Mr. Cooper Group is an industry-leading mortgage services provider serving customers through servicing, originations, and digital real estate solutions. Using Google Cloud AI and ML solutions, they created a highly reliable, cloud native document analysis and processing platform to process lending documents and unlocked new levels of accuracy and operational efficiency that help them to scale and control the cost at the same time.
Good artists copy, great artists steal, and smart software developers use other people’s machine learning models. If you’ve trained ML models before, you know that one of the most time-consuming and cumbersome parts of the process is collecting and curating data to train those models.
Network and infrastructure management has always been complicated. AIOps promises to simplify things. Credit: momius via Adobe Stock As networks and data center infrastructures grow ever more powerful, management is becoming increasingly complicated and challenging.
Data centers may be in the midst of a flash revolution, but managing hard disk drives (HDDs) is still paramount. According to IDC, stored data will increase 17.8% by 2024 with HDD as the main storage technology. At Google Cloud, we know first-hand how critical it is to manage HDDs in operations and preemptively identify potential failures.
In capital markets, the stakes have been raised for participants to establish value, win loyalty, and expand their share of wallet. An organization’s data analytics capabilities, combined with artificial intelligence and machine learning , can open new opportunities in these areas.
Some of the most important data at your company isn’t living in databases, but in documents, and most business processes begin, involve or end with a document. Yet most companies are still manually entering data and reliant on guesswork to make sense of it all as the volume and variety of data explodes.
Launched at AWS re:Invent 2017, Amazon SageMaker is a fully-managed service that has already helped tens of thousands of customers quickly build and deploy their machine learning (ML) workflows on AWS. To help them get the most ML bang for their buck, we’ve added a string of cost-optimization services and capabilities, such as Managed Spot Training, Multi-Model Endpoints, Amazon Elastic Inference, and AWS Inferentia.
At AWS re:Invent 2020, we announced the preview of Amazon EMR Studio , an integrated development environment (IDE) that makes it easy for data scientists and data engineers to develop, visualize, and debug applications written in R, Python, Scala, and PySpark.
Telecommunications companies sit on a veritable goldmine of data they can use to drive new business opportunities, improve customer experiences, and increase efficiencies. There’s so much data, in fact, that a significant challenge lies in ingesting, processing, refining, and using that data efficiently enough to inform decision-making as quickly as possible—often in near real-time.
Research organizations across industry verticals have unique needs. These include facilitating stakeholder collaboration, setting up compute environments for experimentation, handling large datasets, and more. In essence, researchers want the freedom to focus on their research, without the undifferentiated heavy-lifting of managing their environments.
In our previous post , we introduced PyDeequ , an open-source Python wrapper over Deequ, which enables you to write unit tests on your data to ensure data quality. The use case we ran through was on static, historical data, but most datasets are dynamic, so how can you quantify how your data is changing and detect anomalous changes over time?
Spreadsheets are everywhere! They are one of the most useful productivity tools available. They make organizing, calculating, and presenting data a breeze. Google Sheets is the spreadsheet application included in Google Workspace , which has over 2 billion users .
Graph AI is becoming fundamental to anti-fraud, sentiment monitoring, market segmentation, and other applications where complex patterns must be rapidly identified. Artificial intelligence (AI) is one of the most ambitious, amorphous, and comprehensive visions in the history of automated information systems.
Democratization of data within an organization is essential to help users derive innovative insights for growth. In a big data environment, traceability of where the
