AWS executive Kanishka Agiwal, shared his thoughts on Machine Learning – its applications, growing adoption in different sectors, function in the future, as well as AWS’ role in building and supporting the ML ecosystem.
Machine Learning (ML) is now powering a wide range of applications in organisations across various industries. ML is accelerating digital transformation and catalysing business processes, and Amazon Web Services (AWS) is one of the leading firms selling automatic ML methods and pre-trained models to businesses and developers. In an exclusive interview with The Hindu, Kanishka Agiwal, Head – Service Lines, AISPL for AWS India & South Asia, shared his thoughts on ML – its applications, growing adoption in different sectors, function in the future, as well as AWS’ role in building and supporting the ML ecosystem.
The following transcript has been edited for clarity and brevity.
How is ML changing the way businesses operate?
Earlier, ML technology was limited to a few major tech companies and academic researchers. Things began to change when cloud computing entered the mainstream. Compute power and data became more available, and ML is now making an impact across every industry, be it finance, retail, fashion, real estate, and healthcare. It is moving from the periphery to now becoming a core part of every business and industry.
ML is already helping companies make better and faster decisions. When deployed with the right strategies, ML increases agility, streamlines processes, boosts revenue by creating new products and improving existing ones, and enables better, faster decision making. There’s no doubt ML and artificial intelligence (AI) can help companies achieve more.
Do you think the pandemic has accelerated ML adoption?
As often happens in a crisis, companies tend to step back and think more strategically about their future operations. We’ve seen healthcare organisations lean on technology and the cloud to get accurate, trusted information to patients and direct them to the appropriate level of care. Organisations of every size worldwide have been quick to apply their ML expertise in several areas, whether it’s scaling customer communications, understanding how COVID-19 spreads or speeding up research and treatment.
Several areas utilise ML including ML-enabled chatbots for contactless screening of COVID-19 symptoms and to answer queries. Using ML models to analyse large volumes of data to provide an early warning system for disease outbreaks and identify vulnerable populations. Making use of ML in medical imaging to recognise patterns and deriving contextual relationships between genes, diseases and drugs, and accelerating the discovery of drugs to help treat COVID-19.
I’m inspired and encouraged by the speed at which these organisations are applying ML to address COVID-19. At AWS, we have always believed in the potential of ML to help solve the biggest challenges in our world – and that promise is now coming to fruition as organisations respond to this crisis.
What are the different sectors where ML adoption and application will play a crucial role?
If you take some of the largest sectors such as agriculture, healthcare, citizen services, financial inclusion, you’ll notice ML at play. In agriculture, ML is playing a part in farm advisory, crop assaying, pest management, and traceability of crops.
Healthcare and life sciences organisations from the largest healthcare providers, payers, IT vendors, and niche ISVs across the globe are applying AWS ML services to improve patient outcomes and accelerate decision making. Some of the use cases we are seeing include using ML to accelerate the diagnosis of diseases, improve operational efficiency and delivery of care, population health analytics, and to aid scientific discovery. In India, Common Service Centres (CSC) are deploying ML to accelerate delivery of citizen services.
Increasingly, industrial customers across asset intensive industries such as manufacturing, energy, mining, and automotive are using ML to drive faster and better decisions to help improve operational efficiency, quality, and agility. ML services purpose-built for low latency requirements of industrial environments further remove barriers to industrial digital transformation.
How does AWS plan to support the development of India’s ML ecosystem?
Recently, we announced a collaboration with NITI Aayog to establish a Frontier Technologies Cloud Innovation Centre in India. This will bring together public sector stakeholders, startups, and academia to solve critical societal challenges.
Last year, Atal Innovation Mission, NITI Aayog, collaborated with NASSCOM to launch the ‘ATL AI Step Up Module’, with a focus on driving AI education among school students in India. Through AWS’ Educate, students will be able to gain hands-on practical experience on AWS’ ML services, including Amazon SageMaker.
In addition, the Indian Chamber of Food and Agriculture adopted AWS Educate to introduce certificate courses for agricultural engineering students. In March this year, we announced the AWS DeepRacer Women’s League 2021 in India to help foster community learning, which aims to bring together women to gain hands on experience with ML.
Is AWS planning to push more ML applications into a broader array of its businesses?
To meet our customers where they are on their ML journey and help them achieve specific business outcomes, we provide the broadest set of ML and AI services for builders of all levels of expertise. AWS launched more than 250 new capabilities for ML and AI in 2020 alone.
We are building AI Services that allow developers to easily add intelligence to any application without needing ML skills. These services provide ready-made intelligence applications and workflows to personalise the customer experience, identify and triage anomalies in business metrics, image recognition, and automatically extract meaning from documents.
AWS has also launched end-to-end solutions, which don’t require teams to stitch together multiple services themselves.
How do you see ML evolving in the future?
ML represents a unique opportunity for government and organisations to leverage public data for social good. From chatbots to supporting municipal services to contactless tracing of COVID-19, governments can harness ML to stay close to their citizens through deeper experiences. Organisations can better navigate and utilise data for more strategic and timely decisions if they are equipped with the appropriate technology capabilities. Leveraging ML can result in fewer errors and more timely decision making to enable organisations to execute initiatives with accuracy and speed.
Public safety can be improved over a range of possibilities, from ensuring safe roads, to preventing cyber-attacks and responding to natural disasters. ML will provide government organisations with the ability to improve safety across and beyond this spectrum. ML can dramatically streamline operational processes. As a result, it can save time, costs, and other resources so that organisations can focus on what is more important.
With ML, organisations can leverage data to develop and scale revolutionary ideas that result in cutting-edge research, far beyond human capabilities.
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