Infrastructure as code has been an important practice of DevOps for years. Anyone running an Apache Kafka data infrastructure and running on Kubernetes, the chances are you’ve probably nailed defining your infrastructure this way. If you’re running on Kubernetes, you’re likely using operators as part of your CI/CD toolchain to automate your deployments.
In an ideal world where we reached 100% test coverage, our error handling was flawless, and all our failures were handled gracefully — in a world where all our systems reached perfection, we wouldn’t be having this discussion. Yet, here we are. Earth, 2020. By the time you read this sentence, somebody’s server failed in production. A moment of silence for the processes we lost.
Data scientists today have to choose between a massive toolbox where every item has its pros and cons. We love the simplicity of Python tools like pandas and Scikit-learn, the operation-readiness of Kubernetes, and the scalability of Spark and Hadoop, so we just use all of them. What happens? Data scientists explore data using pandas, then data engineers use Spark to recode the same logic to scale or with live streams or operational databases.
Effectively bringing machine learning to production is one of the biggest challenges that data science teams today struggle with. As organizations embark on machine learning initiatives to derive value from their data and become more “AI-driven” or “data-driven”, it’s essential to find a faster and simpler way to productionize machine learning projects so that they can make business impact faster.
