At AWS, we aim to put machine learning (ML) in the hands of all builders. In this session, learn the different ways AWS is empowering builders with ML using services such as Amazon Aurora, Amazon Redshift, Amazon Neptune, and Amazon QuickSight to build new experiences and reimagine existing processes. Download slides »
Speaker: Tom McMeekin, Enterprise Solutions Architect, AWS
Duration: 30mins
59:03Data is at the center of every application, process, and business decision and the foundation to delivering greater value. Organizations who are successful in extracting insights from their data are a
1:04:52The most common value organizations are hoping to get from their data is smarter decision-making to create better products and services, transform customer experiences, improve operational efficiencie
26:38Join Swami Sivasubramanian, Vice President, Data and Machine Learning, AWS, as he showcases the latest AWS innovations that can help transform your company’s data into meaningful insights and actions
Data can be an invaluable source of growth for organizations. The key to harnessing its true value is analyzing it effectively and creating a data-driven culture.
Businesses have the opportunity to unlock significant value across the organization with the help of machine learning and AI. Follow the proven path to machine learning success. Read the Machine Learn
Move beyond the hype and discover the tangible benefits of machine learning. In this e-book, we have outlined seven leading use cases where businesses have successfully applied machine learning to ach
Machine learning (ML) has become a core component of operations within growth-minded companies, unlocking new opportunities and resolving key challenges.
Machine learning (ML) has become a core technology ingredient for organizations to drive real-world innovation, yet many are struggling to bring more ML models into production in a repeatable and resp
By providing the broadest and most complete set of machine learning (ML) services, AWS is able to meet its customers wherever they’re at in their ML journeys and help them achieve specific objectives.
33:57Organizations are looking to derive greater value from their data to increase agility, improve efficiency, and accelerate innovation. While data is abundant and growing rapidly, just producing or stor
32:04Many organizations know they need AI/ML to create unique competitive advantage, drive better customer engagement, and deliver desired business outcomes. While some are reaping the benefits from the tr
13:44Amazon is the world’s biggest corporate buyer of renewable energy and needs to ensure sustainability is at the heart of all of its operations in order to meet its carbon emission targets. An area it i
31:15AI and ML hold the promise of transforming industries, increasing efficiencies, and driving innovation. The key to machine learning success is scale. This session covers how executives and managers wh
30:02AI/ML techniques are increasingly important fundamentals for organizations looking to transform and deliver their objectives. However, applying AI/ML in the right place is not easy. Join this session
26:21Reproducibility, traceability, explainability have become the fundamental requirement in the machine learning lifecycle for regulated industries. But building a data science platform for a bank or the
26:31The widespread adoption of mobile services, new digital native market entrants, adapting to generational shifts, and critical industry requirements on regulatory compliance make it challenging for fin
37:13When it comes to customer communications, it is not surprising that personalization is the best way to ensure engagement with customers in the long run. Customers are more likely to give their attenti
34:19Whether your organization is starting on its AI/ML journey, or has a large number of projects in production, it is vital to have secure environments that protect your data. In this session, we share h
27:36Over the past decade, NVIDIA has been able to illustrate the effectiveness of its GPUs across the board for both deep learning training and inference. As these models become larger, the inherent need
29:50How can you get the most accurate and specific answers in search queries when the answers may require you to sieve through large volumes of distributed data sources? In this session, we show how to us
