What is MLOps?

Machine learning operations, or MLOps, are a set of processes to standardize and streamline the continuous delivery of high-performing models in production by bringing together machine learning (ML) development and ML deployment.

Making and refining ML models is known as ML development. Deploying machine learning models is releasing them for use in a production setting.

By automating the processes required to move ML models from development to production, MLOps closes the gap between the two methods. This aids in the rapid and effective deployment of ML models and their continued success in industrial settings.

What are the benefits of MLOps?

The value of MLOps lies in the fact that it enables enterprises to:

  • MLOps streamlines the process of bringing ML models from development to production, allowing faster deployment. This facilitates the rapid deployment of ML models, which provides an advantage to organizations.
  • MLOps aids businesses in enhancing the quality of their machine learning (ML) models by ensuring that they are deployed in a production environment consistent with the training environment. Doing so reduces the likelihood that our models would drift over time, losing accuracy as the underlying data distribution shifts.
  • By automating the processes of managing and monitoring ML models in production, MLOps enables businesses to cut down on the high cost of ML operations. Employees can thus devote more time to other projects, such as creating new ML models.

So, how exactly does MLOps function?

Continuous integration and delivery (CI/CD) pipelines are commonly used to implement MLOps. Building, testing, and releasing software applications are all processes that can be automated with the help of CI/CD pipelines.

The following are examples of typical steps in a CI/CD pipeline for MLOps:

  • The ML model is trained using data collected in the past.
  • The ML model is tested by being compared against a holdout dataset.
  • The ML model is put into production, known as “deployment.”
  • Keep an eye on the ML model to ensure it’s holding up in production.

You can manually or automatically start the CI/CD workflow. For instance, when a new version of the machine learning model is learned, the pipeline could be activated.

What tools are used for MLOps?

For MLOps, you may choose from a variety of useful instruments. Common equipment includes:

  • A collection of tools known as ML frameworks is available to train and deploy ML models. The ML frameworks TensorFlow, PyTorch, and scikit-learn are among the most widely used.
  • Cloud computing platforms offer the necessary infrastructure and services to install and manage ML models in production. Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure are just a few of the most well-known cloud computing platforms for MLOps.

MLOps systems are:

  • A centralized hub for building.
  • Releasing.
  • Maintaining machine learning (ML) models.

Kubeflow, MLflow, and Prefect are examples of well-liked MLOps systems.

How to Begin Using MLOps

There are a few things you can take to kick off your MLOps experience:

Choose the right tools: MLOps equipment comes in a variety of forms. Pick the equipment that suits your requirements and budget.

Set up a CI/CD pipeline: The process of bringing ML models from development to production can be automated with the help of a CI/CD pipeline.

Monitor your ML models: Keep an eye on how well your production ML models are doing.

Conclusion

The MLOps set of techniques aims to standardize and streamline the continuous delivery of high-performing models in production by bringing together ML development and ML deployment. Businesses can benefit from MLOps by speeding up the deployment of ML models, increasing the quality of those models, and decreasing the cost of ML operations. You can do various things to get started with MLOps, such as selecting the appropriate tools, establishing a CI/CD pipeline, and monitoring your ML models.

Machine learning operations, or MLOps, are a set of processes to standardize and streamline the continuous delivery of high-performing models in production by bringing together machine learning (ML) development and ML deployment.

Making and refining ML models is known as ML development. Deploying machine learning models is releasing them for use in a production setting.

By automating the processes required to move ML models from development to production, MLOps closes the gap between the two methods. This aids in the rapid and effective deployment of ML models and their continued success in industrial settings.

What are the benefits of MLOps?

The value of MLOps lies in the fact that it enables enterprises to:

  • MLOps streamlines the process of bringing ML models from development to production, allowing faster deployment. This facilitates the rapid deployment of ML models, which provides an advantage to organizations.
  • MLOps aids businesses in enhancing the quality of their machine learning (ML) models by ensuring that they are deployed in a production environment consistent with the training environment. Doing so reduces the likelihood that our models would drift over time, losing accuracy as the underlying data distribution shifts.
  • By automating the processes of managing and monitoring ML models in production, MLOps enables businesses to cut down on the high cost of ML operations. Employees can thus devote more time to other projects, such as creating new ML models.

“MLOps is the unsung hero in the realm of artificial intelligence. It acts as a silent force knitting the entire practical application of machine learning, from experimentation to production, smoothly. Additionally, its strength lies in mainly automating tasks like model training, version control, and deployment, thus ensuring scalability and effective monitoring. Basically, see MLOPs as a sturdy bridge that fills the gap between innovation and practical implementation of AI.” – Vrushali Prasade, Co-founder & Co-CTO at Pixis. 

So, how exactly does MLOps function?

Continuous integration and delivery (CI/CD) pipelines are commonly used to implement MLOps. Building, testing, and releasing software applications are all processes that can be automated with the help of CI/CD pipelines.

The following are examples of typical steps in a CI/CD pipeline for MLOps:

  • The ML model is trained using data collected in the past.
  • The ML model is tested by being compared against a holdout dataset.
  • The ML model is put into production, known as “deployment.”
  • Keep an eye on the ML model to ensure it’s holding up in production.

You can manually or automatically start the CI/CD workflow. For instance, when a new version of the machine learning model is learned, the pipeline could be activated.

What tools are used for MLOps?

For MLOps, you may choose from a variety of useful instruments. Common equipment includes:

  • A collection of tools known as ML frameworks is available to train and deploy ML models. The ML frameworks TensorFlow, PyTorch, and scikit-learn are among the most widely used.
  • Cloud computing platforms offer the necessary infrastructure and services to install and manage ML models in production. Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure are just a few of the most well-known cloud computing platforms for MLOps.

MLOps systems are:

  • A centralized hub for building.
  • Releasing.
  • Maintaining machine learning (ML) models.

Kubeflow, MLflow, and Prefect are examples of well-liked MLOps systems.

How to Begin Using MLOps

There are a few things you can take to kick off your MLOps experience:

Choose the right tools: MLOps equipment comes in a variety of forms. Pick the equipment that suits your requirements and budget.

Set up a CI/CD pipeline: The process of bringing ML models from development to production can be automated with the help of a CI/CD pipeline.

Monitor your ML models: Keep an eye on how well your production ML models are doing.

Conclusion

The MLOps set of techniques aims to standardize and streamline the continuous delivery of high-performing models in production by bringing together ML development and ML deployment. Businesses can benefit from MLOps by speeding up the deployment of ML models, increasing the quality of those models, and decreasing the cost of ML operations. You can do various things to get started with MLOps, such as selecting the appropriate tools, establishing a CI/CD pipeline, and monitoring your ML models.

Prathamesh Ingle is a Mechanical Engineer and works as a Data Analyst. He is also an AI practitioner and certified Data Scientist with an interest in applications of AI. He is enthusiastic about exploring new technologies and advancements with their real-life applications

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