Learn more about MLOps: Discussion on the definition, concepts and applications of MLOps

What is MLOps?

The discipline of delivering machine learning models through repeatable and efficient workflows is called Machine Learning Operations (MLOps).

MLOps is similar to the DevOps of the software development life cycle and aims to continuously deliver high-performance ML applications into large-scale production. It focuses specifically on the unique needs of ML within existing SDLC and CI/CD programs to create a new lifecycle that exists alongside them. Doing so can lead to more efficient workflows and better results for ML.

MLOps methods improve the quality of machine learning and deep learning models, simplify management processes, and enable automated deployment. Models are more easily aligned with business needs and regulatory requirements.

Machine learning (ML) has a unique life cycle that is different from traditional software. The core of ML is data, not application activity. Therefore, the focus of the code is on data management. The ML system is an open and constantly evolving system. Once the model is distributed, the task has just begun. For optimal performance, models in production need to be regularly monitored, retrained, and redeployed in response to changing data signals. This process is ongoing and requires constant optimization and adjustment to ensure the accuracy and reliability of the model. Through continuous iteration and improvement, ML systems can be kept efficient and effective to adapt to changing needs.

The ML life cycle is the continuous integration, development and delivery process of machine learning models. The model is continuously cycled through three key stages of development, deployment, and operation to continuously adjust and optimize its performance in production.

How does MLOps work?

MLOps provides a solution that helps data scientists, machine learning engineers, and application developers solve problems similar to those in software engineering. It is similar to DevOps and solves the challenges of data scientists delivering models to production environments and maintaining them. MLOps focuses on collaboration, enabling teams to deliver value to customers. Through MLOps, teams can better manage the life cycle, version control, and deployment of models, thereby improving model availability and reliability. This approach also ensures model consistency and repeatability, allowing teams to roll out new features and improvements faster. In summary, MLOps and DevOps have much in common in solving software engineering problems, providing similar advantages to data scientists.

MLOps includes all the essential components and the ability to integrate them all—from data sources to compliance tools.

To build repeatable machine learning (ML) models, we can package the modeling code, dependencies, and runtime requirements. By enabling reproducible ML, we can reduce the cost of shipping and maintaining model versions. After packaging, large-scale deployment becomes quite easy. This stage provides repeatability and is one of several fundamental steps in the MLOps journey.

The goal of MLOps is to support the entire life cycle of machine learning models through a consistent set of principles. These principles include the implementation of source code control, maintenance of a model version registry, standardized packaging, creation of validation checklists, definition of deployment methods, and development of monitoring protocols.

Because monitoring pipelines notice data drift, mature MLOps technology allows enterprises to determine when models need to be retrained. It can also help determine which data, model versions, and code bases were used to make a specific prediction.

Benefits of MLOps

Deliver more models faster: The speed with which you can deploy and iterate on models gives you a competitive advantage in machine learning. MLOps’ repeatable, scalable approach brings machine learning to production faster.

1. Maximize ROI: If MLOps exists, model and infrastructure monitoring becomes a piecemeal job. You can quickly monitor and adjust your models and infrastructure to gain additional profit margins while reducing infrastructure spending.

2. Improve business competitiveness: In order to stay ahead of the competition, the model needs to be adjusted frequently. If you use MLOps to build automated pipelines and workflows, you can reduce the overall cost of ML while remaining competitive.

3. Flexible integration: If you want to use the most cutting-edge machine learning technology, you can use MLOps to simply maintain integration with various data science tools.

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