Large language models (LLMs) have demonstrated promising capabilities in machine translation (MT) tasks. Depending on the use case, they are able to compete with neural translation models such as Amazon Translate. LLMs particularly stand out for their natural ability to learn from the context of the input text, which allowsContinue Reading
In the modern, cloud-centric business landscape, data is often scattered across numerous clouds and on-site systems. This fragmentation can complicate efforts by organizations to consolidate and analyze data for their machine learning (ML) initiatives. This post presents an architectural approach to extract data from different cloud environments, such as GoogleContinue Reading
Machine learning (ML) helps organizations to increase revenue, drive business growth, and reduce costs by optimizing core business functions such as supply and demand forecasting, customer churn prediction, credit risk scoring, pricing, predicting late shipments, and many others. Conventional ML development cycles take weeks to many months and requires sparseContinue Reading
Over the last 18 months, AWS has announced more than twice as many machine learning (ML) and generative artificial intelligence (AI) features into general availability than the other major cloud providers combined. This accelerated innovation is enabling organizations of all sizes, from disruptive AI startups like Hugging Face, AI21 Labs,Continue Reading
Amazon DataZone is a data management service that makes it quick and convenient to catalog, discover, share, and govern data stored in AWS, on-premises, and third-party sources. Amazon DataZone allows you to create and manage data zones, which are virtual data lakes that store and process your data, without theContinue Reading
Innovations in artificial intelligence (AI) and machine learning (ML) are causing organizations to take a fresh look at the possibilities these technologies can offer. As you aim to bring your proofs of concept to production at an enterprise scale, you may experience challenges aligning with the strict security compliance requirementsContinue Reading
As digital commerce expands, fraud detection has become critical in protecting businesses and consumers engaging in online transactions. Implementing machine learning (ML) algorithms enables real-time analysis of high-volume transactional data to rapidly identify fraudulent activity. This advanced capability helps mitigate financial risks and safeguard customer privacy within expanding digital markets.Continue Reading
This is a guest post co-written with Ratnesh Jamidar and Vinayak Trivedi from Sprinklr. Sprinklr’s mission is to unify silos, technology, and teams across large, complex companies. To achieve this, we provide four product suites, Sprinklr Service, Sprinklr Insights, Sprinklr Marketing, and Sprinklr Social, as well as several self-serve offerings.Continue Reading
