DeepLearning.AI launches Machine Learning Engineering or Production (MLOps) Specialization to help learners become industry-ready. Today, MLOps has become an essential part of organizations to build robust machine learning-based solutions. However, there is a dearth of courses that can enable learners to build end-to-end AI solutions.
Taught by instructors from Google, Pulsar, and Andrew Ng, there are four courses in the MLOps specialization by DeepLearning.AI — introduction to machine learning in production, machine learning data lifecycle in production, machine learning modeling pipelines in production, and deploying machine learning models in production.
Andrew Ng announced on LinkedIn the release of the MLOps specialization by DeepLearning.AI on Coursera. The specialization covers the designing of an ML production system, modeling strategies, development requirements, establishing a model baseline, building data pipelines, and more.
“Being able to train ML models is essential. And, to build an effective AI career, you need production engineering skills as well, to build and deploy ML systems. With this specialization, you can grow your knowledge of ML into production-ready skills.,” wrote Andrew Ng on LinkedIn.
Since the specialization is categorized as advanced level, you would require prerequisites like knowledge of Python and familiarity with deep learning frameworks like PyTorch, Keras, or TensorFlow.