Cloud News

7 Things to Know About AWS ML: Swami Sivasubramanian

Donna Goodison

‘Advancements in machine learning, fueled by scientific research, abundance of compute resources and access to data, has … meant that machine learning is going mainstream,’ says Sivasubramanian, vice president of artificial intelligence and machine learning at Amazon Web Services. ‘We see this in our customers’ adoption of the machine learning technology.’

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. “Marketers can more easily tailor their message. 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. Accelerating adoption of ML requires bright minds to come together and share learnings, advances and best practices.”

Research firm Gartner named AWS a "visionary" in its 2021 Magic Quadrant for Data Science and Machine Learning Platforms in March, noting Amazon SageMaker, AWS’ flagship fully managed ML service, is continuing to “demonstrate formidable market traction, with a powerful ecosystem and considerable resources behind it.”

“We built Amazon SageMaker from the ground [up] to provide every developer and data scientist with the ability to build, train and deploy ML models quickly and at a lower cost by providing the tools required for every step of the ML development life cycle in one integrated, fully managed service,” Sivasubramanian said. ”For expert machine learning practitioners, researchers and data scientists, we focus on giving a choice and flexibility with optimized versions of the most popular deep learning frameworks, including PyTorch, MXnet and TensorFlow, which set records throughout the year for the fastest training times and lowest inference latency. And AWS provides the broadest and deepest portfolio of compute, networking and storage infrastructure services—with the choice of processes and accelerators—to meet our customers’ unique performance and budget needs for machine learning.”

Read on to find out what more Sivasubramanian had to say about AWS’ and parent company’s work in ML and how AWS customers are leveraging it.

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