Machine learning operations (MLOps)

As per Gartner, only 50% of machine learning models reach production, leading to an efficiency challenge for many organizations. To improve the rate of machine learning model deployment, many organizations have begun to adopt MLOps.

MLOps is the concept of applying DevOps principles to ML (Machine Learning) workflows and helping increase the deployment rate of ML models to production by leveraging a set of practices, processes, and tools per the used case scenario.

The MLOps process includes several stages: data preparation, model training, testing and validation, deployment, monitoring, and feedback loops. The main goal of MLOps is to increase the machine learning process’s efficiency, accuracy, and reliability while ensuring the models perform as expected in real-world environments.

MLOps typically involves using various processes, tools, and technologies, such as version control systems, containerization platforms, and continuous integration and deployment pipelines, to automate and streamline the machine learning workflow. The tools and practices enabled by MLOps can help reduce the risk of errors, improve collaboration, and ensure that machine learning models are updated and optimized for better performance over time.

Let us look into the 7 key benefits of implementing MLOps

1. Increases productivity

MLOps practitioners leverage various tools and practices designed to help streamline and automate machine learning development and deployment processes. It can include automating data preprocessing and feature engineering, managing model training and evaluation, and deploying and monitoring models in production.

By implementing tools designed to automate and standardize development and deployment processes, organizations can reduce the time and effort required to develop and deploy machine learning models, allowing data scientists and engineers to focus on higher-level tasks.

This results in the faster and more efficient delivery of high-quality machine learning models, ultimately driving business value and improving productivity.

2. Faster deployment and easy monitoring

MLOps methodologies can help organizations accelerate modeling processes. They can also help facilitate machine learning models’ seamless construction and deployment by helping leverage automated systems.

Commonly used MLOps tools can help with automatic system monitoring systems, which can be used in the continuous monitoring of models in production, allowing for quick identification and resolution of any issues. These tools help organizations improve the speed and quality of their machine learning deployment, leading to increased productivity and better outcomes.

3. Budget and cost management

Implementing MLOps can help ensure efficient resource usage and cost control by using tooling designed to monitor usage patterns, identify bottlenecks, and scale resources based on demand. These tools can help estimate and track costs before, during, and after experimentation.

4. Reproducibility and versioning

Organizations can leverage MLOps policies to enable a structured approach for practitioners and data scientists to track and manage changes, enable versioning, and provide a history of edits and versions created.

Versioning aids in deploying models in production and enhances the reliability and scalability of machine-learning models.

5. Reliability

Leveraging MLOps methods and tools can result in more reliable ML pipelines by minimizing the scope of human error and providing real-time data insights. MLOps can improve the dependability of machine learning models, ensuring their consistency and accuracy in production.

By continuously monitoring model performance and dependencies, teams can promptly identify and address issues, increasing the models’ reliability.

6. Collaboration

MLOps best practices and policies are meant to break down silos between teams, allowing them to collaborate, share data more efficiently, and seamlessly integrate their workflows. This collaboration can lead to faster and more efficient model deployment and a more streamlined machine-learning process.

With MLOps, organizations can ensure that their machine learning projects are connected and working together efficiently, leading to better outcomes and improved productivity.

7. Monitorability

Through MLOps, organizations can get insights into model performance and retrain the model continuously to ensure it gives the most accurate output. MLOps enables practitioners to do this by providing guidance on best practices for successfully implementing automated monitoring systems. These monitoring systems facilitate constant model monitoring and allow stakeholders to identify any issues or anomalies that may arise quickly.

Identifying problems and irregularities can help to improve model performance and reduce downtime, leading to better outcomes and a more efficient deployment process.

With MLOps, organizations can ensure that their machine learning models always perform optimally, improving productivity and better business results.

MLOps with Fractal

Fractal has a successful track record of delivering ML projects for clients leveraging our in-house MLOps methodology. Our secret lies in using a reliable methodology designed to remove uncertainty, foster consistency, and enable the quick realization of value, as well as continuous packaging, validation, and deployment of models to production—partnering with us for MLOps implementation grants you access to this proven methodology.

Getting started with MLOps

Organizations looking to implement MLOps can leverage the same proven methodology we use in-house to deliver projects for clients successfully.

Contact us to get started.