Tag: Machine Learning Models
As direct to customer (D2C) gains popularity among retailers, there’s an increasing need to mix online and offline experiences to improve customer engagements and sentiment. One such popular channel is popup stores. This post explores a Capgemini solution that uses Amazon Web Services (AWS) to help retailers engage with customers in a smart way. The solution leverages deep learning to enhance the customer experience through gamification and provides key insights and marketing leads to retailers.
Deploying machine learning (ML) models as a packaged container with hardware-optimized acceleration, without compromising accuracy and while being financially feasible, can be challenging. As machine learning models become the brains of modern applications, developers need a simpler way to deploy trained ML models to live endpoints for inference. This post explores how a ML engineer can take a trained model, optimize and containerize the model using OctoML CLI, and deploy it to Amazon EKS.
Although the business case for digital analytics is well-articulated, many organizations are looking for ways to build stronger cases around transformations by consolidating data generated across the enterprise with customer behavioral data. Learn how Softcrylic developed the Analytics Shift solution which helps businesses bring Adobe Analytics data into Amazon Redshift to drive deeper insights and data integration.
A closed loop assurance system predicts network events, such as faults and congestions, that are highly probable of causing service degradation or interruption, and automatically take preventive actions to avert service disruptions. Learn how Infosys leveraged AWS data streaming, data analytics, and machine learning services to ingest, process, and analyze high volumes of data from disparate sources; and to build ML models to predict network events that cause service degradation.
Spark is a distributed computing framework that added new features like Pandas UDF by using PyArrow. You can leverage Spark for distributed and advanced machine learning model lifecycle capabilities to build massive-scale products with a bunch of models in production. Learn how Perion Network implemented a model lifecycle capability to distribute the training and testing stages with few lines of PySpark code. This capability improved the performance and accuracy of Perion’s ML models.
Next Caller uses machine learning on AWS to drive data analysis and the processing pipeline. Amazon SageMaker helps Next Caller understand call pathways through the telephone network, rendering analysis in approximately 125 milliseconds with the VeriCall analysis engine. VeriCall verifies that a phone call is coming from the physical device that owns the phone number, and flags spoofed calls and other suspicious interactions in real-time.
Voice data represents a rich and relatively untapped source of information that can help organizations gaining precious insights into their customers and operations. By leveraging a number of AWS services, Deloitte’s speech analytics solution, TrueVoice, can process voice data at scale, apply machine learning models to extract valuable information for this unstructured data, and continuously refine and enrich such models, tailoring them to specific industries and business needs.