‘Machine learning is pivotal to every line of business, every organisation must have an ML strategy’

Bengaluru, August 12

We don’t have to wait for post-Covid-19, we’re seeing this right now. Artificial Intelligence (AI) and Machine Learning (ML) are playing a key role in better understanding and addressing the Covid-19 crisis. In the fight against Covid-19, organisations have been quick to apply their machine learning expertise in several areas, including, scaling customer communications, understanding how Covid-19 spreads, and speeding up research and treatment. We’re seeing adoption of AI/ML across all industries, verticals and sizes of business. We expect this to not only continue, but accelerate in the future.

We don’t break out that number, but what I can tell you is AWS offers the broadest and deepest set of machine learning services and supporting cloud infrastructure putting machine learning in the hands of every developer, data scientist and expert practitioner.

Gartner's inclusion criteria explicitly excluded providers who focus primarily on developers. However, the Cloud AI Developer Services Magic Quadrant does cite us as a leader. Also, the recently released Gartner Solution Scorecard, which evaluated our capabilities in the Data Science and Machine Learning space, scored Amazon SageMaker higher than offerings from the other major providers.

I think, India is in a really good place. I remember visiting some of our customers and start-ups in India, there is so much innovation happening in India. I happen to believe that transformation comes because at a ground level, developers start adopting technologies and this is one of those things where I think India, especially at a ground level when it comes to the start-up ecosystem, have been jumping into in a big way to adopt machine learning technology.

For example, machine learning is embedded in every aspect of what Freshworks, a B2B unicorn in India, is doing. In fact, they build something like 33,000 models and they are iterating and they’re trying to build ML models, again using some of our technologies like Amazon SageMaker. They’ve cut down from eight weeks to less than one week. redBus, which I’m a big fan of as I travel back and forth between Chennai to Bengaluru, is also using some of our ML technologies and their productivity has increased. One of the key things we need to be cognizant of is that machine learning technology is not going to get mainstream adoption if people are just using it for extremely leading-edge use cases. It should be used in everyday use cases. I think even in India now, it is starting to get into mainstream use cases in a big and meaningful way. For instance, Dish TV uses AWS Elemental, our video processing service to process video content and then they feed it into Amazon Rekognition to flag inappropriate content. There are start-ups like CreditVidya, who are building an ML platform on AWS to analyze behavioural data of customers and make better recommendations.

One thing is for sure, there is change coming and technology is driving it. I’m very optimistic about the future. I remember the days where there used to be manual switching of telephones, but then we moved to automated switching. It’s not like those jobs went away. All these people re-educated themselves and they are actually doing more interesting, more challenging jobs. Lifelong education is going to be critical. In Amazon, my team, for instance, runs Machine Learning University. We train our own engineers and Amazon Associates on various opportunities and expose them to leading-edge technology such as machine learning. Now, we are actually making this available for free as part of the AWS Training and Certification programs. In November 2018 we made it free, and within the first 48 hours of us making this free, we had more than one lakh people registered to learn. So, there is a huge appetite for it. In 2012, we decided, every organisation within Amazon had to have a machine learning strategy, even when machine learning was not even actually considered cool. So Jeff and the leadership team said, machine learning is going to be such a pivotal thing for every line of business irrespective of whether they run cloud computing or supply chain or financial technology data, and we required every business group in their yearly planning, to include how they were going to leverage machine learning in their business. And “no, we do not plan to” was not considered an acceptable answer.

The vast majority of ML being done in the cloud today is on AWS. With an extensive portfolio of services at all three layers of the technology stack, more customers reference using AWS for machine learning than any other provider. AWS released more than 250 machine learning features and capabilities in 2019, with tens of thousands of customers using the services, spurred by the broad adoption of Amazon SageMaker since AWS re:Invent 2017. Our customers include, American Heart Association, Cathay Pacific, Dow Jones, Expedia.com, Formula 1, GE Healthcare, UK’s National Health Service, NASA JPL, Slack, Tinder, Twilio, United Nations, the World Bank, Ryanair, and Samsung, among others.

Our AI/ML services are meant for: Advanced developers and scientists who are comfortable building, tuning, training, deploying, and managing models themselves, AWS offers P2 and P3 instances at the bottom of the stack which provide up to six times better performance than any other GPU instances available in the cloud today together with AWS’s deep learning AMI (Amazon Machine Image) that embeds all the major frameworks. And, unlike other providers who try to funnel everybody into using only one framework, AWS supports all the major frameworks because different frameworks are better for different types of workloads.

At the middle layer of the stack, organisations that want to use machine learning in an expansive way can leverage Amazon SageMaker, a fully managed service that removes the heavy lifting, complexity, and guesswork from each step of the ML process, empowering everyday developers and scientists to successfully use ML. SageMaker is a sea-level change for everyday developers being able to access and build machine learning models. It’s kind of incredible, in just a few months, how many thousands of developers started building machine learning models on top of AWS with SageMaker.

At the top layer of the stack, AWS provides solutions, such as Amazon Rekognition for deep-learning-based video and image analysis, Amazon Polly for translating text to speech, Amazon Lex for building conversations, Amazon Transcribe for converting speech to text, Amazon Translate for translating text between languages, and Amazon Comprehend for understanding relationships and finding insights within text. Along with this broad range of services and devices, customers are working alongside Amazon’s expert data scientists in the Amazon Machine Learning Solutions Lab to implement real-life use cases. We have a pretty giant investment in all layers of the machine learning stack and we believe that most companies, over time, will use multiple layers of that stack and have applications that are infused with ML.

At Amazon, we always approach everything we do by focusing on our customers. We have thousands of engineers at Amazon committed to ML and deep learning, and it’s a big part of our heritage. Within AWS, we’ve been focused on bringing that knowledge and capability to our customers by putting ML into the hands of every developer and data scientist. But we do take a different approach to ML than others may – we know that the only constant within the history of ML is change. That’s why we will always provide a great solution for all the frameworks and choices that people want to make by providing all of the major solutions so that developers have the right tool for the right job. And our customers are responding! Today, the vast majority of ML and deep learning in the cloud is running on AWS, with meaningfully more customer references for machine learning than any other provider. In fact, 85 per cent of TensorFlow being run in the cloud, is run on AWS.

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