Machine learning (ML) can offer new insights into critical quality attributes and critical process parameters, which can facilitate better process control. Supervised learning algorithms may expedite high-throughput screening experiments, such as clone selection, media screening, and feed development strategies, and resin and column dimension selection, and formulation development. ML could also help to predict deviations in product quality and assist in effective decision making.
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