Atharv Gyan Beta

 What is Cloud Computing? ☁ Cloud computing is the delivery of different services through the Internet. These resources include tools and applications like data storage, servers, databases, networking, and software. Rather than keeping files on a proprietary hard drive or local storage device, cloud-based storage makes it possible to save them to a remote database. One of the Cloud Service providers is AWS — Amazon Web Services. We don’t need to pay any upfront amount; AWS offers pay-as-you-go pricing — which enables you to pay only for what you use. Once you stop your usage, there is no fee to terminate, and billing stops immediately. Concept behind Cloud Computing Technology: Now let’s talk about what is the concept behind cloud computing. We need to go through some steps for this. Let’s understand the following: There are basic three ways to install Operating System. 1. Bare Metal 2. Virtualization 3. Containerization In Bare Metal you directly install the OS to ...

Cloud Computing has become an essential technology for businesses of all sizes, and three major cloud service providers are leading the way: Google Cloud Platform (GCP), Amazon Web Services (AWS), and Microsoft Azure. Each cloud platform has its strengths and weaknesses when it comes to configurability, ease of use, features, AI , data engineering , Kubernetes , and speed. In this article, we’ll compare these three cloud platforms based on these criteria and help you decide which platform is the best for your needs. Configurability GCP offers great flexibility and configurability, allowing you to customize the platform to meet your specific requirements. GCP’s infrastructure is entirely open-source, making it easy to integrate with your existing systems. AWS is also highly configurable, with a range of configuration options available. Azure is less configurable than the other two platforms, with limited customization options. In terms of configurability, all three cloud platforms offer...

P ython is a powerful high level object oriented programming language with a simple syntax. It has many applications but the major ones are web development, software development and data science. Data science is a field where meaningful insights are extracted from data to allow for decision making and planning in businesses. It combines math, statistics, programming, machine learning, artificial intelligence and advanced analytics.  Data Science project life cycle The data science project life cycle consists of various processes which include: data collection, data cleaning, exploratory data analysis, model building and model deployment. Python has multiple libraries which facilitates these processes hence making it suitable for data science. Examples of these libraries are pandas for data analysis, wrangling and cleaning, matplotlib and seaborn for data visualization, tensor flow and scikit-learn for machine learning, keras and pyTorch for deep learning, SciPy and NumPy for mathem...

The Power of AI and Blockchain Integration The integration of AI and blockchain technology has tremendous potential to transform various industries. Blockchain provides a secure platform for data storage and management, while AI brings the capability to process and analyze large amounts of information. Together, these technologies can lead to the creation of solutions that are faster, more secure, and more efficient than current systems. For example, in finance, AI-powered trading algorithms can be used on decentralized exchanges, reducing trading costs and eliminating intermediaries. In healthcare, the combination of AI and blockchain can provide patients with control over their electronic health records, stored on the blockchain and analyzed by AI algorithms to give personalized health advice. Smart contracts, self-executing agreements stored on the blockchain, can be improved with AI. These contracts can automatically adapt to changing conditions and make decisions based on real-tim...

We are pleased to announce a major update to JupyterLab Desktop which adds many new features with main focus on the user experience. JupyterLab Desktop is the cross-platform desktop application for JupyterLab and it is the quickest and easiest way to get started with Jupyter notebooks on your personal computer. JupyterLab Desktop Welcome Page Users are now presented with the Welcome Page when the app is first launched. It contains links to several session create options on the left and the Jupyter News feed on the right. The news feed is populated using the Jupyter blog contents and is aimed to keep you up to date with the news and events related to Jupyter ecosystem projects. Clicking on a news item opens the blog post in browser. Sessions and Projects With this release we are introducing the concept of sessions and projects. Sessions are representations of local project launches and connections to existing JupyterLab servers. Each JupyterLab UI window in the app is associated with a ...

Introduction Overview of Python Programming Language: Python is a high-positioned, interpreted programming language that has come increasingly popular over time. It was first released in 1991 and has since come one of the most extensively used programming languages for a variety of operations, including web development, scientific computing, data analysis, artificial intelligence, and more. Python is known for its simplicity, readability, and versatility, making it a great choice for newcomers and educated inventors likewise. Benefits of Learning Python: There are several benefits to learning Python: Ease of use and readability:  Python has a straightforward and intuitive syntax that makes it easy to write, read, and understand code. Wide range of operations  Python is used in a variety of diligence and for a wide range of operations, from web development to data analysis and machine literacy. Large community and resources: Python has a large and probative community of invento...