![]() By the end, you will be ready to employ your new production-ready skills to participate in the development of leading-edge AI technology to solve real-world problems. In this Specialization, you will become familiar with the capabilities, challenges, and consequences of machine learning engineering in production. ![]() #In production how toIn this Specialization, you will learn how to use well-established tools and methodologies for doing all of this effectively and efficiently. Moreover, the production system must run non-stop at the minimum cost while producing the maximum performance. In striking contrast with standard machine learning modeling, production systems need to handle relentless evolving data. The Machine Learning Engineering for Production (MLOps) Specialization covers how to conceptualize, build, and maintain integrated systems that continuously operate in production. All productions listed below are making timely decisions to contribute to stop the spread of COVID-19. are responding individually and based on best practices for businesses and employers from the BC Centre for Disease Control. ![]() Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles. In December 2018, a CRTC industry summit identified the need for a more inclusive Canadian production industry and asked all Canadian broadcasting companies. Due to the current global circumstances and health concerns related to the COVID-19 pandemic, local productions in B.C. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well.Įffectively deploying machine learning models requires competencies more commonly found in technical fields such as software engineering and DevOps. ![]()
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