Machine Learning Operations

One place to deploy, maintain, and govern all your production models

Deploying MLOps at scale

Many organizations are embracing the power of cloud to build machine learning models to automate processes, augment decision making, and make sense of complex patterns.  To derive the business benefits from machine learning models, the models need to be deployed in the production environment.  

The reasons that machine learning projects fail are varied and a lot to do with culture, people, and organizational reasons as much as technical.  Like how DevOps brought development and operations team together with automated delivery to optimize the entire product life cycle, MLOps can help bring together various teams to shorten feedback loops, reduce risk, and increase value of ML models.

ML Ops Diagram

 

Tools won’t accomplish this alone — best practices crystalized as blueprints or standard operating procedures will help adoption and result in:

  • Simplify writing production-ready code
  • Spending more time building data pipelines that are scalable, deployable, reproducible and versioned
  • Standardizing the way that teams collaborate
  • Reduce skill gap for junior engineers and data scientists
  • Reduce risk by building privacy and security into solutions from the start

 

Ready to get started?

Building a successful AI program begins with strategic alignment on your business goals. If you’re brand new to AI, we recommend thinking big but starting small—with one project that demonstrates impact and builds momentum. The key is to initiate projects with an eye towards getting to a quick ‘yes’ or ‘no.’ The time for rapid experimentation without a path to production is behind us.

Slalom can help you every step of the way. We quantify the impact of your AI solutions from the start. We challenge the notion that data quality is an obstacle to get started. We leverage the computing power of the cloud for all that it has to offer. And we bring your teams along on the journey to push our co-created solutions to the next level.

Additonal reading materal:

Using MLOps to Unify Machine Learning and DevOps on AWS

MLOps has been evolving rapidly as the industry learns to marry new ML technologies and practices with incumbent software delivery systems and processes. WordStream is a software-as-a-service (SaaS) company using ML capabilities to help small and mid-sized businesses get the most out of their online advertising. Their product development team partnered with Slalom to augment their data science expertise and accelerate project delivery. — Read the story

The Modern MLOps Blueprint

MLOps is the fusion of traditional DevOps processes in the context of data science and machine learning. This blueprint allows you to pick and choose technologies and frameworks that best fit your needs at any stage in your organization's path to ML enablement. — Learn more

MLOps Part 1: Assessing Machine Learning Maturity

In this article, we examine the challenges involved in operationalising machine learning projects and focus on the underlying technical obstacles that need to be addressed (based on Slalom’s experience delivering hundreds of ML related solutions into production and seeing the results). We then look at how MLOps addresses these obstacles and what a mature MLOps capability looks like. — Check it out

MLOps: Evolving and sustaining innovation

Because MLOps cannot be simply created through a tool suite or a process; it’s important to see it as a coordinated and mature set of capabilities that together provide sustainable and widely applicable successes. In this article, we address 4 steps to better under standing the MLOps operations ecosystem. — Read more