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What Is MLOps?

MLOps, or Machine Learning Operations, is a set of best practices for enterprise-level businesses to run AI successfully. MLOps streamlines and automates AI infrastructure services, saves resources, and delivers better AI quality. This way, businesses can get higher quality insights from their data in less time.
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What businesses benefit
from MLOps the most?

As entire industries go data-driven these days, a company’s competitive edge is determined by, not only, whether they adopt machine learning (ML), but whether they can extract business value from accumulated data faster than their competitors. This is especially true for fast-paced industries that produce lots of data.
Retail Drive more sales by learning users’ shopping patterns and correlating it to notable events of the day
Healthcare Improve quality of clinical care and unlock important patterns and trends around diagnosis, treatment and continued care
Insurance Get better risk assessment, maximize return on investments, elevate customer service and boost overall efficiency
Media and Entertainment Gain hidden insights into customer behavior and help in delivering personalized content

Speed up the data-to-value workflow
and yield higher quality business insights in less time

Imagine that you have collected data once, used it to develop a ML model, then integrated this model into your business infrastructure and now it delivers flawless results. But over time, this model will inevitably lose some accuracy, because the data it was trained on goes obsolete and does not reflect the current business landscape. In this case, your competitors who leverage ML better can now take advantage of the market situation that your currently decayed model has failed to predict.

To avoid this, you initiate this loop again — gather data, update the model, deploy and integrate it. After this, your business scales up and you now need several ML models, each of them ten times larger than this one. How can you keep up with the pace of a large-scale ML model time-to-market? Or rather, how can your company become that competitor that sees the market ahead and reaps the benefits?

Why is MLOps important?
  • Saves time and effort on ML model maintenance by using pipelines and automation
  • Enables the smooth flow of training operations and the integration of finished ML models into finished software products
  • Saves the effort of your data science team so they can focus on model quality
  • Gives your business accurate and valuable knowledge — and this is the secret weapon your company can put into practice allowing you to stay ahead of the market!

Technical challenges
that MLOps addresses

Managing the full ML model lifecycle

Managing the full ML model lifecycle

An ML model consists of elements that are essentially software entities with their own needs, including management and maintenance. Traditional DevOps methods do not apply to ML models. MLOps is an innovative technique that combines people, process and technology to optimize and deploy ML models swiftly and safely.
Continuous Integration and Deployment

Continuous Integration and Deployment

MLOps lives within a CI/CD framework advocated by DevOps as a proven way to deploy quality code updates at frequent intervals. However, MLOps expands the CI part with data and model validation, while the CD part addresses the complexities of ML deployments.
Orchestration of multiple pipelines

Orchestration of multiple pipelines

The development of machine learning models involves several pipelines — pre-processing, feature engineering, model training and model inference and so on. MLOps plays an essential role in the simple orchestration of these multiple pipelines to ensure the updating of the model automatically.
Scaling ML applications

Scaling ML applications

Managing thousands of models at once is a very cumbersome and a challenging task on its own, and given the need of scaling the models up, this really interferes with productivity. MLOps facilitates scaling and managing thousands of model pipelines in production.
Continuous Training

Continuous Training

A concept unique for MLOps, Continuous Training (CT) is all about the automation of model retraining. It embraces all steps of the model lifecycle from data ingestion to tracking its performance in production. CT ensures that your algorithm will be updated at the first signs of decay or changes in the environment.
Model Governance

Model Governance

Leveraging Model Governance in MLOps can provide rich insights for adjusting and fine-tuning the model performance. Tools for monitoring attributes on a massive scale — such as logging, audit trails, taking snapshots of the pipelines — provide data for further analysis, improving the model output quality.

How does MLOps work

Design

Design

  • Requirements Engeeniring
  • ML-Use Cases Prioritizaion
  • Data Availability Check
Model Development

Model Development

  • Data Engeeniring
  • ML Model Engeeniring
  • Model Testing & Validation
Operations

Operations

  • ML Model Deployment
  • CI/CD Pipelines
  • Monitoring & Triggering

Why WaveAccess is
a perfect partner for MLOps

Select Clients

I have worked with Wave Access for over 10 years now via several companies and they have always delivered what I want on time and on budget. We have worked together on web projects, integration, data loading, building de novo platforms, supporting and further developing legacy code, documentation, and mobile apps. Now my company supports the life science community by curating data and providing knowledge mining services. WaveAccess team embraces our goals, and I am happy with their work products.
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How we process your personal data

When you submit the completed form, your personal data will be processed by WaveAccess USA. Due to our international presence, your data may be transferred and processed outside the country where you reside or are located. You have the right to withdraw your consent at any time.
Please read our Privacy Policy for more information.