How MLOps Bridges the Gap Between Data Science and Operations
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In Artificial intelligence (AI) and Machine learning (ML), the transition from model development to operational deployment has historically been a challenging process. Data scientists focus on building and refining ML models, but operationalizing these models in real-world environments often introduces complex technical hurdles. Machine Learning Operations (MLOps) is the solution to this challenge, acting as a bridge between data science and IT operations to ensure seamless, scalable, and efficient deployment of ML models. The Role of Data Science and Operations Data scientists typically work on creating machine learning models, experimenting with algorithms, and tuning them using datasets to improve their accuracy. Their focus is on developing solutions that can provide insights or make predictions based on data. However, data scientists often lack the tools and processes to deploy these models in production environments where real-time decisions are required. MLOps Training i
