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Machine Learning Operations

What are Machine Learning Operations?

The practice of Machine Learning Operations involves developing new machine learning models, deploying them into production through repeatable processes, and automating them. Machine Learning Operations are responsible for scaling Machine Learning-driven applications in an organization by defining processes that make Machine Learning development more efficient and reliable.

Machine Learning Operations aims to collaborate and communicate among data scientists and operations professionals to facilitate the creation of Machine Learning products.

Examples of Machine Learning Operation

Machine Learning is used in internet search engines, spam filters, websites that make personalized recommendations, banking software that detects unusual transactions, and many apps on our phones like voice recognition.

Artificial Intelligence and Machine Learning are used behind the scenes to improve our daily lives, make informed business decisions, and optimize operations for some of the world's biggest companies. Here are three outstanding examples:

1. Consumer goods

With the help of Natural Language Processing, Machine Learning, and advanced analytics, Hello Barbie listens and responds to children. ToyTalk's servers receive what is said through a microphone on Barbie's necklace, and a recording of 8,000 lines of dialogue is analyzed to determine the appropriate response. Barbie gets the correct answer from the servers to respond to the child in under a second. Questions such as what their favorite food is are stored and will be used in conversation later.

2. Image recognition

A well-known and widely used example of Machine Learning in the real world is image recognition. An image can be identified as being a digital image by the intensity of its pixels, whether it's a black and white or a color image.

Examples of image recognition include:

  • Identifying a cancerous or non-cancerous x-ray.
  • The act of assigning a name to a photographed face (also known as "tagging" on social media).
  • Segmenting a letter into smaller images can help you recognize handwriting.

Machine Learning is also frequently used for facial recognition within an image. A database of people allows the system to identify commonalities among them and match them to faces. This is commonly used by law enforcement.

3. User and entity behavior analytics Machine Learning

Creating an automated monitoring system that continuously studies logs, this analysis should be used to identify abnormal and unusual behavior and provide security by creating alarms and sending alerts when unusual activity is detected.

 

Why are Machine Learning Operations important?

It is vital for enterprises to use Machine Learning to gain an understanding of trends in customer behavior and operational business patterns, as well as to develop new products. In fact, companies such as Facebook, Google, and Uber make Machine Learning a central part of their operations.

It provides the technology and practices for deploying, monitoring, managing, and governing machine learning in production. Machine Learning Operations are required to scale the number of machine learning-driven applications within an organization.

Machine learning operations have the following benefits:

  • Improved innovation through robust machine learning lifecycle management.
  • Develop reproducible workflows and models.
  • Deploy high-precision models anywhere.
  • Ability to manage the entire Machine Learning process effectively.
  • A management system and control for Machine Learning resources.

Machine Learning Operations vs. Other Technologies & Methodologies

Machine Learning Operations vs. Data Science

Machine Learning uses tools and techniques for creating models that can learn by themselves as they use data. However, Data Science studies data and how to extract meaning from it.

More precisely, Machine Learning is a subset of data science. The main difference between Data Science and Machine Learning is the scope of Data Science. While focusing on algorithms and statistics (like machine learning), it also deals with data processing as a whole. Data Science is better done with Machine Learning because machines cannot learn without data. Machine Learning also refers to techniques used by Data Scientists so that machines learn from data, whereas Data Science is used to extract insights from data. Data Science relies on tools such as Machine Learning and data analytics.

Machine Learning Operations vs. Data Mining

Machine Learning uses predictive models, statistical algorithms, and neural networks to accomplish its objectives. For Data Mining, data warehouses and pattern evaluation methods are used. Their application, concepts, implementation, learning capability, and scope differ significantly.

Data Mining involves finding patterns in an existing dataset (such as a data warehouse). On the other hand, Machine Learning is trained on a 'training' data set, which teaches the computer how to understand data and then make predictions about new data sets.

Machine Learning and Data Mining are fields that have been inspired by one another, yet both have very different ends, as mentioned above.

In Data Mining, rules are extracted from large quantities of data, while in Machine Learning, parameters are taught to a computer. Or, to put it another way, Data Mining is simply a method of researching to determine a particular outcome from a collection of information. In contrast, we have Machine Learning, which teaches a system to perform complex tasks and uses harvested data and experience to become smarter.