This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
This week on the keynote stages at AWS re:Invent 2024, you heard from Matt Garman, CEO, AWS, and Swami Sivasubramanian, VP of AI and Data, AWS, speak about the next generation of Amazon SageMaker , the center for all of your data, analytics, and AI. The relationship between analytics and AI is rapidly evolving.
This blog post is co-written with Pinar Yasar from Getir. In this post, we explain how ultrafast delivery pioneer, Getir , unleashed the power of data democratization on a large scale through their data mesh architecture using Amazon Redshift. Next, we’ll provide a broader overview of modern data trends reinforced by Getir’s vision.
The opportunities are great, but so are the challenges To make that future a reality, regulatory challenges confronting banks and insurance companies demand a constant reassessment of compliance strategies and operational frameworks. It’s hard to believe it’s been 15 years since the global financial crisis of 2007/2008. Well, sort of.
We are expanding IBM Db2 Warehouse on Power with a new Base Rack Express at a 30% lower entry list price, adding to today’s S, M and L configurations, while still providing the same total-solution experience, including Db2 Data Warehouse’s connectivity with watsonx.data to unlock the potential of data for analytics and AI.
As I meet with our customers, there are always a range of discussions regarding the use of the cloud for financial services data and analytics. It is not intended to be an exhaustive list, rather some of the issues that rise to the top of the strategy when assessing your hybrid data cloud deployment. .
Investments in artificial intelligence are helping businesses to reduce costs, better serve customers, and gain competitive advantage in rapidly evolving markets. Here, I’ll focus on why these three elements and capabilities are fundamental building blocks of a data ecosystem that can support real-time AI.
With the rapid advancements in cloud computing, data management and artificial intelligence (AI) , hybrid cloud plays an integral role in next-generation IT infrastructure. As an initial step, business and IT leaders need to review the advantages and disadvantages of hybrid cloud adoption to reap its benefits.
There is an urgent need for banks to be nimble and adaptable in the thick of a multitude of industry challenges, ranging from the maze of regulatory compliance, sophisticated criminal activities, rising customer expectations and competition from traditional banks and new digital entrants. Addressing new customers and markets.
The cloud space is exciting and fast evolving. And yet, we are only barely scratching the surface of what we can do with newer spaces like Internet of Things (IoT), 5G and Machine Learning (ML)/Artificial Intelligence (AI) which are enabled by cloud. What drew you to work in the cloud space? That’s so interesting!
Across industries, the exponential growth of technologies such as hybrid cloud, data and analytics, AI and IoT have reshaped the way businesses operate and heightened customer expectations. Businesses are now entering an even greater digital era marked by broader applications of AI, including generative AI models.
A data lakehouse is an emerging data management architecture that improves efficiency and converges data warehouse and data lake capabilities driven by a need to improve efficiency and obtain critical insights faster. Why is a data lakehouse architecture becoming increasingly important?
This digitalization and need to share medical data are driving the demand for precision medical technologies. Leading life sciences companies are discovering the power of cloud in enabling analytics and artificial intelligence (AI) , shrinking innovation cycles, and standardizing processes across global operations, among other benefits.
Incidentally, gaming has also grown because of demand reasons. With ever-changing regulations and the rise of digital competition, it is a challenge to run the casino business competitively. The casino operators are expected to face known challenges of rising competition, decreasing ROI, and a high churn rate.
It’s not a surprise that in today’s challenging economic landscape, rising costs pose a significant threat to the telecommunications industry. With the strategic use of open-source solutions and generative AI, the industry can not only implement cost-effective approaches but also pave the way for enhanced efficiency and scalability.
This blog post provides a concise session summary, a video, and a written transcript. Paco Nathan presented, “Data Science, Past & Future” , at Rev. Session Summary. Nathan also provided excerpts and insights from recent surveys to help provide additional context. Key highlights from the session include. Transcript.
All of these shifts, meanwhile, happen within the context of an AI-enabled threat landscape. Because VPNs are internet-connected, it has become relatively straightforward for attackers to use AI for automated recon targeting VPN vulnerabilities. But as cyber threats evolve, VPNs have shifted from trusted tools to major liabilities.
While political and demographic realities were a major factor, an unsung hero is the data modernization efforts that helped Canada track and contain the sudden rise in infections and meet the demand for public health services. This solution would later be key to Canadian public health agencies’ response to COVID-19.
We organize all of the trending information in your field so you don't have to. Join 42,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content