What is MLOps vs DevOps?

Unlike most things related to model monitoring, the difference between DevOps and MLOps is relatively simple. Development and operations (DevOps) combines two critical disciplines: software development and IT management. Ideally, the DevOps framework expedites the development of new products or projects and simplifies the maintenance process for existing deployments. Although DevOps is fairly new, it has already brought immense value and has become widely accepted. Some benefits of DevOps include: 

• Helping teams release deliverables more frequently and faster.
• Enabling teams to close feedback loops efficiently and quickly.
• Helping eliminate silos and increase collaboration.

MLOps, or machine learning (ML) operations, dictates ML-specific best practices for developing ML models, monitoring their progress, and retraining them for optimization. In the past, there was a concerning lack of structure for the ML lifecycle which forced ML teams to solely rely on DevOps principles for direction. MLOps was created to solve this issue and provide concrete processes as part of a model monitoring framework.

So, how are MLOps and DevOps related? Will one replace the other? Will DevOps disappear?  We will explore answers to these questions and more in the next few sections. 

Will MLOps replace DevOps?

No, MLOps is not intended to replace DevOps. MLOps applies DevOps principles and practices to machine learning requirements and training ML algorithms. Because machine learning is so different from software, both need their own lifecycles. In short, bugs in ML models can’t be fixed the same way developers fix code.

The complexities involved with machine learning require ML teams to continually monitor all aspects of the ML lifecycle. This is especially important because the quality of ML model predictions tend to degrade overtime. Once model drift or degradation is detected, ML teams follow MLOps practices to perform root cause analysis and gain insight into the model’s behavior. 

Various benefits can be gleaned from MLOps, including: 

• Improved observability: MLOps provides greater transparency into ML workflows and enables teams to easily detect negative changes in model behavior either before or during production.
• Greater compliance: Because regulations surrounding machine learning are ever-evolving, it can be difficult to keep up. MLOps best practices encourage a model governance framework, making it easier to maintain compliance with new AI regulations.
• Fewer (or no) bottlenecks: MLOps fosters greater collaboration within data and operations teams. This results in increased knowledge sharing and eliminates siloes which reduces errors during the building, testing, monitoring, and deployment phases of the machine learning process. 

In the end, MLOps enables teams to develop and deploy more reliable models and helps AI reach its full potential. 

Is MLOps part of DevOps?

Yes, MLOps is an additional phase of the DevOps lifecycle that was developed to create a standardized methodology and lifecycle for machine learning processes. Because of their similarities, ML and DevOps are closely related in many ways. In fact, many MLOps processes are informed by DevOps. The overarching goals of DevOps and MLOps are rather similar: 

• Foster greater collaboration between teams 
• Utilize continuous monitoring 
• Encourage continuous improvement

Ultimately, machine learning and DevOps approaches happily coexist.

Implementing MLOps

Because it is such a new concept, MLOps is not completely standardized yet. This means many organizations attempt to create their own processes for the MLOps lifecycle and base them on DevOps procedures. However, cobbling MLOps framework from scratch is not the most efficient approach.

Utilizing an AI observability platform eliminates the guesswork and helps organizations solve various ML operational challenges. But what exactly is AI observability? In simple terms, it empowers MLOps practices by monitoring and explaining model performance at every stage of the ML lifecycle.

An AI observability platform is one of the most critical MLOps tools because it: 

• Streamlines operations and offers centralized model monitoring, so teams can easily evaluate model performance. This eliminates the utilization of disparate monitoring tools, which hinders explainability and compromises data.
• Provides a unified dashboard that offers model monitoring alerts and actionable insights into model behavior. This helps teams perform root cause analysis and tackle issues at any stage of the ML lifecycle.
• Prevents ML teams from wasting their time manually tracking model performance and issues.
• Provides explainable AI that empowers ML teams to strengthen their model’s prediction accuracy.

‍Try Fiddler today to improve your MLOps practices.