77. AWS re:Invent 2021 recap - #3 Putting your data to work
Swami Sivasubramanian, AWS VP Machine Learning, who delivered the "database, analytics, and machine learning" keynote at re:Invent, explored what it takes to put data in action with an end to end data strategy, In this blog, we would also look into another two keynotes, delivered by CTO Dr. Werner Vogels and Peter DeSantis, SVP of AWS Utility Computing.
Organization needs to move to a modern end-to-end data strategy
1.?Modernize your data infrastructure & Reduce storage costs
A successful modernization strategy starts with the business need in mind, only then focuses on technologies. Many customers asked the question "how to diagnose performance problems in current database and how to manage cost?"
Organization needs to modernize their data and application infrastructure, get off of on-premises databases and onto cloud data infrastructure. So that organizations can access IT resources like databases over the internet instead of buying, owning, and maintaining physical data centers and servers themselves. where AWS database services take care of all management tasks such as server provisioning, patching, configuration, or backups, after 15 years of development, AWS now provides a different range of databases serve different type of demands, incl. relational or unstructured data (80% of data today).
Being good at managing databases is not a differentiating factor for most businesses, so a lot of them naturally gravitate toward managed services in the cloud. But even so, these customers would still like to see more automation tools for managing these services, especially around diagnosing performance issues. DevOps Guru for RDS helps these users detect issues when performance metrics spike for some reason. The service looks at the activity in the database and flags anything unusual. But what’s probably most important is that the service then also performs a root cause analysis to recommend changes and — whenever possible — it will even automatically remediate issues.
2.?Unify your data
It's important to have a "single source of truth" about your business. Ensuring that your teams are all looking at the same data can help your company make the most of it. Of course,?this doesn't mean every team?should have access to every piece of data; different teams can and should have different permissions and levels of access. What's important is that this data is consistently reported and recorded.?
"Opportunities to transform your business with data exist all along the value chain," says Sivasubramanian.?"But creating such a solution requires companies to have a full picture and a single view of their customers and their business."
AWS Lake Formation is a powerful approach to organize data in a secure and efficient way, with row and cell-level security for lake formation, this is pretty impressive.
3. Find innovative uses for?your data
"Machine learning is improving customer experiences, creating more efficiencies, and spurring completely new innovations," says?Sivasubramanian. "And having the right data strategy is the key to these innovations."
SageMaker, is the company’s managed service for building, training and deploying machine learning (ML) models.?AWS launched a new SageMaker Ground Truth Plus service that uses an expert workforce to deliver high-quality training datasets faster.?also rolled out a new SageMaker Inference Recommender?tool to help users choose the best available compute instance to deploy machine learning models for optimal performance and cost. AWS says the tool automatically selects the right compute instance type, instance count, container parameters and model optimizations.?
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Sivasubramanian points?to several?AWS customers that have benefited from applying machine learning and analytics to their data.?Tyson Foods has used?cameras armed with computer vision to identify ways to reduce?waste by?cutting down on packaging. And?Pinterest has used?natural language processing to create more accurate search engines that allow employees to find the information they need faster.
Over 1.5 million AWS customers are using database, analytics, or machine learning AWS services, including Netflix, GE, Nasdaq etc. As ML spreads to more industries and across different lines of business, the structure and processes needed to create models must adapt to new environments and use cases, it requires experiment, inspect and adapt as you go, while generate outcomes.
It's critical to provide more people of machine learning capabilities, AWS AI & ML scholarship program is launched.
Case 1: synthetic aperture radar (SAR) satellites - Payam Banazadeh
It provides 24-hour all-weather Earth observation. What makes SAR unique is its ability to penetrate atmospheric conditions, providing near real-time visibility in cloud covered areas, both day and night.Capella Space’s commercial SAR technology offers many advantages that traditional earth observation sensors cannot provide.
Capella has developed a modern, reliable and scalable online platform for customers to access SAR imagery that was built on a cloud-native architecture using AWS. Capella’s online platform provides both a web-based data portal, Capella Console, and a corresponding API.
Case 2: Liberty Mutual Insurance - Matt Coulter
Liberty Mutual Insurance has shared the journey of transitioning to a #Serverless??Infrastructure, to become more agile, releasing higher-quality solutions for customers on a faster time line while reducing cost per million transactions to $60, deployed over 3,500 serverless patterns in 1 year using AWS CDK, following “AWS Well-Architected” while developing mindset of “code is liability” to drive simplification
AWS also launched SageMaker Studio Lab, a free service to help developers learn machine learning techniques and experiment with the technology, a new machine learning service called Amazon SageMaker Canvas. The new service will allow users to build machine learning prediction models, using a point-and-click interface.
Re:Invent opened a couple days after?The Wall Street Journal?published an editorial entitled “It’s Time to Get Rid of the IT Department,” in which academic Joe Peppard advocated moving hardware and software to the cloud to dispense with the need for the IT specialists who manage it, leaving CIOs to coordinate IT activities embedded in the rest of the business.
“Business users and analysts can use Canvas to generate highly accurate predictions using an intuitive, easy-to-use interface, without writing code and with no machine learning experience,” Selipsky said. “Canvas uses terminology and visualizations that are already familiar to analysts and complements the data analysis tools that they're already using.”
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