Adopting MLOps best practices
March 15, 2021
Jean Frerot

Adopting MLOps best practices

MLOps

The rise of artificial intelligence has become omnipresent in recent years. In reality we see that only a small percentage of models makes it to production and stays so. In a series of blog posts on MLOps, we explain why and how companies can adopt MLOps practices to unlock the business value of AI.

Recently, we’ve received many requests from companies asking us to help integrate MLOps best practices within their organization. Challenges range from internal teams that are struggling to bring ML models into production, extended timelines, lower than expected ROI, etc. 

With our MLOps Advisory services, we collaborate with our clients, their business and IT teams to integrate MLOps best practices into their organization to increase the success rate of their machine learning projects and successfully unlock the value of AI. 

How we help clients embrace MLOps

Understanding your organization

Typically, we start by making a diagnosis of the current practices and processes in your teams. We assess AI and MLOps maturity by conducting multiple interviews with key stakeholders from the business, IT, Data Science and Ops teams. 


This step is crucial to ensure we have the necessary level of understanding on how your organization works. This helps us to come up with recommendations that fit your organization. By getting to know the teams, we also build connections and trust that will help us speed up knowledge transfer and the adoption from best practices at a later stage. The earlier we can let teams experience the benefits of MLOps best practices, the better. 


A custom action plan

Based on our findings, we share our recommendations on how to improve your internal ways of working and propose a pragmatic approach to help integrate MLOps best practices within your organization.


We might do a number of inspiration sessions with your team to give an overview of the key concepts of MLops, zoom in on the necessary technical building blocks and give insights into the existing tooling and their pros and cons. 


For organizations on GCP, we also help design a high-level data & AI architecture on GCP and share our Kubeflow and TFX best practices. Dedicated training sessions can be organized to upskill your team fast and efficiently. 


For organizations on other platforms, we review existing architecture designs and give recommendations how to make your data & AI architecture more future-proof and on your choice of tech tooling.


Another way in which we can kick start MLOps best practices is by working together with your team on a specific project and machine learning solution. We strongly believe in a quicker adoption of best practices if you can experience the benefits it brings. By connecting your team with our ML6 experts, we can kickstart the knowledge adoption while also showing how it unlocks value on a specific use case.



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This post is  part of a series of blog posts on the topic of MLOps. In this series, we explain why and how companies can adopt MLOps practices to unlock the business value of AI. Find the other content here.


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