Programa especializado: Machine Learning Engineering for Production (MLOps)

• ¿Cuál es la política de reembolsos?

  Si estás suscrito, obtienes una prueba gratis de 7 días, que podrás cancelar cuando desees sin ningún tipo de penalidad. Una vez transcurrido ese tiempo, no realizamos reembolsos. No obstante, puedes cancelar tu suscripción cuando quieras. Consulta nuestra política completa de reembolsos.

• ¿Puedo inscribirme en un solo curso?

  ¡Sí! Para empezar, haz clic en la tarjeta del curso que te interesa e inscríbete. Puedes inscribirte y completar el curso para obtener un certificado que puedes compartir o puedes acceder al curso como oyente para ver los materiales del curso de manera gratuita. Cuando cancelas la suscripción de un curso que forma parte de un programa especializado, se cancela automáticamente la suscripción de todo el programa especializado. Visita el panel del estudiante para realizar un seguimiento de tu progreso.

• ¿Hay ayuda económica disponible?

  Sí. En ciertos programas de aprendizaje, puedes postularte para recibir ayuda económica o una beca en caso de no poder costear los gastos de la tarifa de inscripción. Si hay ayuda económica o becas disponibles para tu selección de programa de aprendizaje, verás un enlace para postularte en la página de descripción.

• ¿Puedo tomar este curso de manera gratuita?

  Cuando te inscribes en el curso, tienes acceso a todos los cursos del programa especializado y obtienes un certificado cuando completas el trabajo. Si solo deseas leer y ver el contenido del curso, puedes participar del curso como oyente de manera gratuita. Si no puedes pagar la tarifa, puedes solicitar ayuda económica.

• ¿Este curso es 100 % en línea? ¿Necesito asistir a alguna clase en persona?

  Este curso es completamente en línea, de modo que no necesitas ir a un aula en persona. Puedes acceder a tus lecciones, lecturas y tareas en cualquier momento y cualquier lugar a través de Internet o tu dispositivo móvil.

• What is machine learning engineering for production? Why is it relevant?

  Machine learning engineering for production refers to the tools, techniques, and practical experiences that transform theoretical ML knowledge into a production-ready skillset. Effectively deploying machine learning models requires competencies more commonly found in technical fields such as software engineering and DevOps. 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. With machine learning engineering for production, you can turn your knowledge of machine learning into production-ready skills.

• What is the Machine Learning Engineering for Production (MLOps) Specialization about?

  The Machine Learning Engineering for Production (MLOps) Specialization covers how to conceptualize, build, and maintain integrated systems that continuously operate in production. In striking contrast with standard machine learning modeling, production systems need to handle relentless evolving data. Moreover, the production system must run non-stop at the minimum cost while producing the maximum performance. In this Specialization, you will learn how to use well-established tools and methodologies for doing all of this effectively and efficiently. In this Specialization, you will become familiar with the capabilities, challenges, and consequences of machine learning engineering in production. 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.

• What will I be able to do after completing the Machine Learning Engineering in Production (MLOps) Specialization?

  By the end of the Machine Learning Engineering for Production (MLOps) Specialization, you will be ready to:

  1. Design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment requirements.
  2. Establish a model baseline, address concept drift, and prototype how to develop, deploy, and continuously improve a productionized ML application.
  3. Build data pipelines by gathering, cleaning, and validating datasets.
  4. Implement feature engineering, transformation, and selection with TensorFlow Extended.
  5. Establish data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas.
  6. Apply techniques to manage modeling resources and best serve offline/online inference requests.
  7. Use analytics to address model fairness, explainability issues, and mitigate bottlenecks.
  8. Deliver deployment pipelines by productionizing model serving with different infrastructures.
  9. Apply best practices and progressive delivery techniques to maintain a continuously operating production system.

• What background knowledge is necessary for the Machine Learning Engineering for Production (MLOps) Specialization?

  • Learners should have a working knowledge of AI and deep learning. 
  • Learners should have intermediate Python skills and experience with any deep learning framework (TensorFlow, Keras, or PyTorch).
  • Learners should be proficient in basic calculus, linear algebra, and statistics.
  • We highly recommend that you complete the updated Deep Learning Specialization before starting this Specialization.

• Who is the Machine Learning Engineering for Production (MLOps) Specialization for?

  The Machine Learning Engineering for Production Specialization is for early-career machine learning practitioners or software engineers looking to gain practical knowledge of how to formulate a reproducible, traceable, and verifiable machine learning project for production.

• How long does it take to complete the Machine Learning Engineering for Production (MLOps) Specialization?

  This Specialization consists of four courses. At the rate of 5 hours a week, it typically takes 3 weeks to complete the first course, 4 weeks to complete the second, 6 weeks to complete the third, and 4 weeks to complete the fourth. It typically takes about 4 months to complete the entire Specialization.

• Who is the Machine Learning Engineering for Production (MLOps) Specialization by?

  The Machine Learning Engineering for Production Specialization has been created by Andrew Ng, Robert Crowe, and Laurence Moroney.

  Andrew Ng is Founder of DeepLearning.AI, General Partner at AI Fund, Chairman and Co-Founder of Coursera, and an Adjunct Professor at Stanford University. As a pioneer in machine learning and online education, Dr. Ng has changed countless lives through his work in AI, authoring or co-authoring over 100 research papers in machine learning, robotics, and related fields. Previously, he was chief scientist at Baidu, the founding lead of the Google Brain team, and the co-founder of Coursera – the world's largest MOOC platform. 

  A data scientist and TensorFlow addict, Robert Crowe has a passion for helping developers quickly learn what they need to be productive. Since the very early days, he’s used TensorFlow and is excited about how rapidly it's evolving to become even better. Before moving to data science, Robert led software engineering teams for large and small companies, focusing on providing clean, elegant solutions for well-defined needs.

  Laurence Moroney leads AI Advocacy at Google, with a vision to make AI easy for developers and to widen access to ML careers for everyone. He’s written dozens of programming books, the most recent being ‘AI and ML for Coders’ at O’Reilly. Laurence believes that MOOCs are one of the greatest ways to learn, and is excited to create TensorFlow Specializations with DeepLearning.AI on Coursera. When not working with technology, he’s an active member of the Science Fiction Writers of America, and has authored several sci-fi novels, and comics books and a produced screenplay. Laurence is based in Washington state, where he drinks way too much coffee.

• Is this a standalone course or a Specialization?

  The Machine Learning Engineering for Production (MLOps) Specialization is made up of 4 courses.

• Do I need to take the courses in a specific order?

  We recommend taking the courses in the prescribed order for a logical and thorough learning experience.

• Can I apply for financial aid?

  Yes, Coursera provides financial aid to learners who cannot afford the fee.

• Can I audit the Machine Learning Engineering for Production (MLOps) Specialization?

  You can audit the courses in the Machine Learning Engineering for Production Specialization for free. 

  Note that you will not receive a certificate at the end of the course if you choose to audit it for free instead of purchasing it.

• How do I get a receipt to get this reimbursed by my employer?

  1. Go to your Coursera account. 
  2. Click on My Purchases and find the relevant course or Specialization.
  3. Click Email Receipt and wait up to 24 hours to receive the receipt. 
  4. You can read more about it here.

• I want to purchase this Specialization for my employees. How can I do that?

  Visit coursera.org/business for more information, to pick up a plan, and to contact Coursera. For each plan, you decide the number of courses every member can enroll in and the collection of courses they can choose from.

• ¿Recibiré crédito universitario por completar el programa especializado?

  No.

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