Make Building and Using AI Systematic and Repeatable
The shift toward data-centric AI development is being enabled by the emerging field of MLOps, which provide tools that make building, deploying, and maintaining AI systems easier than ever before. Tools that are geared to help high-quality data sets, in particular, hold the key to addressing the challenges of small data sets, the high cost of customization, and the long road to getting an AI project into production outlined above.
Just to clarify, MLOps, also known as Machine Learning Operations, refers to the practices, processes, and tools that organizations use to manage and operationalize their machine learning models. MLOps tools are software solutions that help organizations automate, manage, and monitor the entire lifecycle of machine learning models, from development to deployment and maintenance.
How, exactly? First, ensuring high-quality data means that AI systems will be able to learn from the smaller datasets available in most industries. Second, by making it possible for a business domain expert, rather than AI experts, to engineer the data, the ability to use AI will become more accessible to all industries. And third, MLOps platforms provide much of the scaffolding software needed to take an AI system to production, so teams no longer have to develop this software. This allows teams to deploy AI systems — and bridge the gap between proof of concept and production weeks or months rather than years.
Most valuable AI projects have yet to be imagined. And even for projects that teams are already working on, the gap that leads to deployment in production remains to be bridged — indeed, Accenture estimates that 80% to 85% of companies’ AI projects are in the proof-of-concept stage.
Here’re some things companies can do right now:
- Instead of merely focusing on the quantity of data you collect, also consider the quality, and make sure it clearly illustrates the concepts we need the AI to learn.
- Make sure your team considers taking a data-centric approach rather than a software-centric approach. Many AI engineers, including many with strong academic or research backgrounds, were trained to take a software-centric approach; urging them to adopt data-centric techniques as well.
- For any AI project that you intend to take to production, be sure to plan the deployment process and provide MLOps tools to support it. For example, even while building a proof of concept system, urge the teams to begin developing a longer-term plan for data management, deployment, and AI system monitoring and maintenance.
It’s possible for AI to become a thriving asset outside of data-rich consumer internet businesses, but has yet to hit its stride in other industries. But because of this, the greatest untapped opportunity for AI may lie in taking it to these other industries. Just as electricity has transformed every industry, AI is on a path to do so too. But the next few steps on that path will require a shift in our playbook for how we build and deploy AI systems. Specifically, a new data-centric mindset, coupled with MLOps tools that allow industry domain experts to participate in the creation, deployment, and maintenance of AI systems, will ensure that all industries can reap the rewards that AI can offer.