The Importance of MLOps in Successful Machine Learning
Machine learning (ML) adoption has grown exponentially in companies worldwide. However, most ML projects face a major...

10 MIN READ

October 14, 2024

10 MIN READ
The Importance of MLOps in Successful Machine Learning

Machine learning (ML) adoption has grown exponentially in companies worldwide. However, most ML projects face a major obstacle: they don’t make it to production. 

Why? What’s the roadblock?

This article dives into that critical question and discusses how companies can extract real value from their ML initiatives. It all stems down to the practice of MLOps.

Let’s break it down.

The Challenge of Machine Learning Projects

The Experimental Nature of Data Science

How to Form an MLOps Strategy (Starting Steps)

Step #1. Choosing the right tools

Step #2. Create a versioning strategy with GIT

Step #3. Create a standard environment for model deployment

Step #4. Build a CI/CD pipeline of templates

Step #5. Implement continuous model monitoring

Step #6. Establish a feature store

Step #7. Incorporate data Versioning and ensure model reproducibility

Conclusion

The Challenge of Machine Learning Projects

One of the biggest challenges companies face is that most machine learning projects never make it to production; meaning they fail to be fully implemented within the organization’s systems.

This happens for several reasons: the complexity of the models, inadequate infrastructure, and a lack of seamless integration between data science and IT operations. As a result, the data science team often becomes a hub for experimentation and testing but struggles to efficiently bring those models into actual production.

data science team and machine learning team finding a solution

The Experimental Nature of Data Science

Many companies still view data science as something “experimental.” 

Data scientists often work in isolation, developing models in a controlled environment that doesn’t reflect the company’s real-world operations. As a result, these models tend to prioritize statistical performance, like accuracy, but overlook practical considerations such as costs and response times (latency). This disconnect between the development environment and operational needs creates roadblocks when implementing and scaling machine learning models effectively.

MLOps and Learning From DevOps Practices

To address these challenges, data science teams can benefit from adopting practices used by software engineering teams, particularly those that have boosted productivity and efficiency, such as DevOps. 

DevOps transformed the way we develop, test, and deploy software by bridging the gap between development and operations. Similarly, MLOps introduces continuous integration and continuous delivery (CI/CD) for machine learning models, resulting in smoother deployment and scalability.

The Need for Continuous Monitoring

Once an ML model goes into production, it needs to be constantly monitored. Real-world conditions are subject to change, and models need to be adjusted accordingly to continue delivering value. Without continuous monitoring, models can degrade, leading to poor business decisions and loss of value.

Extracting Real Business Value with MLOps

To truly extract value from machine learning initiatives, companies need to embrace MLOps practices. 

This approach goes beyond just continuous integration and delivery of models; emphasizing collaboration between data scientists and software engineers. Best practices include: unit testing to validate data inputs, automating manual processes, and continuously monitoring models in production to ensure their effectiveness and reliability.

data scientist staring at computer screen, analyzing data

How to Form an MLOps Strategy (Starting Steps)

Implementing an MLOps strategy in your company may seem daunting, but by following a few key steps, you can build an efficient and productive process. Here are the seven essential steps to help you get started with an MLOps strategy:

Step #1. Choosing the right tools

The first step is selecting the right tools to support your MLOps strategy. Popular options include MLFlow, Azure Machine Learning, Kubeflow, and Amazon SageMaker. These tools assist with experiment management, model tracking, pipeline orchestration, and deployment automation.

Step #2. Create a versioning strategy with GIT

Versioning is crucial for traceability and change management. Use GIT to version code for models, data preprocessing scripts, and notebooks. This ensures that all changes are documented and you can revert to previous versions when needed.

Step #3. Create a standard environment for model deployment

Establish a standard environment for deploying machine learning models. The use of containers, like Docker, is highly recommended, as it allows the creation of isolated and replicable environments. Define base images that include all the dependencies required to run the models.

Step #4. Build a CI/CD pipeline of templates

Automate your machine learning models’ continuous integration (CI) and delivery (CD) processes. Set up pipelines that handle testing, validation, and deployment automatically. Tools like Jenkins, GitLab CI/CD, and Azure DevOps are popular options for streamlining this automation.

Step #5. Implement continuous model monitoring

To ensure your models continue delivering value after deployment, it’s crucial to establish continuous monitoring. Leverage monitoring tools that can track key performance metrics, detect deviations in behavior, and signal when retraining is required. Check out tools like Prometheus and Grafana for this purpose.

Step #6. Establish a feature store

A feature store is a centralized repository for the features (variables) used in machine learning models. This promotes feature reuse, ensures consistency between development and production environments, and provides traceability of the features employed in models.

Step #7. Incorporate data Versioning and ensure model reproducibility

Like code, the data used for training machine learning models must be versioned. This practice guarantees the reproducibility of experiments and facilitates model auditing. Tools like DVC (Data Version Control) or Delta Lake are recommended to support data versioning.

Conclusion

MLOps is critical for transforming data science from an experimental endeavor into a productive and efficient operation. By embracing MLOps, organizations can effectively deploy, monitor, and continuously improve their machine learning models for sustained value and impact.

Now is the time for businesses to take MLOps seriously to maximize success and create real business outcomes through AI.

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