Machine learning seems to be getting all the interest and hype these days, and some are even saying that it's going mainstream. There are even dedicated conferences and summits for ML just like the 2021 AWS Machine Learning Summit. For ML to go mainstream, in my perspective, there are still real-world lessons we'll need to translate ML into production for businesses, and I was hoping to get some takeaways from this summit. I listed here some parts that made the most impact on me. Hopefully, you'll find these useful when you are planning to apply ML: Since the talk has been organized by AWS, the summit is leaning towards the use of their ML services, but the takeaways here can also be applied to other cloud computing platforms like GCP that offer their own ML services.

Machine learning is one of the most transformative technologies of this generation, but the tech community only is "scratching the surface" when it comes to what's possible, according to Swami Sivasubramanian, vice president of artificial intelligence and machine learning at Amazon Web Services. Machine learning is transforming everything from the way business is conducted to the way people entertain themselves to the way they get things done in their personal lives. "Entire business processes are being made easier with machine learning," Sivasubramanian said during AWS' Machine Learning Summit last week. Supply chain analysts can have faster and more accurate forecasts. And manufacturers can easily spot defects in products." And the barriers to entry have been significantly lowered, enabling builders to quickly apply ML to their most pressing challenges and biggest opportunities. "ML is improving customer experience, creating more efficiencies in operations and spurring new innovations and discoveries like helping researchers discover new vaccines and enhancing agricultural output with better crop monitoring," Sivasubramanian said. "But we are just scratching the surface about what is possible, and there is so much invention yet to be done.

We're about a week away from the AWS Machine Learning Summit and if you haven't registered yet, you better get on it! On June 2, 2021 (Americas) and June 3, 2021 (Asia-Pacific, Japan, Europe, Middle East, and Africa), don't miss the opportunity to hear from some of the brightest minds in machine learning (ML) at the free virtual AWS Machine Learning Summit. This Summit, which is open to all, brings together industry luminaries, AWS customers, and leading ML experts to share the latest in ML. You'll learn about science breakthroughs in ML, how ML is impacting business, best practices in building ML, and how to get started now without prior ML expertise. This post is your guide to navigating the Summit.

Amazon Web Services Inc. has made a big bet on artificial intelligence and machine learning, and just how big a bet is likely to become apparent Tuesday when its AI chief presents his keynote at the cloud giant's re:Invent virtual conference that continues this week. Swami Sivasubramanian, vice president of Amazon AI, will hold the first-ever re:Invent keynote on the topic, a clear sign that AWS views AI and machine learning as an area ripe for reinvention. AWS Chief Executive Andy Jassy (pictured) told me that the company's overall aim is to enable machine learning to be embedded into most applications before the decade is out by making it accessible to more than just experts. "People hire products and services to do a job," he said. "They don't really care what you do under the covers."

Many believe SageMaker, the machine learning (ML) service from Amazon Web Services, has truly democratised the adoption of artificial intelligence (AI) and data science by making it available for developers, corporations, and laymen alike. What you may not know is that the idea behind this AWS offering took root in Chennai a few years ago, when a software engineer on an annual pilgrimage back home was nursing a bad bout of jet jag. Swami Sivasubramanian, VP, Machine Learning, AWS, is considered a pioneer in cloud computing. The 41-year-old joined Amazon in 2005 after completing a PhD in distributed computing, making him one of the early employees for an idea that is now a $36 billion ARR business. Over the years, he has built 40 AWS services along with his team.

It's pretty clear that the next big battleground for public cloud providers will involve artificial intelligence. Just as companies like Amazon Web Services made it possible for ten-person startups to take advantage of world-class computing infrastructure, so too will the big cloud providers compete to provide artificial intelligence expertise to companies that can't afford to duplicate the advanced machine-learning research already underway. Swami Sivasubramanian, vice president of Amazon AI, is one of the key drivers of AI research for AWS. Cloud rivals like Google and Microsoft have signaled quite clearly that they will attempt to compete for the cloud workloads of the future by pushing the envelope of AI and machine-learning research and abstracting that effort for their cloud customers, and AWS must at least match those efforts to stay on top. Sivasubramian will be talking about Amazon's work in this area at our Cloud Tech Summit this Wednesday in Bellevue, and I recently caught up with him to get a preview of his talk.