The world of Machine Learning (ML) is booming, but deploying and managing these models effectively requires specialized skills. Look no further than Google! They’ve just launched a treasure trove of knowledge – 7 brand new, completely free MLOps courses designed to equip techies with the expertise to excel in this dynamic field.
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Table of Contents
About the Google
Google is an American multinational corporation and technology company focusing on online advertising, search engine technology, cloud computing, computer software, quantum computing, e-commerce, consumer electronics, and artificial intelligence (AI). It has been referred to as “the most powerful company in the world” and is one of the world’s most valuable brands due to its market dominance, data collection, and technological advantages in the field of AI.Google’s parent company, Alphabet Inc. is one of the five Big Tech companies, alongside Amazon, Apple, Meta, and Microsoft.
Google was founded on September 4, 1998, by American computer scientists Larry Page and Sergey Brin while they were PhD students at Stanford University in California. Together, they own about 14% of its publicly listed shares and control 56% of its stockholder voting power through super-voting stock.
The company went public via an initial public offering (IPO) in 2004. In 2015, Google was reorganized as a wholly owned subsidiary of Alphabet Inc. Google is Alphabet’s largest subsidiary and is a holding company for Alphabet’s internet properties and interests. Sundar Pichai was appointed CEO of Google on October 24, 2015, replacing Larry Page, who became the CEO of Alphabet. On December 3, 2019, Pichai also became the CEO of Alphabet
Eligibility Criteria
This Free MLOps courses for Techies and College Students, Working Professionals,Housewives
Here Are Google Launched 7 Free MLOps courses
1. Machine Learning Operations
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.
When you complete this course, you can earn the badge displayed here! View all the badges you have earned by visiting your profile page. Boost your cloud career by showing the world the skills you have developed!
Course Link Click Here
2.MLOps: Continuous delivery
This document discusses techniques for implementing and automating continuous integration (CI), continuous delivery (CD), and continuous training (CT) for machine learning (ML) systems.
Data science and ML are becoming core capabilities for solving complex real-world problems, transforming industries, and delivering value in all domains. Currently, the ingredients for applying effective ML are available to you:
Large datasets
Inexpensive on-demand compute resources
Specialized accelerators for ML on various cloud platforms
Rapid advances in different ML research fields (such as computer vision, natural language understanding, and recommendations AI systems).
Therefore, many businesses are investing in their data science teams and ML capabilities to develop predictive models that can deliver business value to their users.
This document is for data scientists and ML engineers who want to apply DevOps principles to ML systems (MLOps). MLOps is an ML engineering culture and practice that aims at unifying ML system development (Dev) and ML system operation (Ops). Practicing MLOps means that you advocate for automation and monitoring at all steps of ML system construction, including integration, testing, releasing, deployment and infrastructure management.
Data scientists can implement and train an ML model with predictive performance on an offline holdout dataset, given relevant training data for their use case. However, the real challenge isn’t building an ML model, the challenge is building an integrated ML system and to continuously operate it in production. With the long history of production ML services at Google, we’ve learned that there can be many pitfalls in operating ML-based systems in production. Some of these pitfalls are summarized in Machine Learning: The high-interest credit card of technical debt.
Course Link Click Here
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3. Build and Deploy Machine Learning
Earn a skill badge by completing the Build and Deploy Machine Learning Solutions with Vertex AI course, where you will learn how to use Google Cloud’s unified Vertex AI platform and its AutoML and custom training services to train, evaluate, tune, explain, and deploy machine learning solutions.
This skill badge course is for professional Data Scientists and Machine Learning Engineers.
The datasets and labs are built around high business impact enterprise machine learning use cases; these include retail customer lifetime value prediction, mobile game churn prediction, visual car part defection identification, and fine-tuning BERT for review sentiment classification. Learners who complete this skill badge will gain hands-on experience with Vertex AI for new and existing ML workloads and be able to leverage AutoML, custom training, and new MLOps services to significantly enhance development productivity and accelerate time to value.
Course Link Click Here
4. 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.
Course Link Click Here
5.MLOps with Vertex AI
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.
Learners will get hands-on practice using Vertex AI Feature Store’s streaming ingestion at the SDK layer.
When you complete this course, you can earn the badge displayed here! View all the badges you have earned by visiting your profile page. Boost your cloud career by showing the world the skills you have developed!
Course Link Click Here
6. Machine Learning Operations (MLOps): Getting Started
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.
When you complete this course, you can earn the badge displayed here! View all the badges you have earned by visiting your profile page. Boost your cloud career by showing the world the skills you have developed!
Course Link Click Here
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7. Production of 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.
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.
When you complete this course, you can earn the badge displayed here! View all the badges you have earned by visiting your profile page. Boost your cloud career by showing the world the skills you have developed!
Course Link Click Here
