Scale your Artificial Intelligence projects from proofs-of-concept to production systems with MLOps
MLOps is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently. The word is a compound of “Machine Learning (ML)” and “DevOps”, a systemic approach of collaboration between software development and operational teams. The intrinsic goal of MLOps is to bring DevOps culture, processes, and tools right into the scope of machine learning practitioners.
A well-implemented practice of MLOps will enable your business to design, build and manage reproducible, testable, and evolvable ML-powered software. You should use it as a general tool to prioritize improvements in how your organization leverages AI & ML through its business. Adopting these principles will result in strategic edges like, among others, a decreased time to production for your use cases, and an increased product uptime for your deployed systems.
MLOps is a concept that stimulates a lot of reflections these days. A few interesting works to refer to for further investigations could be the Google Cloud or Microsoft Azure resources about MLOps implementations. The MLOps reference website is also an interesting resource.
Machine learning is hard. Building and maintaining systems embedding machine learning is even harder. Modern artificial intelligence requires way more than Data Science code to deliver value to your business. Provisioning a serving infrastructure, collecting and maintaining datasets, automating models training and deployments, monitoring the resulting predictions and insights… All these capabilities are unavoidable steps in your journey to machine learning at scale.
On top of specific capabilities requirements, the rise of machine learning in modern IT stacks highlighted clear challenges for artificial intelligence developers. How to experiment with data in a quick and structured way keeping track of produced models and datasets? How to maintain symmetry between experimental and operational environments? In an ever-evolving world, how to keep our models relevant against new realities?
How we can help
Are you ready to start your MLOps journey? Good. In this journey, challenges are ahead and will be numerous but, at Data Minded, we will keep you covered. Implementing MLOps will challenge the way your organization and its whole IT stack are operating. Enabling data-driven use cases will require you to reconsider your organization's culture, people skillset, delivery methodologies, and general perception of data & AI. Sounds daunting, no? The good news is that building data platforms is our specialty at Data Minded. We already did it for clients from various industries and, there are good chances we are busy building new ones at the moment you will read these lines. Our experience put us in good position to help your organization making the transition from thinking about AI as a source of innovation to a critical source of business value. We can help you in all those tough decision-making processes involving people, platforms, implementations, and processes. You’d like to read concrete examples? Gotcha, follow along!
A machine learning project always starts from a good question that is instantiated from business challenges. Therefore, we will start together by defining what are the use cases that are worth living (and growing) in your AI portfolio. We will also aim to define what kind of business value you want to unlock and what are the key metrics associated. Our common goals are clearer? Perfect! Time to kickstart a project! On a day-to-day basis, we will help your teams to focus on iteratively answering your challenging business questions through researches and technical implementations.
Industrializing machine learning models is all about building a scalable and sustainable foundation. This requires bringing structure and standardizing your way of working. We will define, along with your engineering teams, how architecture components like CI/CD pipelines, model stores, feature stores, monitoring applications, or experimentation platforms will embed in your existing IT stacks. We will also help you building shareable organization-specific AI & ML template projects to speed-up the delivery of new use cases.
We don’t want your data platform and the DataOps/MLOps processes running there to become the Wild West of your IT department. Count on us to help you framing the governance and lifecycle of the various projects built by your ML engineers. Down the road, you will only get more efficient, organized, and reliable!