Why do customers from all walks of life like to choose Amazon Cloud Services (AWS) machine learning services?
At the AWS re:Invent 2020 held recently, AWS Global Machine Learning Vice President Swami Sivasubramanian (hereinafter referred to as Swami) delivered a several-hour speech on artificial intelligence and machine learning topics, and also revealed the answers to the above questions.
Machine learning: an important way to realize artificial intelligence
If you want to select the hottest IT technology at the moment, I am afraid that the first thing most people think of is AI artificial intelligence.
In 1956, MIT professor John McCarthy first proposed the term "Artificial Intelligence" (AI) at the Dartmouth Conference, which is also widely regarded as a sign of the official birth of artificial intelligence. .
However, what many people did not expect at the time was that for a long period of time, due to various reasons such as lack of data and insufficient computing power, the landing and application of artificial intelligence encountered serious bottlenecks. Many scholars even believe that the wonderful prospects portrayed by artificial intelligence are nothing but flowers in the mirror, and the moon in the water will never become a reality.
However, with the rapid rise of emerging technologies such as cloud computing and big data in recent years, various data volumes have begun to show rapid growth in geometric progression, and computing power has also been greatly improved, which has also become a driving force for the development of artificial intelligence technology. Great driving force. As an important way to realize artificial intelligence, Machine Learning (ML) technology has developed rapidly under this background.
As the saying goes, “Workers must first sharpen their tools if they want to do good things.” By analyzing and designing various algorithms, machine learning services can allow computers to automatically learn from massive amounts of data and find patterns, thereby greatly accelerating artificial intelligence in various industries. The application and landing of the field, so it is deeply loved by developers. This is also the reason why people in the industry often include cloud computing, big data, and machine learning when they talk about artificial intelligence.
AWS: A leader in machine learning
Among the many machine learning service providers, AWS is undoubtedly one of the most remarkable.
From the launch of AI SaaS machine learning services a few years ago, to the official release of the highly acclaimed Amazon SageMaker in 2017, AWS has made rapid progress in the field of machine learning in recent years.
"In the past three years, AWS has delivered more than 200 machine learning functions each year. Among them, in 2020 alone, AWS has added more than 250 machine learning functions. For artificial intelligence technology, these machine learning functions It is very important that AWS has truly released the capabilities of artificial intelligence technology." Gu Fan, general manager of cloud service product management for AWS Greater China, said, "At present, more than 100,000 customers worldwide are using AWS machine learning services. These customers cover All industries, whether it is finance, medical, media, automotive, manufacturing, retail, or sports, are using our machine learning."
Swami pointed out that AWS's machine learning provides customers with a wealth of functions and services. More and more industry customers are using these tools provided by AWS to better develop their own business in various application scenarios. For example, by using the machine learning model provided by AWS, customers can change the training time from the past few days to a few hours, saving a lot of time and energy, and greatly shortening the deployment time, and innovating faster.
Features of AWS machine learning service
Why do so many industry customers favor AWS's machine learning services? Swami believes that the main reason is that AWS's machine learning services have the following characteristics:
1. The breadth and depth of service. Regarding machine learning, AWS's attitude has always been "Right tools for the right job", which means to do the right thing with the right tool, and open the lock with one key. AWS can provide the most suitable services and solutions for what kind of work the customer wants to run, in what kind of scenario, and what kind of tool in the toolbox should be selected. This is also a big advantage of AWS machine learning service in breadth and depth.
2. An open mind. In the fields of cloud computing, artificial intelligence, and machine learning, AWS has always held an open and cooperative mentality, including many tools provided by AWS are very open, and can be very well integrated and compatible with the customer's entire operating environment.
3. The mode of cooperation between AWS and customers. AWS, which regards “customer-centric” as its corporate culture, always follows two principles when serving customers: First, it is better to teach people how to fish than to teach people how to fish. While AWS provides tools for customers, it also hopes to teach customers how to use them. Tools, so as to truly build up their own capabilities; second, when customers need help in engineering gaps, product prototype implementation difficulties, etc., AWS will also help customers quickly solve technical and business problems.
Machine learning drives corporate innovation
In the AWS re:Invent 2020 keynote speech, Swami highlighted four major themes:
1. A solid foundation for machine learning
This foundation includes two things: one is the framework of machine learning, and the other is the underlying computing power infrastructure that machine learning relies on, that is, various CPUs and GPUs. AWS' vision is to put machine learning as a tool in the hands of all companies, not just in the hands of a few large companies. To this end, AWS will do all the support and optimization of the machine learning framework, and spare no effort to leave this option to customers.
2. Create a shortcut to success in machine learning
For many customers, even if the underlying architecture is very solid, it may not be able to fully use machine learning. Enterprises still need a powerful, end-to-end, highly integrated case environment such as Amazon SageMaker to achieve rapid application landing. In terms of machine learning instances, there will always be countless combinations of various computing, storage, and cost requirements. AWS will use Amazon SageMaker's continuous iteration to maximize the ease of use of each step in the machine learning workflow.
3. Let more people join the empowerment of machine learning
As we all know, Amazon SageMaker can provide a great help for data scientists, data development engineers or machine learning development engineers, but in fact, the people who really want to use machine learning are much larger than these groups. These people may not want to learn the difficult technology of machine learning, but this does not mean that they do not use it, so AWS has been working hard to make machine learning services empower more people.
4. Solve customers' actual business problems end-to-end
With the continuous evolution of technology, the scene of machine learning is also expanding. Whether it is an industrial manufacturing scenario or an edge scenario, AWS machine learning services can provide end-to-end packaged integrated solutions.
"In the past few years, machine learning has come a long way, and technical barriers have been greatly reduced. This allows developers to quickly apply machine learning to solve their most challenging problems and strive for the greatest Opportunities.” Swami said, “Especially under the new crown epidemic, our customers need to respond quickly to this ever-changing world. They apply machine learning to create new ways to interact with customers, rethink the way they work and learn, and automate business. Process in order to respond to customer needs faster. For example, the medical industry can use machine learning to track diseases, find new ways to treat and care for patients, accelerate the development of vaccines, etc. The reason they are able to do this is because Their model builders are able to take full advantage of the potential of machine learning and freely invent these technologies. This is also the passion of the AWS team, which is why we continue to drive innovation and introduce new features day after day."