It seems like every day there are new acronyms and buzzwords joining the application development lexicon. That doesn’t mean they’re just passing fads. Most represent advances and technologies that are impacting application development in a positive way.

That’s the case with MLOps, which fuses the words “machine learning” and “operations” — and is the focus of part 2 in our three-part blog series on machine learning (ML).

The Need for MLOps

What is MLOps? Integrating machine learning, DevOps, and data engineering, MLOps is a best practices approach that drives the integration between overall IT operations and the ML development cycle to optimize data management — and ML app development.

It’s a much-needed approach given the low percentage of successful ML projects. In Algorithmia’s “2020 state of enterprise machine learning” report, only 45% of respondents deployed models into production. And according to one study, 85% of ML projects ultimately failed to deliver on their intended promises to business.

These struggles don’t have to be the norm. As organizations better understand MLOps and as more platforms offer ML lifecycle management capabilities, ML project success rates are likely to rise.

The Basics of MLOps

In simple terms, MLOps refers to a collection of methods for automating the lifecycle of ML algorithms in production — from initial model training to retraining against new data. The MLOps workflow can vary based on factors such as business tasks and ML complexity. However, it usually entails automating the following repetitive tasks:

  • Model training pipeline
  • Data ingestion
  • Data validation
  • Data preparation
  • Model training and retraining
  • Model validation
  • Data versioning
  • Model registry
  • Model serving
  • Model monitoring
  • CI/CD orchestration

While it draws upon some of the principles behind DevOps and other software development approaches, including continuous integration (CI) and continuous delivery (CD), MLOps’ focus on using data for model training differentiates it.

For example, with MLOps, CI isn’t just about testing and validating code and components. It also entails testing and validating data, data schemas, and models. CD isn’t restricted to a single software package or a service, but rather a system (an ML training pipeline) that will automatically deploy a model prediction service. There’s also an element unique to MLOps —continuous training (CT) that focuses on automatically retraining and serving the models.


The exact components that comprise MLOps and their definitions may differ from one practitioner to the next. However, they typically include methods around:

  • Model lifecycle management – to manage model development, training, deployment, and operationalization in order to provide consistent, reliable processes for moving models from the data science environment to the production environment.
  • Model versioning and iteration – to operationalize different versions of models and to support multiple versions in operation as needed.
  • Model monitoring and management – to monitor and manage model usage, consumption, and results, ensuring their accuracy, performance, and other measures continue to provide acceptable results.
  • Model security – to protect models from being attacked by nefarious means, corrupted by tainted data, overwhelmed by denial of service attacks, or inappropriately accessed.
  • Model governance – to provide for tracking data changes to model changes, controlling and prioritizing model access, providing transparency into how models use data, and meeting regulatory or compliance needs for model usage.
  • Model discovery – to enable the location of internally developed and third-party consumable models via model catalogs and registries.

The Benefits of MLOps

Among the most significant benefits of MLOps are those resulting from its focus on automation. By applying automation to model training and retraining processes, MLOps streamlines the overall ML development process, which leads to accelerated time to market for the ML models. It also uses CI/CD practices for deploying and updating ML pipelines, so ML-based solutions move into production faster.

In addition to the automation aspect, MLOps best practices like CT and model monitoring, ML-powered apps get timely updates that help improve the user experience and overall satisfaction. Meanwhile, the platforms provide a management and operational framework. That way, data scientists can focus on developing and testing their models.

MLOps also handles data and model validation, evaluation of model performance in production, and the retraining of models against fresh datasets. It facilitates monitoring of ML models for model drift in production. These processes help eliminate risks emanating from false insights, strengthening ML development teams’ confidence in the results produced by the algorithms when making critical decisions.

Another major benefit: MLOps facilitates collaboration between data scientists and IT operations. As such, ML projects leverage a combination of skills, techniques, and tools used in data engineering, machine learning, and DevOps.

That’s important because data scientists and IT operations, as well as data engineering, lines of business, and ML engineering teams, often work in silos. That can lead to the creation of multiple models with different versions and from multiple sources that generate complications around model versioning, governance, access, security, and model usage. Duplicated efforts and other inefficiencies can also result.

How to Take Advantage of MLOps

Unlike DevOps, MLOps is still in its infancy; there aren’t many expert practitioners. ClearScale is among the few organizations that are not only employing MLOps on the cloud but also helping to shape the practice. For organizations seeking to develop and deploy ML-powered apps efficiently and cost-effectively, ClearScale’s MLOps experience makes us a strong development partner.

You can learn about one of the successful projects in which ClearScale used MLOps here.

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