MLOps (Machine Learning Operations)

What Is MLOps?

Machine learning models provide valuable insights to the business, but only if those models can access and
analyze the organization’s data on an ongoing basis. Machine learning operations (MLOps) is the critical process
that makes this possible.

MLOps is a cross-functional, collaborative, and iterative process that operationalizes data science. MLOps does
this by treating machine learning (ML) and other types of models as reusable software artifacts. Models can then
be deployed and continuously monitored via a repeatable process.

MLOps supports continuous integration and repeatable, rapid deployment of models. As such, it helps businesses
discover valuable information and insights from their data more quickly. MLOps also involves ongoing monitoring
and retraining of models in production to ensure they perform optimally as data changes (drifts) over time.

The Benefits of MLOps

One of the main benefits of MLOps is that it enables data science, machine learning, statistical, and other model types
to deliver business value quickly. MLOps does this by ensuring models can be repeatedly deployed and continuously
monitored. The MLOps process allows for:

• Deploying more models faster with automated processes
• Accelerating time-to-value with rapid delivery of models
• Optimizing productivity via collaboration and reusing models
• Reducing risk of wasting time and money on models that are never put into
  production
• Continuous monitoring and updating of models as data drifts over time

The MLOps Process

MLOps supports speedy model delivery at scale. A pared-down version of the MLOps process includes the following steps:

Build – this involves data preparation, feature engineering, model building, and testing.

Manage – after the models are created, they are often put in a model repository that is auditable and under version control to promote reuse throughout the organization.

Deploy – this step involves exporting the model or pipeline, deploying, and integrating it with production business systems and applications.

Monitor – continuous monitoring is required to ensure optimal performance. As data drifts, the model may be retrained or a new model can replace the existing.

Challenges With MLOps

Many organizations face challenges in moving machine learning models into production environments.

On average, between 60% and 80% of models created with the intent to deploy are never deployed. Plus, it typically takes six to eight months to deploy a model. If you deploy a model that was created six to eight months ago, that model may already be obsolete.

Organizations that struggle to integrate machine learning applications with existing production applications waste time and money on data science projects that are never put into production.

MLOps can greatly reduce the risk of such failures and get models into production more quickly where they will ultimately provide the most value to a business.

MLOps vs. DevOps vs. DataOps

MLOps unifies data collection, preprocessing, model training, evaluation, deployment, and retraining in a single
process that teams can maintain. This collaboration and communication between DevOps, ITOps, data engineers, data
science teams, and other departments bring a common understanding of how machine learning models in production are
developed and maintained, similar to what DevOps (development operations) does for software.

DevOps focuses on continuous
delivery of software and automating integration, testing and deployment of code. It doesn’t involve managing data or
analytics. The process of MLOps is modeled after DevOps and relies on collaboration with DevOps teams for model
deployment services.

DataOps (data operations) is
concerned with managing data pipelines and automating processes to reduce the time it takes to complete data
analysis.

MLOps and Analytics Automation

Analytics automation is a larger, all-encompassing solution that brings together the important
processes of data preparation and blending, machine learning model creation, and MLOps to help get organizations
from inputs to insights to outcomes more quickly.

Analytics automation
connects all the building blocks of a data science and analytics workflow to enable smarter, faster decision-making.
With analytics automation solutions, it’s easy to build automated, repeatable workflows to save data scientists time and optimize the
processes of data preparation, model creation, and MLOps.

How to Get Started with MLOps

The Alteryx Analytics Automation Platform is the key to accelerating your data science processes and finding success
with MLOps.

Data access, preparation, modeling, monitoring and model tuning, and sharing of analytic results all happens in
the same place, on one easy-to-use platform. Get started by signing up for free trial of the
platform today.

For more information on Alteryx data science, machine learning, and MLOps solutions, contact us today.