites that provide user personalized offers and recommendations - Click Link
Predicting Customer Value - Click Link
Large-scale Custom NLP (Natural Language Processing) - Click Link
Databricks solutions that allow superior ML solutions- Click Link
As a Solution Architect Scott can help you design solutions like this:
Integrated Cloud Architecture with Azure, AWS, Goolge Cloud, and others.
- Azure Cosmos Db
- Azure Event Hubs
- Azure Stream Analytics
- Azure Storage
- Azure Functions
- Azure Machine Learning
- Azure Cache for Redis
- Power BI Dashboards for Analysts
- AWS
- SAP on AWS
- Google Cloud
- Google Anti Money Laundering AI
Predict Customer Value Using AI and Data Science
This solution helps a Marketing Department use the power of AI and Data Science to predict the value of any group of customers to the company, to improve maketing planning and spend.
- A chief marketing executive might use these predictive models to determine which customers are futrue high-value customers versus occassional customers.
- The Sales Department might offer potential high-value customer better customer care or special incentives to build a better customer relationship.
- And the customer recieves a superior customer experience that better meets their needs.
Large-scale Custom NLP (Natural Language Processing)
This is a custom NLP (Natural Language Processing solution)
- Use Spark NLP for tasks like topic and sentiment detection and analysis
- During the processing steps, Databricks, Azure Synapse Analytics, and Azure HDInsight are used with Spark NLP to provide NLP functionality
- Databricks offers superior access to Spark and takes advantage of its processing power
- This allows a parallel processing framework that supports in-memory processing to boost the performance of big-data analytic applications