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Whether it’s data being used inside applications, feeding machinelearning models, or downstream analysis, companies are increasingly reliant on this data, and that’s not changing. Executive teams and boards are demanding innovation with LLMs and data. 80% of data is unstructured within organizations.
Mai-Lan Tomsen Bukovec, Global Vice President for AWS Storage will deliver one of the keynotes. Cloud data lakes are a key technology enabling the innovation in analytics and machinelearning. This time, the conference will build on the foundation from last year’s event.
AWS has decreased prices for EC2, elastic compute cloud, and S3, simple storage service, 42 times in eight years. The first manifestation of large scale, near-free compute I’ve seen is in machinelearning. When I worked at Google in 2005, we would test individual machinelearning models one or two at a time.
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It was around that time about 12 years ago that Jeff Bezos launched AWS, and some of you may remember that, when he did this, Wall Street analysts were looking at him and saying, “Why would you take what’s already a very unprofitable business and drive it further into the red by investing in this AWS initiative?”
But it is really incredible and inspiring to see how these companies and these cloud leaders have ushered in this new phase of innovation and growth, even in the hardest moments of society. We’ve all seen AWS and what they’ve done with their platform. It is staggering. Because more and more hardware is becoming soft.
Despite bringing innovations, the industry of that time faced tremendous upfront costs and restricted accessibility. Moreover, there were slower innovation cycles compared to the cloud-based SaaS model that we know now, thanks to the advent of smart neural networks. That established the foundation for the SaaS model in technology.
Serverless platforms, such as AWS Lambda and Azure Functions, automatically scale resources based on demand, providing agility and cost optimization. Tools like Terraform and AWS CloudFormation enable infrastructure to be defined in code, promoting consistency, repeatability, and scalability across environments.
Feature Engineering : Data scientists transform raw data into features that are informative for machinelearning models. Design, develop, and implement machinelearning models and statistical analyses to extract meaningful patterns and trends. Bonus points : Experience with cloud platforms (AWS, Azure, GCP).
Using LLMs to enhance these solutions will no longer be seen as innovative but will become the standard.” Just like machinelearning before it disappeared in the background, AI will soon be so ubiquitous that it’s no longer a differentiator. In a better way too.”
Sameer Dhokalia: It turns out if you do ask those two basic questions of 100 people in your business, you will learn an awful lot about what needs to be focused on. You needed to understand how do we keep the bad actors out and use machinelearning to prevent spammers and phishers from taking advantage of our system.
The wave of SaaS companies that built themselves on the likes of AWS and Azure have reinforced the pre-eminence of cloud computing. It has been an eventful, often fraught decade in tech, then, and that’s not even to mention artificial intelligence or machinelearning, Alexa or Siri, Bitcoin or WeWork, Edward Snowden or GDPR.
The ecosystem continues to innovate but the second killer app (currency being the first), hasn't yet been found. Look no further than AWS Re:Invent where Amazon announced an entire suite of MachineLearning tools that compete with nearly every player in the ecosystem in every level of the stack.
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Mikkel : Well again, the public cloud, AWS, was the dominant leader. We are seeing platform shifts from how they traditionally run their infrastructure and services and business to seeing them run that stuff on AWS. What kind of customer experience, what kind of innovation do we need to help do you gather?
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And we also lead with a sense of optimism and lead with innovation. But ultimately we believe that Google Cloud comes at it from a really strong place of innovation and the DNA of our company is with engineers that want to help solve the world’s hardest problems and look for the most aggressive, bold opportunities.
78 times in the AWS … ADABAS was referenced in the Amazon press release and earnings announcement. And you get folks like Tobi at Shopify and also this room, I’d like to think, are now part of this club that’s driving tech innovation and tech value creation and job creation on a massive massive scale.
In just the past few years, weve watched Software-as-a-Service evolve at breakneck speed, transforming from a neat cloud-based delivery model into an essential driver of business innovation. Well, AI and machinelearning (ML) are making it a reality. Twilio, AWS) Storage (e.g., So, whats in store for 2025?
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