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This article mainly talks about "what benefits can MLOps bring". Interested friends might as well take a look. The method introduced in this paper is simple, fast and practical. Now let the editor take you to learn "what benefits can MLOps bring?"

MLOps is still a relatively new concept, but the demand for it is growing day by day. Around 2018, after a speech held by Google, industry professionals spoke publicly for the first time about the need for integrated machine learning life cycle management in industrial operation (production).

The practice of introducing machine learning models into practical business is not limited to the training of data preparation, development, neural networks or other machine learning algorithms. From validating datasets to testing and deploying datasets in a reliable big data production environment, the quality of product solutions is affected by many factors.

This means that the actual results of prediction or classification depend not only on the neural network architecture and machine learning methods proposed by data scientists, but also on how the development team implements the model and how administrators deploy the model in a clustered environment. The quality of the input data, the source, channel, and frequency of the data received are also important, which fall within the responsibility of the data engineer.

In the process of developing, testing, deploying and supporting machine learning solutions, multidisciplinary experts will encounter many organizational problems and technical obstacles in the interaction, which not only prolongs the time of product creation, but also reduces the actual value that the product brings to the business.

In order to remove these obstacles, the concept of MLOps came into being. Like DevOps and DataOps, MLOps aims to improve automation and the quality of industrial machine learning solutions, while taking into account regulatory needs and business interests.

Therefore, MLOps is a cultural concept and application example that combines system development and operation support (including integration, testing, release, deployment, infrastructure management, etc.) to realize machine learning system life cycle composite and automatic management.

It can be said that MLOps extends CRISP-DM methodology (CRISP-DM cross-industry data mining standard process) with agile methods and technical tools to automate operations including data, machine learning models, code, and environment.

These tools include Cloudera's data science workbench, ClouderaData Science Workbench, and putting MLOps into practice will help data scientists avoid common pitfalls and problems in the classic phase of CRISP-DM.

Ten benefits of MLOps to Business and data Science

Of all the benefits of implementing MLOps, the most prominent is the agile approach in the deployment details of the machine learning industry:

Through reliable and effective machine learning life cycle management, time is reduced and high quality results are obtained.

Continuous development (CD), continuous integration (CI), and continuous training (CT) methods and tools ensure the repeatability of workflows and models.

It is easy to deploy high-precision machine learning models anytime, anywhere.

The integrated management system can continuously monitor machine learning resources.

Remove organizational barriers and bring together the experience of multidisciplinary machine learning experts.

Therefore, the following machine learning operations can be optimized using MLOps:

Unify the machine learning model and the release cycle of associated software products.

Automated testing of machine learning components, such as data validation, testing the machine learning model itself, and its integration into product solutions.

Practice agile principles in machine learning projects.

Support machine learning models and datasets in CI, CD, and CT systems.

Use machine learning model to reduce technical debt.

It is worth noting that the practice of MLOps should be independent of language, framework, platform, and infrastructure. From a technical point of view, the overall architecture of the MLOps system should include the collection and aggregation of big data platform, applications for modeling, analyzing and preparing data for machine learning, tools for performing calculations and analysis, and tools for automatically moving machine learning models and their lifecycle associated data and software products.

As a result, the tasks of data scientists, data engineers, machine learning experts, architects and developers of big data solutions, and DevOps engineers using a unified and efficient pipeline are expected to be partially or fully automated.

At this point, I believe you have a deeper understanding of "what benefits MLOps can bring". You might as well do it in practice. Here is the website, more related content can enter the relevant channels to inquire, follow us, continue to learn!

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