Azure Machine Learning helps customers stay ahead of challenges

Machine learning (ML) is the process of using mathematical models of data to help a computer learn without direct instruction. It’s considered a subset of artificial intelligence (AI). Machine learning uses algorithms to identify patterns within data, and those patterns are then used to create a data model that can make predictions. With increased data and experience, the results of machine learning are more accurate—much like how humans improve with more practice.

The adaptability of machine learning makes it a great choice in scenarios where the data is always changing, the nature of the request or task is always shifting, or coding a solution would be effectively impossible. Machine learning has many applications and the possibilities are constantly expanding. Let’s dive into how Azure Machine Learning is helping individuals, teams, and organisations meet and exceed business goals.

Uncover insight

Machine learning can help identify a pattern or structure within both structured and unstructured data, helping to identify the story the data is telling.

Enhance user experience

Adaptive interfaces, targeted content, chatbots, and voice-enabled virtual assistants are all examples of how machine learning can help optimize the customer experience.

Anticipate customer behavior

Machine learning can mine customer-related data to help identify patterns and behaviors, letting you optimize product recommendations and provide the best customer experience possible.

Improve data integrity

Machine learning is excellent at data mining and can take it a step further, improving its abilities over time

Reduce risk

As fraud tactics constantly change, machine learning keeps pace—monitoring and identifying new patterns to catch attempts before they’re successful.

Lower costs

One machine learning application is process automation, which can free up time and resources, allowing your team to focus on what matters most.

What can machine learning do?

• Helpful in identifying cause and effect between variables, regression algorithms create a model from values, which are then used to make a prediction. Regression studies help forecast the future, which can help anticipate product demand, predict sales figures, or estimate campaign results.
• Often used to spot potential risk, anomaly detection algorithms pinpoint data outside anticipated norm. Equipment malfunction, structural defect, text errors, and instances of fraud are examples of how machine learning can be used to address concern.
• Clustering algorithms are often the first step in machine learning, revealing the underlying structure within the dataset. Categorizing common items, clustering is commonly used in market segmentation, offering insight that can help select price and anticipate customer preferences.
• Classification algorithms help determine the correct category for information. Bearing similarity to clustering, classification is different in that it is applied in supervised learning, where predefined labels are assigned.

Learn how to train, deploy, & manage machine learning models, use AutoML, and run pipelines at scale with Azure Machine Learning. Sign up for our Azure Data Analytics and Machine Learning Bootcamp today!

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