What is MLOps?
Deploy and maintain impactful machine learning models reliably and efficiently
Machine Learning Operations (MLOps)
ML Ops is the combination of cultural philosophies, practices, and tools that increases an organization’s ability to deploy and maintain impactful machine learning models reliably and efficiently.
A well run ML Op Shop:
Fosters relationships between Data Science and other key business functions
Identifies and prioritizes the highest-impact and lowest-effort business opportunities for ML teams
Maintains lean workflows with regular feedback loops to facilitate continuous refinements in reliability, efficiency, security, and level of impact
MLOps applies to the entire Machine Learning lifecycle, from: requirement gathering, data gathering, model creation (software development lifecycle, continuous integration/continuous delivery), orchestration, deployment, health, diagnostics, governance, and business reporting.
MLOps Maturity Model
Your organization gets more value from its data as its MLOps maturity increases. But, maturing isn’t as simple as waiting for your team to figure it out or increasing their budget until they scale out of their current issues. Sure, additional capital can help, but, if an ML practice isn’t configured to succeed, further investment will just exacerbate inefficiencies and potentially cause your operation to stall out.
The ML Op Shop Maturity Model captures and generalizes the five most common stages an ML organization evolves through. This framework combines the most useful and actionable online information with over 16 years professional experience.
We name these 5 stages in this model as follows:
Stage 1: Early Analytics
Stage 2: Mature Analytics
Stage 3: Early Machine Learning
Stage 4: Mature Machine Learning
Stage 5: Transcendence
It’s important to note that some aspects of your ML operation may sit in different stages, but overall, you should be able to map your own operation onto one of the 5 stages after reading more about the MLOps Maturity model here.
Ask us a question.
Have a question about Machine Learning or Artificial Intellegence? Reach out to us and we will be in touch to see if we can help.