Bienvenidos
IPN-Dharma IA Lab
Es una iniciativa de Laboratorio de Inteligencia Artificial del CIC del IPN con la colaboración de DHARMA para motivar a investigadores, profesores y estudiantes a aprovechar los cursos, recursos y herramientas de las principales plataformas tecnológicas de la industria en las áreas de Aprendizaje Automático, Ciencia de Datos, Computación en la Nube, Inteligencia Artificial e Internet de las Cosas con el propósito de generar una experiencia práctica a través de un modelo de aprendizaje entre pares y por objetivos.
Nivel 2: Conocimiento Contextual
Preparing for Google Cloud Certification: Machine Learning Engineer Professional
This program provides the skills you need to advance your career and provides training to support your preparation for the industry-recognized Google Cloud Professional Machine Learning Engineer Certification.
What you will learn:
- Learn the skills needed to be successful in a machine learning engineering role.
- Prepare for the Google Cloud Professional Machine Learning Engineer certification exam.
- Understand how to design, build, productionalize ML models to solve business challenges using Google Cloud technologies.
- Understand the purpose of the Professional Machine Learning Engineer certification and its relationship to other Google Cloud certifications.
Cursos en este programa
1) Google Cloud Big Data and Machine Learning Fundamentals
This course introduces participants to the big data capabilities of Google Cloud. Through a combination of presentations, demos, and hands-on labs, participants get an overview of Google Cloud and a detailed view of the data processing and machine learning capabilities. This course showcases the ease, flexibility, and power of big data solutions on Google Cloud.
What you will learn:
What you will learn:
- Identify the purpose and value of the key Big Data and Machine Learning products in Google Cloud.
- Use Cloud SQL and Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud.
- Employ BigQuery to carry out interactive data analysis.
- Choose between different data processing products on Google Cloud.
Esfuerzo estimado 12 horas
Idioma español e inglés
2) How Google does Machine Learning
What is machine learning, and what kinds of problems can it solve? Google thinks about machine learning slightly differently -- of being about logic, rather than just data. We talk about why such a framing is useful for data scientists when thinking about building a pipeline of machine learning models.
Then, we discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important the phases not be skipped. We end with a recognition of the biases that machine learning can amplify and how to recognize this.
What you will learn:
Then, we discuss the five phases of converting a candidate use case to be driven by machine learning, and consider why it is important the phases not be skipped. We end with a recognition of the biases that machine learning can amplify and how to recognize this.
What you will learn:
- Frame a business use case as a machine learning problem.
- Gain a broad perspective of machine learning and where it can be used.
- Convert a candidate use case to be driven by machine learning.
- Recognize biases that machine learning can amplify.
3) Launching into Machine Learning
Starting from a history of machine learning, we discuss why neural networks today perform so well in a variety of data science problems. We then discuss how to set up a supervised learning problem and find a good solution using gradient descent. This involves creating datasets that permit generalization; we talk about methods of doing so in a repeatable way that supports experimentation.
What you will learn:
What you will learn:
- Identify why deep learning is currently popular.
- Optimize and evaluate models using loss functions and performance metrics.
- Mitigate common problems that arise in machine learning.
- Create repeatable and scalable training, evaluation, and test datasets.
4) Introduction to TensorFlow
This course is focused on using the flexibility and “ease of use” of TensorFlow 2.x and Keras to build, train, and deploy machine learning models. You will learn about the TensorFlow 2.x API hierarchy and will get to know the main components of TensorFlow through hands-on exercises. We will introduce you to working with datasets and feature columns. You will learn how to design and build a TensorFlow 2.x input data pipeline. You will get hands-on practice loading csv data, numPy arrays, text data, and images using tf.Data.Dataset. You will also get hands-on practice creating numeric, categorical, bucketized, and hashed feature columns.
We will introduce you to the Keras Sequential API and the Keras Functional API to show you how to create deep learning models. We’ll talk about activation functions, loss, and optimization. Our Jupyter Notebooks hands-on labs offer you the opportunity to build basic linear regression, basic logistic regression, and advanced logistic regression machine learning models. You will learn how to train, deploy, and productionalize machine learning models at scale with Cloud AI Platform.
What you will learn:
We will introduce you to the Keras Sequential API and the Keras Functional API to show you how to create deep learning models. We’ll talk about activation functions, loss, and optimization. Our Jupyter Notebooks hands-on labs offer you the opportunity to build basic linear regression, basic logistic regression, and advanced logistic regression machine learning models. You will learn how to train, deploy, and productionalize machine learning models at scale with Cloud AI Platform.
What you will learn:
- Use the Keras Sequential and Functional APIs for simple and advanced model creation.
- Design and build a TensorFlow 2.x input data pipeline.
- Use the tf.data library to manipulate data and large datasets.
- Train, deploy, and productionalize ML models at scale with Cloud AI Platform.
5) Feature Engineering
Want to know how you can improve the accuracy of your ML models? What about how to find which data columns make the most useful features? Welcome to Feature Engineering where we will discuss good vs bad features and how you can preprocess and transform them for optimal use in your models.
What you will learn:
What you will learn:
- Compare the key required aspects of a good feature.
- Understand how to preprocess and explore features with Cloud Dataflow and Cloud Dataprep.
- Combine and create new feature combinations through feature crosses.
- Understand and apply how TensorFlow transforms features.
6) Art and Science of Machine Learning
Welcome to the Art and Science of machine learning. The course covers the essential skills of ML intuition, good judgment and experimentation needed to finely tune and optimize ML models for the best performance. You will learn how to generalize your model using Regularization techniques and about the effects of hyperparameters such as batch size and learning rate on model performance. We’ll cover some of the most common model optimization algorithms and show you how to specify an optimization method in your TensorFlow code.
What you will learn:
What you will learn:
- Generalize a ML model using Regularization techniques.
- Tune batch size and learning rate for better model performance.
- Optimize a ML model.
- Apply the concepts in TensorFlow code.
7) Production Machine Learning Systems
This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators.
What you will learn:
What you will learn:
- Compare static vs. dynamic training and inference.
- Manage model dependencies.
- Set up distributed training for fault tolerance, replication, and more.
- Export models for portability.
8) MLOps (Machine Learning Operations) Fundamentals
This course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud. MLOps is a discipline focused on the deployment, testing, monitoring, and automation of ML systems in production. Machine Learning Engineering professionals use tools for continuous improvement and evaluation of deployed models. They work with (or can be) Data Scientists, who develop models, to enable velocity and rigor in deploying the best performing models.
What you will learn:
What you will learn:
- Identify and use core technologies required to support effective MLOps.
- Configure and provision Google Cloud architectures for reliable and effective MLOps environments.
- Adopt the best CI/CD practices in the context of ML systems.
- Implement reliable and repeatable training and inference workflows.
9) ML Pipelines on Google Cloud
In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. You will learn about pipeline components and pipeline orchestration with TFX. You will also learn how you can automate your pipeline through continuous integration and continuous deployment, and how to manage ML metadata.
Then we will change focus to discuss how we can automate and reuse ML pipelines across multiple ML frameworks such as tensorflow, pytorch, scikit learn, and xgboost. You will also learn how to use another tool on Google Cloud, Cloud Composer, to orchestrate your continuous training pipelines. And finally, we will go over how to use MLflow for managing the complete machine learning life cycle.
Then we will change focus to discuss how we can automate and reuse ML pipelines across multiple ML frameworks such as tensorflow, pytorch, scikit learn, and xgboost. You will also learn how to use another tool on Google Cloud, Cloud Composer, to orchestrate your continuous training pipelines. And finally, we will go over how to use MLflow for managing the complete machine learning life cycle.