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.
Are you a data scientist or AI practitioner who wants to understand cloud platforms? Are you a data scientist or AI practitioner who has worked on Azure or AWS and curious to know how ML activities can be done on GCP? If yes, this course is for you. This course will help you to understand the concepts of the cloud. In the interest of the wider audience, this course is designed for both beginners and advanced AI practitioners.
We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. As organizations continue to migrate their workloads and shift to hybrid or remote work, cloud computing is growing at a rapid rate. Last week at the AWS Summit, according to Swami Sivasubramanian, the vice president of data, analytics and machine learning (ML) services at AWS, analysts project that between 5-15% of IT spend has moved to the cloud -- suggesting that organizations will continue to migrate even more of their workloads to the cloud in the future. The enterprise ecosystem is experiencing a disruption that's largely a result of more cloud-native applications coming to the scene. More companies are embracing a mix of both corporate devices and bring-your-own-device strategies.
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.
As we navigate the roadmap drawn by COVID-19, we know there will continue to be accelerated digital transformation and rapid innovation of education intended to positively impact student outcomes in 2022. This will take many forms, from institutions evolving their operations to students optimizing their learning with technology to caregivers connecting directly with their children's education through edtech services. To support both new and existing solutions, while delivering engaging learning experiences, developing forward-thinking data strategies and building new revenue streams, edtechs are using Amazon Web Services, Inc. (AWS) the world's most comprehensive and broadly adopted cloud platform. Offering over 200 fully featured services from data centers globally, AWS helps these organizations lower costs, become more agile and innovate faster technology solutions that support students and educators every day. Early stage edtechs are also participating in the AWS EdStart program, the AWS edtech virtual startup accelerator, designed to help entrepreneurs build the next generation of online learning, analytics and campus management solutions on the AWS Cloud.
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.
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.
Learn to apply machine learning, remote sensing, big spatial data using the Google Earth Engine cloud computing. Do you want to learn how to access, process and analyze remote sensing data using open source cloud-based platforms? Do you want to master machine learning algorithms to predict Earth Observation big data? Do you want to start a spatial data scientist career in the geospatial industry? Enroll in my new course to master Machine Learning with Remote Sensing in Google Earth Engine.
The uploaded CSV file is stored in IBM Cloud Object Storage service as a dataset. The dataset is then used to build and deploy a machine learning model. This course is intended for anyone with basic to intermediate experience in machine learning who wants to improve skills in regression but most importantly learn how to deploy machine learning models on IBM Cloud whether they be regressors or classifiers. By the end of this course you will know how to do that and have a little refresher on data visualization and data analysis. Do not worry if you pay attention in one of the videos I show you how to solve and prevent many problems when registering the model as well as many other things.