Machine learning is still harder than it needs to be. The open-source tool ModelDB and the ML model management platform Verta can help. By Matt Asay.
The artificial intelligence/machine learning software development and deployment lifecycle is still very nascent. The challenge of moving models into production is exacerbated by a demand for speed and a shortage of qualified ML engineers. But there’s hope that things may soon get better.
There’s a need for MLOps: We’re still early enough in ML that it lacks the mature tooling and workflow processes of traditional software development. There, concepts like agile development and continuous integration and continuous deployment let entrenched companies and scrappy startups push new features to market quickly.
Many machine learning solutions are actually assemblies of models. They run several models to get one prediction. Then, after all that, data scientists need to monitor model performance, retrain when needed and redeploy. Interesting read!
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