We may earn an affiliate commission when you visit our partners.
Nestor Nicolas Campos Rojas
En este proyecto, vamos a levantar de forma simple un proceso DevOps con modelos de Machine Learning para entender todo un proceso MLOps.
Register for this course and see more details by visiting:
OpenCourser.com/course/08nybk/creando
Three deals to help you save
We found three deals and offers that may be relevant to this course.
Save money when you learn.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Valid until December 12
Annual Subscription Savings
Unlock programs from Google, Microsoft, and IBM and join 77% of learners who reported positive career outcomes.
Only $239!
Valid for a limited time only
Future-proof your career
Access O'Reilly books, live events, courses, and more. Save with an annual subscription.
Take
15%
off
What's inside
Syllabus
Visión general del proyecto
En este proyecto, vamos a levantar de forma simple un proceso DevOps con modelos de Machine Learning para entender todo un proceso MLOps.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Fortalece las bases en aprendizaje automático (ML) para principiantes
Combina modelos de aprendizaje automático (ML) con procesos DevOps
Proporciona una visión general del proceso MLOps, esencial para el desarrollo y la implementación de modelos de ML
Save this course
Save Creando un proceso de MLOps con Azure Machine Learning to your list so you can find it easily later:
Save
Save
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Creando un proceso de MLOps con Azure Machine Learning with these
activities:
Revise linear algebra
Show steps
Linear algebra is used heavily in machine learning. Revising key concepts in linear algebra will help to reinforce understanding of course materials.
Browse courses on
Matrix
Show steps
-
Review notes from a previous course on linear algebra
-
Solve practice problems from a textbook or online resource
Follow tutorials on scikit-learn
Show steps
Scikit-learn is a popular machine learning library in Python. Following tutorials on scikit-learn will help to gain hands-on experience with machine learning algorithms.
Browse courses on
scikit-learn
Show steps
-
Find a tutorial on scikit-learn such as those on the official website.
-
Follow the tutorial step-by-step and implement the code
-
Try modifying the code to experiment with different parameters
-
Troubleshoot any errors that may occur
Join a study group and discuss course materials
Show steps
Joining a study group and discussing course materials with peers will help to reinforce understanding and to learn from others.
Show steps
-
Find a study group such as those organized by the university or online forums
-
Attend study group meetings and actively participate in discussions
-
Prepare questions and share insights with the group
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend machine learning meetups and conferences
Show steps
Attending machine learning meetups and conferences is a great way to connect with others in the field and to learn about the latest trends and developments.
Browse courses on
Networking
Show steps
-
Find machine learning meetups and conferences in your area
-
Attend the events and introduce yourself to others
-
Ask questions and share your knowledge with others
Practice coding machine learning algorithms
Show steps
Coding machine learning algorithms from scratch helps to develop a deeper understanding of how they work. This will also prepare for practical implementations in real-world scenarios.
Browse courses on
Machine Learning Algorithms
Show steps
-
Choose a machine learning algorithm to practice, such as linear regression
-
Implement the algorithm in a programming language such as Python or R
-
Test the algorithm on a dataset and evaluate its performance
Build a simple machine learning model
Show steps
Building a machine learning model is a practical way to apply the concepts learned in the course. This will help to gain hands-on experience with the entire machine learning pipeline.
Show steps
-
Define a problem statement and gather a dataset
-
Preprocess the data and explore it
-
Choose and train a machine learning model
-
Evaluate the performance of the model
-
Deploy the model and monitor its performance
Write a blog post about a machine learning technique
Show steps
Writing a blog post about a machine learning technique helps to solidify understanding and allows one to share knowledge with others. It is also a good way to practice communication skills.
Browse courses on
Technical Writing
Show steps
-
Choose a machine learning technique to write about
-
Research the technique and gather information
-
Write a clear and concise blog post
-
Proofread and edit the blog post
-
Publish the blog post on a platform such as Medium or Hashnode
Mentor other students in the course
Show steps
Mentoring other students in the course is beneficial for both the mentor and the mentee. The mentor gets to reinforce their understanding and the mentee gets personalized support.
Show steps
-
Identify students who need help with the course material
-
Offer to mentor them and provide guidance
-
Meet regularly and discuss the course material
-
Provide feedback and support to the mentees
Revise linear algebra
Show steps
Linear algebra is used heavily in machine learning. Revising key concepts in linear algebra will help to reinforce understanding of course materials.
Browse courses on
Matrix
Show steps
Show steps
Show steps
- Review notes from a previous course on linear algebra
- Solve practice problems from a textbook or online resource
Follow tutorials on scikit-learn
Show steps
Scikit-learn is a popular machine learning library in Python. Following tutorials on scikit-learn will help to gain hands-on experience with machine learning algorithms.
Browse courses on
scikit-learn
Show steps
Show steps
Show steps
- Find a tutorial on scikit-learn such as those on the official website.
- Follow the tutorial step-by-step and implement the code
- Try modifying the code to experiment with different parameters
- Troubleshoot any errors that may occur
Join a study group and discuss course materials
Show steps
Joining a study group and discussing course materials with peers will help to reinforce understanding and to learn from others.
Show steps
Show steps
Show steps
- Find a study group such as those organized by the university or online forums
- Attend study group meetings and actively participate in discussions
- Prepare questions and share insights with the group
Five other activities
Expand to see all activities and additional details
Show all eight activities
Attend machine learning meetups and conferences
Show steps
Attending machine learning meetups and conferences is a great way to connect with others in the field and to learn about the latest trends and developments.
Browse courses on
Networking
Show steps
Show steps
Show steps
- Find machine learning meetups and conferences in your area
- Attend the events and introduce yourself to others
- Ask questions and share your knowledge with others
Practice coding machine learning algorithms
Show steps
Coding machine learning algorithms from scratch helps to develop a deeper understanding of how they work. This will also prepare for practical implementations in real-world scenarios.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Choose a machine learning algorithm to practice, such as linear regression
- Implement the algorithm in a programming language such as Python or R
- Test the algorithm on a dataset and evaluate its performance
Build a simple machine learning model
Show steps
Building a machine learning model is a practical way to apply the concepts learned in the course. This will help to gain hands-on experience with the entire machine learning pipeline.
Show steps
Show steps
Show steps
- Define a problem statement and gather a dataset
- Preprocess the data and explore it
- Choose and train a machine learning model
- Evaluate the performance of the model
- Deploy the model and monitor its performance
Write a blog post about a machine learning technique
Show steps
Writing a blog post about a machine learning technique helps to solidify understanding and allows one to share knowledge with others. It is also a good way to practice communication skills.
Browse courses on
Technical Writing
Show steps
Show steps
Show steps
- Choose a machine learning technique to write about
- Research the technique and gather information
- Write a clear and concise blog post
- Proofread and edit the blog post
- Publish the blog post on a platform such as Medium or Hashnode
Mentor other students in the course
Show steps
Mentoring other students in the course is beneficial for both the mentor and the mentee. The mentor gets to reinforce their understanding and the mentee gets personalized support.
Show steps
Show steps
Show steps
- Identify students who need help with the course material
- Offer to mentor them and provide guidance
- Meet regularly and discuss the course material
- Provide feedback and support to the mentees
Career center
Learners who complete Creando un proceso de MLOps con Azure Machine Learning will develop knowledge and skills
that may be useful to these careers:
Project Manager
Project Manager
A Project Manager plans, executes, and closes projects. This course may be useful to one in this role, as it would help them build a foundation in MLOps.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer operationalizes machine learning models. This course may be helpful to one who wishes to enter this field, as it would help them build a foundation in MLOps.
Product Manager
Product Manager
A Product Manager develops, launches, and maintains software products. This course may be useful to one in this role, as it would help them build a foundation in MLOps.
Technical Program Manager
Technical Program Manager
A Technical Program Manager bridges the gap between technical and non-technical teams. This course may be useful to one in this role, as it would help them build a foundation in MLOps.
Software Engineer
Software Engineer
A Software Engineer applies engineering principles to the design, development, deployment, and maintenance of software. Those with experience in machine learning may be interested in this course.
Consultant
Consultant
A Consultant provides expert advice to clients on a variety of business issues. A Consultant with a background in machine learning may be interested in this course.
Data Engineer
Data Engineer
A Data Engineer designs, builds, and maintains the infrastructure for data storage and processing. This course may be useful to one in this role.
Risk Analyst
Risk Analyst
A Risk Analyst identifies, assesses, and mitigates financial risk. This course may be useful to one in this role.
Business Intelligence Analyst
Business Intelligence Analyst
A Business Intelligence Analyst mines and analyzes data to transform it into actionable insights that drive business decisions. This course may be useful to one in this role.
Data Scientist
Data Scientist
A Data Scientist mines, interprets, and presents data, all to the end of uncovering actionable insights that may be monetized to drive revenue or otherwise benefit the organization. Machine learning is central to a Data Scientist's work, and this course may be useful to one in this role.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze data to assess risk and make investment decisions. This course may be useful to one in this role.
Statistician
Statistician
A Statistician applies statistical theories and methods to collect, analyze, interpret, and present data. Machine learning is central to a Statistician's work, and this course may be useful to one in this role.
Business Analyst
Business Analyst
A Business Analyst analyzes business processes and identifies areas for improvement. This course may be useful to one in this role.
Data Analyst
Data Analyst
A Data Analyst performs statistical analyses to extract actionable insights from data. This course may be useful to one in this role.
Financial Analyst
Financial Analyst
A Financial Analyst researches, analyzes, and interprets financial data to make investment decisions. This course may be useful to one in this role.
For more career information including salaries, visit:
OpenCourser.com/course/08nybk/creando
Reading list
We've selected eight books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Creando un proceso de MLOps con Azure Machine Learning.
Save
Libro avanzado que cubre los principios y algoritmos de redes neuronales profundas. Es una lectura esencial para quienes deseen profundizar en el aprendizaje profundo y sus aplicaciones.
Save
Este libro adopta un enfoque de sistemas para el aprendizaje automático, cubriendo el diseño, la implementación y el despliegue de sistemas de aprendizaje automático a gran escala. Proporciona información sobre desafíos y consideraciones para desarrollar y operar sistemas de aprendizaje automático robustos.
Este libro cubre el diseño e implementación de canalizaciones de aprendizaje automático, desde la ingesta de datos hasta la implementación del modelo. Proporciona información sobre los desafíos y las mejores prácticas en la construcción de canalizaciones eficientes y escalables.
Este libro proporciona una guía práctica para el aprendizaje profundo utilizando Python y la biblioteca Keras. Es un recurso valioso para comprender las técnicas y arquitecturas de aprendizaje profundo, que son cada vez más importantes en MLOps.
Este libro proporciona una base sólida en Python para el análisis de datos, cubriendo bibliotecas como NumPy, Pandas y Matplotlib. Es un recurso esencial para comprender el procesamiento y análisis de datos, que son habilidades fundamentales para MLOps.
Libro centrado en el uso de Python para el aprendizaje automático. Proporciona una guía paso a paso para la implementación de varios algoritmos y técnicas de aprendizaje automático.
Libro accesible que proporciona una visión general de alto nivel de la inteligencia artificial, sus conceptos y aplicaciones. Es una lectura valiosa para los principiantes que buscan una comprensión básica del campo.
Este libro ofrece una introducción integral a los algoritmos de aprendizaje automático, cubriendo temas como clasificación, regresión y aprendizaje no supervisado. Proporciona una base sólida para comprender los fundamentos teóricos detrás de los modelos de aprendizaje automático.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/08nybk/creando
Share
Similar courses
Here are nine courses similar to
Creando un proceso de MLOps con Azure Machine Learning.
Gestión de ventas sin fricción
OpenCourser.com/course/xx12hl/gestion
Gestión de ventas sin fricción
Analizar e incrementar - Parte 1
OpenCourser.com/course/kqtsqj/analizar
Analizar e incrementar - Parte 1
¿Cómo tomar decisiones financieras que agreguen valor?
OpenCourser.com/course/bl70vy/?como
¿Cómo tomar decisiones financieras que agreguen valor?
Crea un mapa de proceso con Canva
OpenCourser.com/course/3n87ol/crea
Crea un mapa de proceso con Canva
Gestión de operaciones
OpenCourser.com/course/bkkson/gestion
Gestión de operaciones
Identificación de dispositivos médicos (UDI)
OpenCourser.com/course/smu4yf/identificacion
Identificación de dispositivos médicos (UDI)
Utilizar Diseño Para Crear Soluciones de Negocio Innovadoras
OpenCourser.com/course/mlr7v7/utilizar
Utilizar Diseño Para Crear Soluciones de Negocio...
Producción musical y su efecto en la composición
OpenCourser.com/course/t95x4m/produccion
Producción musical y su efecto en la composición
Calidad en el servicio y lealtad del cliente
OpenCourser.com/course/ftsb3w/calidad
Calidad en el servicio y lealtad del cliente
Effort
1 hora de trabajo
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Nestor Nicolas Campos Rojas
Language
Español
Good to know
Fortalece las bases en aprendizaje automático (ML) para principiantes
Combina modelos de aprendizaje automático (ML) con procesos DevOps
Proporciona una visión general del proceso MLOps, esencial para el desarrollo y la implementación de modelos de ML
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser