Machine learning is the science of getting computers to act without being explicitly programmed. It enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop on their own. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome.
Machine learning is functionality that helps software perform a task without explicit programming or rules. Traditionally considered a subcategory of artificial intelligence, machine learning involves statistical techniques, such as deep learning (aka neural networks), that are inspired by theories about how the human brain processes information.
Artificial Intelligence (AI) and Machine Learning (ML) are two very hot buzzwords right now, and often seem to be used interchangeably. They are not quite the same thing, but the perception that they are can sometimes lead to some confusion.
Both terms crop up very frequently when the topic is Big Data, analytics, and the broader waves of technological change which are sweeping through our world.
Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider “smart”. Machine Learning is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves.
Machine learning is powered by algorithmic models that are trained to recognize patterns in collected data (such as logs, speech, text, or images). Because access to lots of training data and computing power are preconditions for success, the cloud (where data storage and high-performance computation are plentiful and can be particularly cost-efficient) is an ideal platform for machine learning.
Examples of machine learning abound in everyday experiences. A very simple example would be the auto-completion of names, keywords, or addresses in a search field, but the same concept can be applied in more complex use cases across multiple industries. For instance, machine learning is used to:
In summary, wherever there is software that performs a labor-intensive task on a scale beyond human capability, machine learning may well be involved.
Using Machine Learning (ML) one can make better decisions, at any scale, when it matters. Bring AI everywhere to everyone using a cloud service for data scientists and developers that helps build intelligent solutions, analyse data, build better models faster and orchestrate the machine learning development lifecycle.
Once belonging to the exotic domains of statistics and data science, machine-learning capability is now widely accessible in the form of open source libraries (TensorFlow), as well as managed services and cloud APIs. For data scientists who want to build “future-proof” models that can move between on-premises and the cloud, or mainstream developers who lack adequate training data and want to bring a pre-built/pre-trained model into their app via a cloud API, using such tools as part of the daily routine is a realistic goal.
Our team of domain / industry experts, advisory consultants, data scientists, and architects bring a comprehensive view of how Machine Learning (ML) Services can help enhance your bottom line and how you can use your strategic IT assets.
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