Implementing MLOps: 5 Key Steps for Successfully Managing ML Projects

MLOps accelerates the ML model deployment process to make it more efficient and scalable. This is done through automation and additional techniques that help streamline the process. Looking to improve your MLOps knowledge and processes? You’ve come to the right place. In this blog post, we detail the  steps you need to take to build and run a successful MLOps pipeline.

What is MLOps?

MLOps (Machine Learning Operations) is the set of practices and techniques used to efficiently and automatically develop, test, deploy, and maintain ML models and applications and data in production. An extension of DevOps, MLOps streamlines and monitors ML workflows. With MLOps, organizations can ensure ML projects are delivered efficiently and consistently, while overcoming challenges related to deploying, scaling and maintaining ML models, as well as silos between teams in the organization. As a result, with MLOps - ML models can be deployed much faster to production so they can bring business value.

MLOps pipelines support a production-first approach. This means that they are designed to automatically deploy models to production at scale, starting from data collection and all the way to model monitoring.

The Challenges MLOps Solves

MLOps processes ensure ML models are brought to production and can bring business value. Implementing MLOps solves the following challenges:

• Siloed Teams - Before MLOps, data scientists, data engineers and DevOps used to work in silos and with different tools and frameworks. Consequently, the models needed to be technologically converted across the different stages, from the lab to production. This created friction and impacted model quality. MLOps supports collaboration and standardization between these teams, enabling models to be deployed without friction and in a streamlined manner.
• Long Processes - There are multiple steps and phases between the lab and production, like training, testing, security, versioning, hyper parametrization tuning, and more. MLOps enables automating and scaling the process and reducing errors. Without MLOps, this lengthy process takes time and is error-prone, resulting in deployment velocity.
• Feature Access - Generating features is a long, complicated, and computationally heavy process. Feature inaccuracies can impact model accuracy. MLOps solutions that include a feature store can turn feature engineering into a more efficient and accurate process.
• Model Accuracy - Deployed models need to be monitored for drift and other performance issues. MLOps pipelines enable streamlining the process so data professionals are alerted immediately about drift and can retrain these models.

The 5 Steps of Successful MLOps Implementation

When building your MLOps pipelines, there are a number of critical steps to implement. Here are the top MLOps requirements.

Step 1: Data Ingestion and Preparation

The first step of ML management is to gather and prepare the data. This includes collecting, extracting and storing the data, cleaning it, transforming it to a format that can be analyzed and used for training, and more. By taking this step, organizations ensure they have high quality data that is available for model training, feature engineering, and analysis.

MLOps includes the creation and management of data pipelines, as well as automating the data ingestion process, to ensure data is high quality, consistent, and reliable so that it can be used.

Step 2: Model Development

Model development is the process of creating, training, versioning, and evaluating ML models. This includes feature engineering, model selection, hyperparameter tuning, and much more.

Model development is a critical component of MLOps, since it enables ML teams to build pipelines that ensure models are accurate, reliable and scalable, and can be quickly deployed to production. This is all done in an automated manner.

Step 3: Deploying in Production

The deployment stage is the process of making the ML model available for use in production and with real-world data. This includes packaging the model, serving the model to an inference server or framework for handling real-time requests, scalability and load balancing. MLOps CI/CD practices enable automating this process, to ensure faster and more reliable deployment cycles.

Step 4: Continuous Monitoring and Management

Over time, models may begin to drift, which impairs their ability to answer the business’s needs. Therefore, once models are deployed, they still need to be monitored to validate their accuracy, reliability, availability, and performance. This includes tracking metrics such as response time, throughput, error rates, and resource utilization. These metrics are tracked continuously as part of MLOps, to immediately detect performance degradation and retraining the models to fix the issue.

Step 5: Retraining

As the data changes, the model may need to be retrained to maintain its performance. Retraining allows the model to adapt and potentially enhance its performance and accuracy.

Retraining includes new data collection and preprocessing, updating the model, training on the new data, evaluating and deploying to production. This can be done automatically using a retraining MLOps pipeline.

MLOps Frameworks

There are several MLOps frameworks and tools available that can help streamline and automate various aspects of managing machine learning projects.

When looking for an MLOps platform, there are many components to consider. Depending on the business use case, domain, headcount, skill sets and so on, some components maybe be more or less relevant. When it comes to MLOps automation and acceleration, here are four key components to look for:

• Feature Store - A feature store is a component that enables storing, cataloging, and sharing features across the organization. It enables reading data from online and offline resources and data transformation and supports real-time and batch data.
• Real-Time Serving Pipeline - A serving pipeline enables rapidly developing scalable data and ML pipelines with real-time serverless technology. This allows auto-scaling, optimized resource utilization, debugging, and more.
• Monitoring and Retraining Capabilities - Monitoring models for drift and performance degradation, as well as automated retraining to ensure performance, reliability, and consistency.
• CI/CD for ML - The use of CI/CD engines to run automated and continuous processes across code, data, and models.

For example, MLRun is an open-source MLOps orchestration framework that helps organizations automate and manage the ML lifecycle. It provides a unified framework for data ingestion, preparation, model development, deployment, and monitoring. It can be used on any cloud platform or on-premises environment and integrates with the development and CI/CD environment. MLRun also supports feature engineering and real-time serving. For resiliency, security, and management functionality for the enterprise, the Iguazio MLOps Platform offers a completely managed solution.

Conclusion

MLOps implementation can greatly benefit your organization and improve your ML processes. MLOps frameworks streamline steps like data preparation, feature engineering, training, deployment, monitoring, retraining, and more. By automating these processes, MLOps solves common ML challenges and brings models to production efficiently, reliably, and at scale.