Your AI Infrastructure: Getting It Right

Take a step back and look at your AI infrastructure. Can you say confidently that you are set up for AI success? And when you hear about generative AI, is your organization and your infrastructure ready to weather the winds of change. 

In our on-demand webinar, Building Effective AI Infrastructure, three of our technical experts lead a discussion to answer your most pressing questions about your infrastructure. What makes an AI infrastructure successful? What common mistakes do organizations make when building their infrastructure? What metrics should you use to measure success? 

AI Infrastructure Means Including All the Things  

AI infrastructure is not just about one solution, and you can’t simply set up a network and be done with it. Rather, it should include all the systems and processes that cover the entire end-to-end lifecycle of AI projects. This means having the ability to experiment with new use cases, prepare datasets and features, and train models and deploy them into production, as well as monitoring the performance and accuracy of models. With these moving parts in place, you will lay the foundation for success. 

How Do You Build Effective Infrastructure? 

Building effective infrastructure is a balancing act consisting of three main elements: rapid experimentation, reliable productionization, and adaptability in an evolving ecosystem. 

Experimentation

When it comes to rapid experimentation of models, time is the key element. You want to be able to move quickly, and you want your growth to be organic. You also want to make data access easy for the key people on your team. Once you understand the business impact you’re looking for, you can work out your data access policy. 

To avoid slowing down production and making costly mistakes, it’s very important to separate experimentation from production. This allows you to iterate much faster without interrupting production operations. You should also ask several central questions: Is this a valid use case? Has every step been documented? Is it ready for production? 

Keep in mind that some tools are better than others and can save time and money. Look for repeatability in experimentation to ensure the integrity of your model development process. 

Production

Machine learning in production will make the assumption that the data being used for inference is similar to the data it was trained on. You should expect that this assumption will be violated, either because of a change in the data, external conditions, or because upstream software systems have changed. You can protect your production pipeline with monitoring capabilities such as data drift, model drift, and accuracy. 

Collaboration across your organization is also essential to realizing value at production scale, so you should invest in tools and technologies that help facilitate that cross-functional collaboration. Rather than data scientists just throwing a bunch of code over the fence to ML engineers, make sure everyone understands the business goal you’re trying to achieve. Then when things change—as they inevitably do—you can rely on this collaboration to carry your AI project forward and move your use case into production much more quickly. 

Adaptability

Things change. The world changes, data goes out of date quickly, and models start to drift. When this happens, you’ll need to adapt quickly. One way to do that is not to wait for perfection during the experimentation stage. Too many teams wait until they get a model to perfection before putting it into production, but this process can lock them up for a year or longer. If it’s taking you a year to get your models to production, that’s too long. If you focus on getting “good enough” models in less than three months, you’ll be a much more nimble operation. 

Focus on the use case. Think through the ROI you want to achieve, which will help you determine where to make more targeted investments. Also, by focusing on small use cases and iterating on them quickly, you can build your infrastructure so that your experimentation-to-production process is repeatable. 

Every time you introduce a new technology, you should do a post-mortem and ask, what slowed us down? This will help you assess your infrastructure and unlock greater efficiencies. 

Want to Learn More?

Listen to our on-demand webinar to find out more tips and tricks from our data science experts about building the most effective AI infrastructure. 

About the author

May Masoud

Data Scientist, DataRobot

May Masoud is a data scientist, AI advocate, and thought leader trained in classical Statistics and modern Machine Learning. At DataRobot she designs market strategy for the DataRobot AI Platform, helping global organizations derive measurable return on AI investments while maintaining enterprise governance and ethics.

May developed her technical foundation through degrees in Statistics and Economics, followed by a Master of Business Analytics from the Schulich School of Business. This cocktail of technical and business expertise has shaped May as an AI practitioner and a thought leader. May delivers Ethical AI and Democratizing AI keynotes and workshops for business and academic communities.

Meet May Masoud