Tag: HAQM SageMaker

Building effective machine learning models requires storing and managing historical data, but conventional databases can quickly become a nightmare to regulate. Queries start taking too long, for example, slowing down business decisions. Learn how to use HAQM Redshift ML and Query Editor V2 to create, train, and apply ML models to predict diabetes cases for a sample diabetes dataset. You can follow a similar approach to address other use cases such as customer churn prediction and fraud detection.

By avoiding inspections, insurers lose the opportunity to adequately consider all factors during underwriting and take on more risk. TCS has built a virtual inspection solution on AWS that is customizable to various inspection use cases, such as home insurance inspections and claims, auto claims damage assessment and estimation, and mortgage inspection. This post provides an overview of the TCS virtual inspection solution, describes the high-level architecture, and explores the potential business benefits for insurers.

As multi-tenant SaaS providers look to leverage machine learning services, they must consider how they’ll protect the data that flows in and out of these services from different tenants. Learn how tenant isolation of machine learning services can be achieved using AWS IAM, and how the integration between IAM, HAQM SageMaker, and many other AWS services provide developers with a rich set of mechanisms that can be applied to realize tenant isolation goals.

Element 84, in collaboration with AWS and Geoscience Australia, has released the Sentinel-2 Cloud-Optimized GeoTIFF (COG) dataset on AWS Open Data. Sentinel-2 is an important platform for Earth observation, and its imagery contributes to ongoing research in climate change, land use, and emergency management. By making the Sentinel-2 archive more cloud-native, we are making the data more user-friendly and (hopefully) making the lives of emergency managers, climate scientists, and policy makers that much easier.

Operationalizing machine learning models can be a challenge due to lack of established ML architecture and its integration with the existing landscape. DataRobot integrates with AWS and provides the flexibility for a model trained in DataRobot to be deployed on AWS services with centralized model governance, management, and monitoring. Learn how the DataRobot AutoML platform orchestrates the complete model development and training lifecycle.

Industrial data exists in different components and systems, in various formats, and with separate access protocols. To harness this data and benefit from the synergies of system-wide interoperability, businesses must effectively digitize shop floor data. Learn how Amorph Systems’ SMARTUNIFIER software complements AWS services to deliver leading-edge connectivity for Industrial Internet of Things (IIoT). Amorph Systems is a leading developer of software technologies and products for industrial connectivity.

Many organizations leverage unstructured data collected from social media feeds, stock streaming, and data clickstream to gain insights about the needs of their customers. The EZ Lake Access (EZLA) solution developed by TCS centralizes and simplifies access management of the Data Lake House by codifying most of the enterprise access controls in the form of a rule engine. This provides increased efficiencies and easy adoption of the Data Lake House.

Today’s global marketplace relies heavily on contact centers for streamlining, maintaining, and maximizing customer service and sales at scale. Explore the role of machine learning solutions in transforming contact centers and the key aspects of Quantiphi’s contact center intelligence (CCI) solution built on AWS. Learn how it helped a U.S.-based consumer healthcare organization address contact center challenges by using custom artificial intelligence and ML techniques.

There has been a lot of buzz about a new data architecture design pattern called a Lake House. A Lake House approach integrates a data lake with the data warehouse and all of the purpose-built stores so customers no longer have to take a one-size-fits-all approach and are able to select the storage that best suits their needs. Learn how to couple HAQM Redshift with a semantic layer from AtScale to deliver fast, agile, and analysis-ready data to business analysts and data scientists.

Leveraging data to make better decisions is critical for driving optimal business outcomes. Palantir empowers organizations to rapidly extract maximum value from one of their most valuable assets—their data. Palantir Foundry solves for the real-world application of AI, and not how it works in the lab. Effective AI is impossible without a trustworthy data foundation, a representation of an institution’s decisions, and the infrastructure to learn from every decision made.