![]() “It turns out that machine learning is really good at identifying subtle signals against a lot of noisy data. “Now, in addition to making ML more accessible to data analysts, it turns out that combining machine learning with certain types of data models can also lead to better predictions,” he said.Īnother area where customers have been asking AWS for help with using ML is in anomaly detection, said Sivasubramanian. That’s where the new Amazon RedShift ML comes in, as well as the integration of Amazon SageMaker Autopilot into Amazon RedShift, to make it easy for data warehouse users to apply machine learning on that data, he said. “So, we asked ourselves, how can we make this easy for our RedShift customers?” “And customers want their analysts to leverage machine learning with that data in RedShift without having to deal with having the skills or the time to use machine learning,” said Sivasubramanian. That’s when AWS engineers began thinking about existing Amazon RedShift customers who are already processing exabytes of data to power analytics workloads, he said. And with machine learning algorithms that are purpose-built for graph data use SageMaker and the deep graph library, developers can improve prediction accuracy by over 50% compared to that of traditional ml techniques.”Īmazon Neptune ML is now generally available.ĪWS already has ML tools for customers who want to use pre-trained models with Amazon's Aurora relational database and Amazon Athena interactive query services, said Sivasubramanian, but some customers don’t want to select training models at all. “Neptune ML does the hard work for you by selecting the graph data needed for training, it automatically chooses the best ML model for selected data, exposing ML capabilities via simple graph queries and providing templates to allow developers to customize ML models for advanced scenarios. In response, AWS unveiled Amazon Neptune ML, which is built to enable easy, fast and accurate predictions for graph applications, he said. But those customers lacked the time and skills to make that happen. Graph databases are often used to store complex relationships between data and graph models, and customers told AWS that they wanted to apply machine learning to applications that use graph data to build better recommendation engines and generate more accurate predictions for fraud detection, according to Sivasubramanian. That’s how Amazon Neptune ML was inspired, based on the existing Amazon Neptune managed graph database service, he said. Those requests have been driving some of the new innovations and ML tools being developed by AWS, he said. With each new idea for using ML in existing business processes, customers begin to see new needs for the technology, said Sivasubramanian. “As you can see, our customers are innovating virtually in every industry,” he said. Many large enterprises are already using ML from AWS, said Sivasubramanian, including Domino's Pizza, which is using ML for predictive ordering pharmaceutical company Roche, which is using Amazon SageMaker to accelerate the delivery of treatments and tailor medical experiences and Cabbage, an American Express company, which is using ML for loan application processes. Our customers are applying machine learning to the core of their businesses.” These tools are no longer a niche investment. “More than 100,000 customers use AWS for machine learning today, from creating a more personalized customer experience to developing personalized pharmaceuticals. ![]() “Machine learning is one of the most disruptive technologies we will encounter in our generation,” Swami Sivasubramanian, the vice president of AI for AWS, said in the company’s first-ever ML keynote at the event on Tuesday (Dec. The event continues to be held virtually due to the ongoing COVID-19 pandemic. ![]() The new tools include Amazon Neptune ML, which gives application developers access to ML that is purpose-built for graph data Amazon RedShift ML, which enables algorithms to run on Amazon RedShift data without manual selection, building and training of ML models and Amazon Lookout for Metrics, which uses ML to detect anomalies in metrics to protect the health of businesses.Īlso unveiled were additional details about nine new ML capabilities within Amazon SageMaker, which were first discussed last week during the first week of AWS re:Invent 2020. ![]() AWS re:Invent 2020 – Simplifying the use of machine learning and deep learning processes for enterprise, manufacturing and industrial customers is the goal of a series of new ML tools and services unveiled this week by Amazon Web Services at the company’s annual re:Invent tech conference.
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