What Data You Need to Collect in Your Company to Build an MLOps Infrastructure
We know that machine learning has inspired a lot of industries and has actually deep penetrated into their everyday business operations. With implementing ML culture, comes a huge responsibility of designing, implementing, training, testing, deploying, monitoring, and evaluating machine learning models in order to seamlessly experience the end-to-end execution of ML models in the production environment.
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Treat data science projects as software development, and you’ll avoid the most common pitfalls
Professional developers always strive to ensure the highest quality in data science projects. This may sound like a generalization, but it’s the truth. The data science craft is a non-trivial one – treated without caution, it might cause at the very least headaches, and in the worst case scenario, entirely misleading results.