platform engineering, MLOps, machine learning,

Two recent surveys on artificial intelligence (AI) make the case that the value proposition for AI has achieved critical mass. According to PwC’s 2023 Emerging Technology Survey, 73% of U.S. respondents stated that their company has adopted AI in some business areas. In a recent Forbes Advisor survey, 64% of respondents said AI will improve customer relationships and increase productivity, with 60% expecting AI to drive sales growth.

However, as enterprises restructure business operations around predictive and generative AI to gain competitive advantage, they need to maximize the efficiency of their machine learning operations (MLOps) to deliver positive ROI. This is no small feat today, given that AI at scale means enterprises can have tens or hundreds of machine learning models (MLMs) in development, training or production at all times.

Without the right automation and self-service capabilities, the workflows supporting distributed MLOps at scale can hinder ML engineers from never-ending infrastructure and component management tasks. This prevents them from engaging in high-value work on models or the AI applications that their MLMs support.

Take a Platform Engineering Approach

Just as platform engineering emerged from the DevOps movement to streamline app development workflows, so too must platform engineering streamline the workflows of MLOps. To achieve this, one must first recognize the fundamental differences between DevOps and MLOps.

Only then can one produce an effective platform engineering solution for ML engineers. To enable AI at scale, enterprises must commit to developing, deploying, and maintaining platform engineering solutions purpose-built for MLOps.

Scale AI Across Distributed Enterprises With a Customizable Blueprint

Whether due to data governance requirements or practical concerns about moving vast volumes of data over significant geographical distances, MLOps at scale require enterprises to utilize a spoke-and-wheel approach: model development and training occurs centrally; trained models are distributed to edge locations for fine-tuning on local data; fined-tuned models are deployed close to where end users interact with them and the AI applications they leverage.

Here’s how many enterprises are approaching AI at scale:

• Establish a Center of Excellence within the enterprise where MLMs can be developed and trained centrally.
• Leverage open-source models from public repositories so your organization isn’t starting from square one when developing each new model.
• Focus on developing smaller, more specialized models to address specific business use cases.
• Train models on proprietary company data and move trained models to a centrally-located private registry that makes them accessible across the enterprise.
• Utilize a robust, cloud-based, hybrid, and/or multicloud edge architecture that allows for tightly integrated CPU and GPU operations to serve AI inference within the geographic regions where your organization does business.
• Fine-tune models at the edge based on local data to account for regional and cultural considerations while maintaining data governance and privacy requirements.

Optimize MLOps With a Purpose-Built Platform Engineering Solution

Platform engineering solutions designed for MLOps at scale must address all of the following requirements:

• Infrastructure Optimization: Simplify how data scientists and ML engineers deploy infrastructure components optimized for ML workloads.
• Model Management and Deployment: Establish an efficient Kubernetes-based private registry for trained models, making them available and accessible across the enterprise.
• Data Governance and Privacy: Provide edge-based data storage and security measures for maintaining data governance and privacy when training models on proprietary company data and fine-tuning models at the edge based on regional data.
• Model Observability at Every Phase: Integrate monitoring and observability tools into the platform engineering solution so ML engineers can build observability into every phase of MLOps to ensure responsible AI practices.
• Automating Tasks and Self-Service: Automate code builds, testing, and deployments through CI/CD pipelines, as well as infrastructure provisioning and management using infrastructure as code (IaC) tools.

Future-Proof Your Platform Engineering Solution to Future-Proof Your Company’s MLOps

The innovation economy enveloping the AI ecosystem introduces new components that improve the AI stack nearly daily. When developed properly, your ML platform engineering solution can harness powerful new technologies as they become available. To make this possible, your ML platform engineering solution must be managed as a product rather than a project.

This requires treating the data scientists and ML engineers who engage with the platform as customers and assigning a dedicated product support team to manage the solution’s features backlog. The platform engineering product team must continuously improve the solution as requirements change and technology evolves.

Enterprises should hire engineers with MLOps experience to fill platform engineering roles appropriately. According to World Economic Forum research, AI is projected to create around 97 million new jobs by 2025. ML platform engineering roles will comprise a growing number of these opportunities.

Enterprises that adopt an MLOps platform engineering approach will provide a much-needed immediate boost to their operational efficiency and future-proof their AI program by ensuring their ML engineers will always be able to focus on the high-value data science work they were hired to perform.