MLOps Myths That Are Hampering Your Productivity


It is vital for business executives to use data insights for company expansion; research reveals only 50 per cent of AI proofs-of-concept are scaled to production. The secret to developing scalable, production-ready AI solutions lies in MLOps, but this requires debunking myths around MLOps in the first place.

Machine learning (ML) model metrics are outlined to measure the model’s performance. An ML model performing outstandingly during the testing phase might fail altogether to provide the desired result when used for solving real-life business problems. Additionally, once put into production, several things can affect a model’s performance. As organisations try to scale these models, the conventional checkpoints could no longer be functional (think: scaling from a million to a billion credit card users).

This problem led experts to support MLOps. The concept combines the best practices of DevOps, machine learning and data engineering to deploy and maintain machine learning models in production reliably and efficiently. Cut short, MLOps are the key to deploying AI projects in real-life to generate actual business value.

For appropriate deployment, organisations might need to comprehend what is frequently misunderstood about MLOps. Let’s dispel some MLOps myths right away.

Myth 1: MLOps is only modelling

When discussing data projects and AI, MLOps focuses entirely on developing the model as the final product. However, modelling is simply one specific aspect of the work to launch data initiatives successfully. In reality, MLOps encompasses every stage of the lifecycle, including data collection, model building, orchestration, health, diagnostics, deployment, governance, and business KPIs. For instance, version management is a crucial element. Teams can do this to quickly revert in the event of an error while monitoring important metrics across a variety of model variants to guarantee the best one is selected.

MLOps start from data gathering and analysis, preparation, model training and validation, serving, monitoring, and even retraining. Hence, MLOps is not just about models but a series of steps that must  work in tandem to push a model to production and operationalise it.

Myth 2: MLOps is just about duplicating models and making them production ready

Designing a model and making the model ready for production are tasks for two different teams. Most organisations don’t plan for the fact that pipelines must operate not just in the context of the teams designing the models but also in the production environment. Design teams put more emphasis on the performance of their models than on how portable their work is in the production environment. There is excessive work left over when the projects reach the production teams. As a result, the model’s relevancy gets over.

MLOps acts as a bridge between data scientists and operations professionals. It looks after the processes from data ingestion, model training and testing, registering, deployment, to even model monitoring. It automates the deployment of models in large-scale production environments, thereby aligning these models with both business needs and regulatory standards.

Myth 3: MLOps and DevOps are the same

MLOps is an offshoot of DevOps. MLOps applies DevOps procedures and ideas to machine learning operations. It uses pipelines and automation to ensure that training activities run smoothly and that finished models are incorporated into software applications.

However, MLOps require extra testing techniques on top of those used for DevOps. Steps like data validation, model validation, and model quality testing could be necessary. In addition, deployment demands the developer build pipelines for continual data processing and training; this necessitates multi-step pipelines to handle retraining stages, verification steps, and redeployment processes.

Myth 4: ML systems are too risky

MLOps may appear very complicated as it is still a young field. However, the ecosystem is quickly developing, and various resources and tools are available to support teams at every stage of the MLOps lifecycle.

Last year, Andrew Ng discussed how the machine learning community might use MLOps tools to create high-quality datasets and repeatable, systematic AI systems. He demanded that machine learning development moves from being model-centric to becoming data-centric. In the future, MLOps, according to Andrew, can be crucial in assuring a high-quality and constant flow of data across all phases of the project.

Myth 5: MLOps is a part of AI Governance

It is a widespread fallacy that MLOps is a part of AI Governance. Are they related? Yes. However, one should be aware that the two are distinct functions. AI Governance’s primary focus is to manage risks associated with the models and ensure compliance, while MLOps is more concerned about the uptime of the production systems and processes. Both MLOps and AI Governance teams desire healthy projects, but their approaches are very different.

To summarise, by developing more effective workflows, utilising data analytics for decision-making, and enhancing customer experience, machine learning enables people and organisations to implement solutions that uncover previously untapped revenue streams and save time and costs. Without a solid plan to adhere to, these objectives are challenging to achieve. Faster go-to-market times and lower operating costs result from automating model creation and deployment with MLOps.

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