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Recently, machine learning (ML) has become an increasingly essential component of big data analytics, business intelligence, predictive analytics, fraud detection, and more. Because there is a plethora of methods and tools businesses can use to analyze their data, companies must select an ML approach that minimizes cost and maximizes efficiency. The concept of machine learning operations (MLOps) has emerged from big data analytics as that solution.

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Machine learning operations is a way to scale large ML projects. The job of any data scientist is to figure out what data can teach them about their business and help improve it, but MLOps takes that idea one step further by applying deep learning on top of large-scale datasets. It involves the use of methods, systems, algorithms, and processes for improving data-driven decision-making, and value generation through machine learning.

This area of study combines data mining, AI, analytics, and big data with automation to create a self-managing system capable of handling incredibly complex tasks.

ML is being used for a wide range of processes and can benefit those involving predictions or simulations. Companies are employing machine learning to optimize their operations, gain a competitive edge, and drive revenue. Here are some use cases of machine learning in business.

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Alteryx is a California-based computer software company with a development facility in Broomfield, Colorado. The products of the company are used in data science and analytics.

Dataiku is an AI and ML company founded in 2013, which has offices based in New York City and Paris, France. It provides Data Science Studio (DSS) with a focus on cross-discipline collaboration and usability.

DataRobot is a Boston, Massachusetts-based platform for augmented data science and machine learning. The platform automates critical tasks, allowing data scientists to work more effectively and citizen data scientists to more easily develop models.

RapidMiner is headquartered in Boston, Massachusetts. Data preparation, machine learning, deep learning, text mining, and predictive analytics are all offered through the companys integrated ecosystem.

RapidMiner products include RapidMiner Studio, RapidMiner Auto Model, RapidMiner Turbo Prep, RapidMiner Go, RapidMiner Server, and RapidMiner Radoop.

MathWorks is headquartered in Natick, Massachusetts. The companys two flagship products are MATLAB, which offers an environment for scientists, engineers, and programmers to analyze and display data and build algorithms, and Simulink, a graphical and simulation environment for model-based design of dynamic systems.

MATLAB and Simulink are widely used in the aerospace, automotive, software, and other industries. Polyspace, SimEvents, and Stateflow are some of the companys other products.

There are numerous risks involved when it comes to implementing new, cutting-edge technology like machine learning in business operations, including:

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The data required to train ML algorithms can be quite large. Training models often require hundreds of thousands or even millions of instances to identify meaningful patterns.

Training a deep neural network for object recognition, for example, requires images of tens of thousands of labeled objects, and training a natural language processing system means downloading gigabytes worth of data.

For most organizations, its unfeasible to simply push all that data into production and let a model run until it finishes, and many business processes dont allow for taking things offline in order to retrain.

By combining operations and machine learning, developers can build applications that can continuously learn from new data as theyre being created. Not only does an MLOps approach enable faster time-to-market with improved accuracy, it also has big implications for forecasting, anomaly detection, predictive maintenance, and more.

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MLOps: What Is It and Why Do We Need It? - CIO Insight