In the information age, the concept of Machine Learning has been in vogue for several years, as its impact on our daily lives is increasingly present, whether it is the use we have on our musical or movie preferences, voice recognition, or the automation of numerous intelligent processes with cloud technology,including the application of Deep Learning models to resemble human reasoning.
So, let's take a closer look at why machine learning is so important at the enterprise level:
What is Machine Learning?
Machine Learning refers to the ability of machines to learn from data on their own by means of predictive models, whether structured or unstructured. In this way, systems that include machine learning are able to automate many processes without requiring human intervention and without being specifically programmed to do so, even allowing predictions by focusing on classification or clustering to identify patterns or anomalies.
What is Deep Learning?
Deep Learning is a subcategory that makes up Machine Learning and is designed to allow systems to be able to train themselves in the same way that a human can draw their own conclusions, creating a kind of artificial neural network that allows a much deeper application of use.
What are the uses of Machine Learning? Examples:
Some of the most common applications based on Machine Learning that we can find are:
- Fraud Detection: Any company belonging to the financial, telephone or similar sector can take advantage of technology based on Machine Learning for the automatic detection of fraud in real time, unusual behavior by users, systems or banking environments that may violate the security policies defined by the company.
- Recommendations: On-demand movie, video game or music platforms leverage machine learning technology to make personalized recommendations or offers to their customers based on their users' data, preferences, location or history.
- IoT: All the technology, gadgets or virtual assistants that we can find in a Smart home, including facial or voice recognition can be leveraged by ML for different uses such as security, analysis or predictability.
- Predictive Modeling: Learning from data will allow corporate level prediction of seasonal sales, stock requirements, forecast energy demand or future trends, we must take into account that any company has a lot of information from users: age, average ticket, contracted services, recurrence ... etc. to achieve this goal.
5 Advantages of Machine Learning applied to the company:
- Cost Reduction
- Process automation
- Increased security
- More accurate business intelligence
- Optimize customer service
How does Machine Learning influence cloud technology?
Leveraging cloud technologies based on Data&IA allows to generate a very broad spectrum of services aimed at getting a much more effective and accurate performance by providing a holistic view on the data leveraging Machine Learning, such as:
- Cognitive computing: based on natural language processing models or data mining to generate correlations, patterns or associations to improve decision making by simulating human thinking and even detecting emotions.
- Bots and Virtual Assistants: the use of machine learning together with artificial intelligence allows defining learning, reasoning and self-correction processes automatically to generate a language and iterative communication with users.
- IT Security: There is a wide range of products and services whose technology is supported by Machine Learning systems for the detection of anomalous behavior and data flows in real time, in order to avoid security breaches.
Do you know some of Microsoft's Machine Learning technologies?
Data scientists, engineers or developers working with Machine Learning on a daily basis can find some of the most popular ML solutions and tools on the market in the Azure cloud such as:
- Azure Machine Learning: Microsoft's managed ML platform that allows you to train, deploy and manage models in Azure with Python, Tensor Flow and CLI among others.
- Azure Cognitive Services: Allows you to create intelligent applications leveraging natural language processing through a set of pre-generated APIs such as REST and SDKs among others.
- Azure Databricks: The data analysis platform and real-time workflows running on Apache Spark that allows high-speed data processing.
- SQL Server Machine Learning Services: Allows you to train and deploy models in SQL Server running on local servers as in a virtual machine in the cloud.