3/18/2023 0 Comments Color machine learning![]() Additionally, you will continuously monitor your system to detect model decay, remediate performance drops, and avoid system failures so it can continuously operate at all times. You will also implement workflow automation and progressive delivery that complies with current MLOps practices to keep your production system running. You will build scalable and reliable hardware infrastructure to deliver inference requests both in real-time and batch depending on the use case. ![]() In the fourth course of Machine Learning Engineering for Production Specialization, you will learn how to deploy ML models and make them available to end-users. Week 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Types Week 2: Feature Engineering, Transformation, and Selection Week 1: Collecting, Labeling, and Validating data Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. 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. In the second course of Machine Learning Engineering for Production Specialization, you will build data pipelines by gathering, cleaning, and validating datasets and assessing data quality implement feature engineering, transformation, and selection with TensorFlow Extended and get the most predictive power out of your data and establish the data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas. 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 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. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles. 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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