Google Learning path: "Machine Learning Engineer Learning Path" - Completed

Нарешті в останній день дії кредитів на навчання отримав сертифікат "Responsible AI for Developers: Privacy & Safety" від Google, чим і завершив довгий, з вересня 2023, Google Learning path: "Machine Learning Engineer Learning Path".

 

Responsible AI for Developers: Privacy & Safety

Progress "Machine Learning Engineer Learning Path"

 

 

Machine Learning Engineer Learning Path

Machine Learning Engineer Learning Path

A Machine Learning Engineer designs, builds, productionizes, optimizes, operates, and maintains ML systems.

21 activities 

A Machine Learning Engineer designs, builds, productionizes, optimizes, operates, and maintains ML systems. This learning path guides you through a curated collection of on-demand courses, labs, and skill badges that provide you with real-world, hands-on experience using Google Cloud technologies essential to the ML Engineer role. Once you complete the path, check out the Google Cloud Machine Learning Engineer certification to take the next steps in your professional journey.

Machine Learning Operations (MLOps): Getting Started | Google Cloud Skills Boost

Кроки для здобуття необхідних навичок для спеціальностей з напрямку AI & Data на платформі Google Cloud Skills Boost завдяки можливості надданій Google Ukraine.

Course: Machine Learning Operations (MLOps): Getting Started

Summary

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.

 

Machine Learning Operations (MLOps): Getting Started, 07.04.2024

Implement Load Balancing on Compute Engine Skill Badge | Google Cloud Skills Boost | Credly

Кроки для для здобуття необхідних навичок для спеціальностей з напрямку AI & Data на платформі Google Cloud Skills Boost завдяки можливості надданій Google Ukraine.

Course: Implement Load Balancing on Compute Engine

Summary

Complete the Implement Load Balancing on Compute Engine skill badge to demonstrate skills in the following: write gcloud commands and use Cloud Shell, create and deploy virtual machines in Compute Engine, run containerized applications on Google Kubernetes Engine, and configure network and HTTP load balancers.

Implement Load Balancing on Compute Engine Skill Badge, 04.04.2024

Recommendation Systems on Google Cloud | Google Cloud Skills Boost

Кроки для для здобуття необхідних навичок для спеціальностей з напрямку AI & Data на платформі Google Cloud Skills Boost завдяки можливості надданій Google Ukraine.

Курс: Recommendation Systems on Google Cloud

Recommendation Systems on Google Cloud, Apr 3, 2024

Summary

In this course, you apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine. This is the fifth and final course of the Advanced Machine Learning on Google Cloud series.

• Recommendation Systems Overview
• Content-Based Recommendation Systems
• Collaborative Filtering Recommendations Systems
• Neural Networks for Recommendation Systems 
• Reinforcement Learning
  

Natural Language Processing on Google Cloud | Google Cloud Skills Boost

Кроки для для здобуття необхідних навичок для спеціальностей з напрямку AI & Data на платформі Google Cloud Skills Boost завдяки можливості надданій Google Ukraine.

Курс: Natural Language Processing on Google Cloud

Natural Language Processing on Google Cloud, Mar 26, 2024

Summary

This course introduces the products and solutions to solve NLP problems on Google Cloud. Additionally, it explores the processes, techniques, and tools to develop an NLP project with neural networks by using Vertex AI and TensorFlow.

1. NLP on Google Cloud
2. NLP with Vertex AI
3. Text representatation
4. NLP models
   

Computer Vision Fundamentals on Google Cloud | Google Cloud Skills Boost

Кроки для для здобуття необхідних навичок для спеціальностей з напрямку AI & Data на платформі Google Cloud Skills Boost завдяки можливості надданій Google Ukraine.

Курс: Computer Vision Fundamentals on Google Cloud

Computer Vision Fundamentals on Google Cloud,  Mar 25, 2024

Summary

This course describes different types of computer vision use cases and then highlights different machine learning strategies for solving these use cases. The strategies vary from experimenting with pre-built ML models through pre-built ML APIs and AutoML Vision to building custom image classifiers using linear models, deep neural network (DNN) models or convolutional neural network (CNN) models.

The course shows how to improve a model's accuracy with augmentation, feature extraction, and fine-tuning hyperparameters while trying to avoid overfitting the data.

The course also looks at practical issues that arise, for example, when one doesn't have enough data and how to incorporate the latest research findings into different models.

Learners will get hands-on practice building and optimizing their own image classification models on a variety of public datasets in the labs they will work on.

• Module 1: Introduction to Computer Vision and Pre-built ML Models with Vision API
• Module 2: Vertex AI and AutoML Vision on Vertex AI
• Module 3: Custom Training with Linear, Neural Network and Deep Neural Network model
• Module 4: Convolutional Neural Networks
• Module 5: Dealing with Image Data

Production Machine Learning Systems | Google Cloud Skills Boost

Кроки для для здобуття необхідних навичок для спеціальностей з напрямку AI & Data на платформі Google Cloud Skills Boost завдяки можливості надданій Google Ukraine.

Курс: Production Machine Learning Systems

Production Machine Learning Systems, Mar 23, 2024

Summary

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.

This is the second course of the Advanced Machine Learning on Google Cloud series. After completing this course, enroll in the Image Understanding with TensorFlow on Google Cloud course.

• Module 1: Architecting Production ML Systems
• Module 2: Designing Adaptable ML Systems
• Module 3: Designing High-performance ML Systems
• Module 4: Hybrid ML Systems
  

Machine Learning in the Enterprise | Google Cloud Skills Boost

Кроки для для здобуття необхідних навичок для спеціальностей з напрямку AI & Data на платформі Google Cloud Skills Boost завдяки можливості надданій Google Ukraine.

Курс: Machine Learning in the Enterprise

Machine Learning in the Enterprise - Mar 20, 2024

Summary

This course encompasses a real-world practical approach to the ML Workow: a case study approach that presents an ML team faced with several ML business requirements and use cases. This team must understand the tools required for data management and governance and consider the best approach for data preprocessing: from providing an overview of Dataow and Dataprep to using BigQuery for preprocessing tasks.

The team is presented with three options to build machine learning models for two specic use cases. This course explains why the team would use AutoML, BigQuery ML, or custom training to achieve their objectives. A deeper dive into custom training is presented in this course. We describe custom training requirements from training code structure, storage, and loading large datasets to expoing a trained model.

You will build a custom training machine learning model, which allows you to build a container image with lile knowledge of Docker.

The case study team examines hyperparameter tuning using Veex Vizier and how it can be used to improve model peormance. To understand more about model improvement, we dive into a bit of theory: we discuss regularization, dealing with sparsity, and many other essential concepts and principles. We end with an overview of prediction and model monitoring and how Veex AI can be used to manage ML models

● Module 1: Understanding the ML Enterprise Workow
● Module 2: Data in the Enterprise
● Module 3: Science of Machine Learning and Custom Training
● Module 4: Veex Vizier Hyperparameter Tuning
● Module 5: Prediction and Model Monitoring Using Veex AI
● Module 6: Veex AI Pipelines
● Module 7: Best Practices for ML Developmen

Feature Engineering | Google Cloud Skills Boost

Кроки для для здобуття необхідних навичок для спеціальностей з напрямку AI & Data на платформі Google Cloud Skills Boost завдяки можливості надданій Google Ukraine.

Курс: Feature Engineering

Feature Engineering - Mar 9, 2024

Summary

Want to know about Veex AI Feature Store? Want to know how you can improve the
accuracy of your ML models? What about how to nd which data columns make the most
useful features? Welcome to Feature Engineering, where we discuss good versus bad
features and how you can preprocess and transform them for optimal use in your models.
This course includes content and labs on feature engineering using BigQuery ML, Keras, and
TensorFlow.

TensorFlow on Google Cloud | Google Cloud Skills Boost

Кроки для для здобуття необхідних навичок для спеціальностей з напрямку AI & Data на платформі Google Cloud Skills Boost завдяки можливості надданій Google Ukraine.

Курс: TensorFlow on Google Cloud

TensorFlow on Google Cloud. Mar 5, 2024

Summary

This course covers designing and building a TensorFlow input data pipeline, building ML models with TensorFlow and Keras, improving the accuracy of ML models, writing ML models for scaled use, and writing specialized ML models.

#MachineLearning #MachineLearningModels #MachineLearningPipeline

BADGES

Launching into Machine Learning | Google Cloud Skills Boost

Кроки для для здобуття необхідних навичок для спеціальностей з напрямку AI & Data на платформі Google Cloud Skills Boost завдяки можливості надданій Google Ukraine.

Курс: Launching into Machine Learning

Launching into Machine Learning. Feb 26, 2024

 

Learning Objectives

● Describe how to improve data quality
● Peorm exploratory data analysis
● Build and train AutoML Models using Veex AI
● Build and train AutoML Models using BigQuery ML
● Optimize and evaluate models using loss functions and peormance metrics
● Create repeatable and scalable training, evaluation, and test datasets

Summary

The course begins with a discussion about data: how to improve data quality and peorm
exploratory data analysis. We describe Veex AI AutoML and how to build, train, and deploy
an ML model without writing a single line of code. You will understand the benets of Big
Query ML. We then discuss how to optimize a machine learning model and how
generalization and sampling can help assess the quality of ML models for custom training

#MachineLearning #MachineLearningModels #MachineLearningPipeline

BADGES

Introduction to AI and Machine Learning on Google Cloud | Google Cloud Skills Boost

Кроки для для здобуття необхідних навичок для спеціальностей з напрямку AI & Data на платформі Google Cloud Skills Boost завдяки можливості надданій Google Ukraine.

Курс: Introduction to AI and Machine Learning on Google Cloud

This course introduces the artificial intelligence (AI) and machine learning (ML) offerings on Google Cloud that support the data-to-AI lifecycle through AI foundations, AI development, and AI solutions. It explores the technologies, products, and tools available to build an ML model, an ML pipeline, and a generative AI project based on the different goals of users, including data scientists, AI developers, and ML engineers.

#MachineLearning #MachineLearningModels #MachineLearningPipeline

Introduction to AI and Machine Learning on Google Cloud | Google Cloud Skills Boost

BADGES

Google Cloud: DevOps Engineer, SRE Learning Path

Так як час вичерпано на навчання, занотую що мало бути за програмою навчання DevOps Engineer, SRE Learning Path

A DevOps Engineer is responsible for defining and implementing best practices for efficient and reliable software delivery and infrastructure management. 

Reliable Google Cloud Infrastructure: Design and Process COMPLETION BADGE

Нарешті знайшов час і завершив цей курс DevOps SRE що дався мені легко і близько до душі. 

І по закінченню отримав цей Reliable Google Cloud Infrastructure: Design and Process COMPLETION BADGE.

Reliable Google Cloud Infrastructure: Design and Process Jul 6, 2023

Reliable Google Cloud Infrastructure: Design and Process

This course equips students to build highly reliable and efficient solutions on Google Cloud using proven design patterns. It is a continuation of the Architecting with Google Compute Engine or Architecting with Google Kubernetes Engine courses and assumes hands-on experience with the technologies covered in either of those courses. Through a combination of presentations, design activities, and hands-on labs, participants learn to define and balance business and technical requirements to design Google Cloud deployments that are highly reliable, highly available, secure, and cost-effective. 

Google Cloud Skill Badge for Create and Manage Cloud Resources

Ось і скінчилася двох місячна підписка на проходження курсів.
Знайшов час і завершив цей технічний челендж DevOps SRE що почав давно, і не міг пройти то зона не та, то зависло створення кластеру бо ресурси вже не має, а потім 5 спроб закінчилися ....

І по закінченню отримав цей Google Cloud Skill Badge for Create and Manage Cloud Resources.

Badge for Create and Manage Cloud Resources Jul 6, 2023

Create and Manage Cloud Resources

Earn a skill badge by completing the Create and Manage Cloud Resources quest, where you learn how to do the following: Write gcloud commands and use Cloud Shell, create and deploy virtual machines in Compute Engine, run containerized applications on Google Kubernetes Engine, and configure network and HTTP load balancers. A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this skill badge quest, and the final assessment challenge lab, to receive a skill badge that you can share with your network.

Отримав: Google Cloud COMPLETION BADGE - Developing a Google SRE Culture Jul 2, 2023

Нарешті знайшов час і завершив цей не технічний курс DevOps SRE що давався мені не так легко. 

І по закінченню отримав цей Google Cloud COMPLETION BADGE.

Developing a Google SRE Culture Jul 2, 2023

Developing a Google SRE Culture

In many IT organizations, incentives are not aligned between developers, who strive for agility, and operators, who focus on stability. Site reliability engineering, or SRE, is how Google aligns incentives between development and operations and does mission-critical production support. Adoption of SRE cultural and technical practices can help improve collaboration between the business and IT. This course introduces key practices of Google SRE and the important role IT and business leaders play in the success of SRE organizational adoption.

Я закінчив проходження онлайн курсу "Google Cloud Fundamentals: Core Infrastructure"

Сьогодні завершив проходження курсу "Google Cloud Fundamentals: Core Infrastructure". Можливо це комусь як дитячий садочок, але треба з чогось починати.

Отримав новий бейдж : "Google Cloud Fundamentals: Core Infrastructure"

Jun. 2023 Google Cloud Fundamentals: Core Infrastructure

Використання Google Cloud IoT MQTT з консольного BASH сценарію та Mosquitto

Google Cloud Platform надає можливість використати IoT Core для підключення IoT пристроїв за протоколом MQTT та HTTP.
Приклади налаштування, і основи роботи є на багатьох ресурсах і у відео:

• Google Cloud IoT Core
• Google Cloud IoT Core with Raspberry Pi

Моя задача створити безпечне підключення до Google IoT Core з консолі свого пристрою і використати мінімум програм:

• bash
• openssl
• mosquitto
• jq
  

Зв'язок mqtt topic (ts2) з google topic event (b01)

Публікація до google topic event (b01) з mosquitto_pub

Перегляд отриманих повідомлень в google sub підписки на mqtt topic (ts2)

 

Підписка до topic - error

Скрипти для автоматизації підключення.

Головна особливість для зв'язку з Google Cloud Platform, те що потрібно автентифікувати пристрій котрий надсилає #MQTT повідомлення за допомогою JWT.

Автентифікація пристрою за JWT