Artificial Intelligence (AI) and Machine Learning (ML) are two buzzwords that became quite popular during this ongoing pandemic. Nowadays, it is rather common for us to hear businesses and organizations talk about their adoption of AI and ML in their respective fields. What really are AI and ML, and how can they be of help to grow our businesses and organizations?
AI is any technique that enables computers to mimic human intelligence using logic, if-then statements, and Machine Learning, including Deep Learning (DL). On the other hand, ML is a subset of AI that uses machines to search for patterns in data to build logic models automatically. Further, DL is a subset of ML composed of deeply multi-layered neural networks that perform tasks like speech and image recognition. Oftentimes, we will see the term AI/ML denoting an important evolution in the field of Computer Science and data processing that is key in digitally transforming different industries.
AI/ML has become influential in industries like healthcare and life sciences, industrial and manufacturing, financial services, retail, and media and entertainment. The combination of AI/ML is believed to be key in making enterprises future ready by creating seamless experiences for the end consumers using voice, virtual reality, and connectivity. And among the many companies embarking on the use of these new technologies, Amazon is one example where AI/ML has a significant business impact.
For enterprises who are looking into the use of AI/ML, it can play an important role in (1) enhancing the customer experience; (2) better and faster decision-making; (3) improving business operations; and (4) introducing new products and services.
Amazon Web Services (AWS) is democratizing Machine Learning for everyone, both developers and organizations, manifested into three (03) layers: AI Services, ML Services, and ML Frameworks & Infrastructure.
“For businesses, Machine Learning opens new revenue channels. They can now use the data that they collected and have to increase their competitive advantage. It can also improve operational and financial efficiency to make organizations more productive to keep up with the dynamic market conditions. Lastly, with Machine Learning, we can detect and respond to business risks,” according to Donnie Prakoso, Senior Developer Advocate at AWS during a recent media briefing.
To simplify the ML process end-to-end, AWS has Amazon SageMaker. Launched in 2017, Amazon SageMaker is a cloud machine-learning platform that enables developers to create, train, and deploy machine-learning models in the cloud. It also enables developers to deploy ML models on embedded systems and edge devices (hardware that controls data flow at the boundary between two networks). When used with SageMaker Studio, developers can minimize writing codes and reduce the overall cost of running ML workloads.
“Machine Learning is the future. Being able to learn how to utilize ML to solve business problems is the first step. But leveraging the power to create startups, to turn ideas into something that customers use and appreciate is the next step and level,” said Joshua Arvin Lat, Chief Technology Officer of NuWorks Interactive Labs.
Lat, who also happens to be an AWS Machine Learning Hero, is the author of Machine Learning with Amazon SageMaker Cookbook and started his personal journey into Data Science and Machine Learning some eight (08) years ago. He also said that over these past few years, the number of Filipinos taking on the Machine Learning journey has increased, thanks to the availability of more resources and references now compared to yesteryears. AWS for one has built a vast amount of learning resources aimed at developing solutions for real-world problems. He further added that a developer can now build an AI-powered application in just 24 hours using the proper resources and tools like AWS and Amazon SageMaker.