Accelerate Machine Learning with a Unified Analytics Architecture

Conclusion

The benefits of production ML span multiple industries across numerous use cases. Predictive maintenance, customer service, fraud detection, industrials, and IoT, among countless others, are all being looked at from completely new angles with the advent of ML. Organizations that don’t leverage these growing technologies will be left behind by the ones that do. As it becomes simpler to create and deploy, ML will become table stakes for any company to survive, let alone thrive. It will become as simple for a new ML model to be put into production as it is now to put a new graph on your dashboard.

And this democratization is good. It will lead to better products, cheaper services, and wins for both companies and consumers. The unified analytics architecture will do to data science teams what Tableau did to data analytics teams. Building and deploying the model will no longer be the challenge; rather, understanding the business problem and how to deliver the most value will be the focus. When ML becomes the tool instead of the challenge, value will begin to explode.

Because data preparation and model training have already been done on the full, high-scale dataset, and the environment is identical in development, in test, and in production, moving a proven model into production requires a single line of code. Getting an ML model deployed into production takes minutes, not months.