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AWS re:Invent 2022: ‘Machine Learning Is No Longer the Future’

Artificial intelligence and machine learning are transforming the way we live and work.

Joe Kuehne is an Associate Editorial Director at BizTech magazine.

Artificial intelligence, machine learning and data analytics have become so much more than buzzwords visionaries use to describe a distant future. They are tools that enable businesses in nearly every industry to stay ahead of the curve competitively and technologically.

At AWS re:Invent, AWS’s Bratin Saha, vice president and general manager of artificial intelligence and machine learning, presented a leadership session titled “Innovate with AI/ML to Transform Your Business.” In opening the session, he noted, “According to a McKinsey survey on the adoption of AI, almost 60 percent of companies now say that they use AI in at least one function in the organization, and that shows how machine learning has transitioned from being a niche activity to becoming integral to how companies do business.”

“Machine learning is no longer the future,” Saha said. “Machine learning is the present that needs to be harnessed now.”

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AWS Offers Different Layers of Machine Learning

Saha noted that customers approach machine learning in different ways, so AWS seeks to meet them where they are in their implementation.

According to Saha, customers fall into one of three layers of development, and AWS offers services for each layer. “At the bottom layer are the machine learning infrastructure services. This is where we provide the machine learning hardware and software that customers can use to build their own machine learning infrastructure,” he said. “This is meant for customers with highly custom needs, and that is why they want to build their own machine learning infrastructure.”

Most organizations are in the middle layer, Saha explained. That involves AWS building the machine learning infrastructure so customers can focus on what Saha called “the differentiated work of building machine learning models.” In this layer, AWS customers can take advantage of its fully managed machine learning service, Amazon SageMaker.

At the top layer, AWS provides more than just the foundational ML infrastructure. These customers benefit from AI services “where AWS embeds machine learning into different use cases such as personalization, forecasting, anomaly detection, speech, transcription and others,” Saha said.

Amazon SageMaker Is Democratizing Machine Learning

As Saha pointed out, most organizations using ML are somewhere in the middle layer of development, where Amazon SageMaker can help them reach their automation goals.

“Today, tens of thousands of customers of all sizes and across industries rely on Amazon SageMaker. AWS customers are building millions of models, training models with billions of parameters, and generating trillions of predictions every month. Many customers are using ML at a scale that was unheard of just a few years ago,” Saha said in a related press release.

“The new Amazon SageMaker capabilities announced today make it even easier for teams to expedite the end-to-end development and deployment of ML models. From purpose-built governance tools to a next-generation notebook experience and streamlined model testing to enhanced support for geospatial data, we are building on Amazon SageMaker’s success to help customers take advantage of ML at scale,” Saha said.

Machine learning is the present that needs to be harnessed now.”

Bratin Saha Vice President and General Manager, Artificial Intelligence and Machine Learning, AWS

Amazon Introduces New Notebooks Through SageMaker

One enhancement Amazon introduced during Saha’s session was a new line of SageMaker Studio notebooks. Saha said the notebooks can “revolutionize data science by making it easy for customers to prepare data and experiment with machine learning models.”

He added that the new notebooks will enable customers “to visually prepare the data to do real-time collaboration and to quickly move from experimentation to production.”

Enhanced collaboration is one of the notebooks’ new capabilities. “Machine learning development today is a highly collaborative activity. But what happens is that developers use one tool for developing their models and a different tool for communicating with each other,” Saha explained. “With this new generation of notebooks, SageMaker now allows you to both develop and collaborate within the notebook itself. What that means is that multiple users can simultaneously co-edit and read these notebooks and files.”

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New Capabilities for Geospatial Data Can Accelerate Innovation

In a related keynote earlier in the day, Swami Sivasubramanian, vice president of database, analytics and machine learning at AWS, highlighted the difficulties many developers have in training ML models that depend on geospatial data.

“Geospatial data can be used for a wide variety of use cases, from maximizing harvest yield and agricultural farms to sustainable urban development to identifying a new location or opening a retail store,” Sivasubramanian explained. “However, accessing high-quality geospatial data to train your ML models requires working with multiple data sources and multiple vendors.”

Incorporating disparate data sets, which are typically massive and unstructured, “means time-consuming data preparation before you can even start writing a single line of code,” he continued.

To overcome these obstacles, Sivasubramanian announced that Amazon SageMaker can now support new geospatial ML capabilities, which allow users to access geospatial data from different data sources “with just a few clicks.” The platform also includes “built-in visualization tools, enabling you to analyze your data and explore model predictions on an interactive map using 3D-accelerated graphics. Finally, SageMaker also provides built-in, pre-trained neural nets to accelerate model building for many common use cases,” he said.

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