Plotting

Cloud Computing: Instructional Materials


Cloud Machine Learning Engineering and MLOps

#artificialintelligence

With more companies leveraging software that runs on the Cloud, there is a growing need to find and hire individuals with the skills needed to build solutions on a variety of Cloud platforms. Employers agree: Cloud talent is hard to find. This Specialization is designed to address the Cloud talent gap by providing training to anyone interested in developing the job-ready, pragmatic skills needed for careers that leverage Cloud-native technologies. In the first course, you will learn how to build foundational Cloud computing infrastructure, including websites involving serverless technology and virtual machines, using the best practices of DevOps. The second course will teach you how to build effective Microservices using technologies like Flask and Kubernetes that are continuously deployed to a Cloud platform: Amazon Web Services (AWS), Azure or Google Cloud Platform (GCP).



#cloudcomputing_2022-08-15_08-00-01.xlsx

#artificialintelligence

The graph represents a network of 1,935 Twitter users whose tweets in the requested range contained "#cloudcomputing", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Monday, 15 August 2022 at 15:08 UTC. The requested start date was Monday, 15 August 2022 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 2-day, 5-hour, 56-minute period from Friday, 12 August 2022 at 18:03 UTC to Sunday, 14 August 2022 at 23:59 UTC.



Getting Started With Docker Image - Analytics Vidhya

#artificialintelligence

In this article we will be learning in depth about the nuances of how one can start with Docker and is it really important?r


How new-age tech courses and careers are redefining the future of jobs

#artificialintelligence

New-age careers like cloud reliability engineer, Artificicial Intelligence (AI) architect, malware analyst, digital transformation specialists and many others have taken the job market by storm. These choices have been fuelled by tech courses like Data Science, DevOps, Cloud Computing, AI and and Machine Learning, Cybersecurity resulting in a huge demand for online certifications from ed-tech platforms who have collaborated with distinguished educational institutions. But is this change here to stay and what is its impact? The online education market is expected to touch Rs 360 billion by 2024, up from Rs 39 billion, according to a recent report by data firm'Research and Markets.' With a futuristic approach, DevOps as a system encompasses everything from organization to culture, processes, and tools and its adoption is growing rapidly.


Federated Learning

#artificialintelligence

Then, we will start by loading the dataset on the devices in IID, non-IID, and non-IID and unbalanced settings followed by a quick tutorial on PySyft to show you how to send and receive the models and the datasets between the clients and the server. This course will teach you Federated Learning (FL) by looking at the original papers' techniques and algorithms then implement them line by line. In particular, we will implement FedAvg, FedSGD, FedProx, and FedDANE. You will learn about Differential Privacy (DP) and how to add it to FL, then we will implement FedAvg using DP. In this course, you will learn how to implement FL techniques locally and on the cloud. For the cloud setting, we will use Google Cloud Platform to create and configure all the instances that we will use in our experiments. By the end of this course, you will be able to implement different FL techniques and even build your own optimizer and technique. You will be able to run your experiments locally and on the cloud.


Deploy Machine Learning Pipeline on Google Kubernetes Engine

#artificialintelligence

In our last post on deploying a machine learning pipeline in the cloud, we demonstrated how to develop a machine learning pipeline in PyCaret, containerize it with Docker and serve as a web app using Microsoft Azure Web App Services. If you haven't heard about PyCaret before, please read this announcement to learn more. In this tutorial, we will use the same machine learning pipeline and Flask app that we built and deployed previously. This time we will demonstrate how to containerize and deploy a machine learning pipeline on Google Kubernetes Engine. Previously we demonstrated how to deploy a ML pipeline on Heroku PaaS and how to deploy a ML pipeline on Azure Web Services with a Docker container.


Trending online courses in business, data science, tech, and engineering

#artificialintelligence

In this popular beginner-level Specialization, you'll develop management, leadership, finance, and digital marketing skills that can translate to the successful operation of a business. Learn the basics of running a business and develop new strategies for improving an organization's growth and profitability by analyzing financial statements, creating forecasting and budgeting, and embracing digital marketing best practices.


Why You Should Consider a Multi-Cloud Strategy in Your Next Machine Learning Project

#artificialintelligence

Cloud computing services have been dominated by the most popular and big tech companies in the world such as AWS, Microsoft Azure, Google GCP, and IBM. But every cloud service provider has some strengths and drawbacks that make it difficult for one cloud solution to meet all of an organization's needs. Implementing a multi-cloud strategy allows companies to have more flexibility to optimize costs, speed, and performance. In this article, you will learn what Multi-cloud strategy is, its pros and cons, and how it will reduce the cost to run your infrastructure and applications. Multi-cloud strategy refers to the use of more than one cloud service (multiple cloud services) from two or more vendors.