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Data science teams use essential ML practices and platforms to collaborate on model development, configure infrastructure, deploy ML models to different environments, and maintain models at scale. ML life cycle management tools are needed to increase the number of models in production, improve the quality of predictions, and reduce the costs in ML model maintenance. Explaining these practices and tools to business stakeholders and budget decision-makers is difficult, as it is all technical jargon to leaders who want to understand the return on investment and business impact of machine learning and artificial intelligence.
