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Machine Learning in Production: From Jupyter to Serving 100K Predictions/Second

DASTUTE TechnologiesJanuary 10, 202614 min read

Getting ML models to production is different from training them. Most data scientists struggle with the operationalization phase.


The ML Lifecycle


1. Training in notebooks

2. Model validation

3. Containerization

4. Deployment and monitoring

5. Continuous improvement


Each step requires specific tools and practices.

DT

DASTUTE Technologies

Enterprise Software Development Team

DASTUTE TECHNOLOGIES LIMITED is a UK-registered enterprise software development company with expertise in blockchain development, AI/ML solutions, and dedicated development teams.

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50 articles published at DASTUTE Technologies

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