IBM Watson Studio is a comprehensive data science and AI development platform that enables users to build, train, and deploy machine learning models and AI applications. It offers a suite of tools for data preparation, model development, and collaboration, making it ideal for data scientists, analysts, and developers. Watson Studio supports a wide range of machine learning and deep learning algorithms, and it integrates with IBM Cloud services for scalable computing. Use cases include data cleaning and transformation, building and training models for tasks like classification and regression, developing AI-powered applications such as chatbots, automating machine learning with AutoAI, and deploying models for real-time predictions. Its collaborative features make it well-suited for team-based projects across industries like healthcare, finance, and retail.

What is IBM Watson Studio?

IBM Watson Studio is an integrated development environment designed to facilitate data science, machine learning, and AI model development. It offers a collaborative platform for data scientists, analysts, and business professionals to work together on data preparation, model building, and deployment. IBM Watson Studio integrates various tools and technologies, including open-source frameworks, Jupyter Notebooks, SPSS Modeler, and a range of Watson APIs, making it a comprehensive solution for the AI lifecycle.

As a cloud-based service, IBM Watson Studio streamlines the process of exploring data, training machine learning models, and deploying them into production environments. It provides an environment where users can easily scale their AI workflows, access powerful computational resources, and integrate with other IBM Cloud services.

Top 10 Use Cases of IBM Watson Studio

1. Predictive Maintenance
   • Analyze sensor data to predict equipment failures and schedule maintenance before downtime occurs.
2. Fraud Detection
   • Leverage machine learning models to identify patterns of fraudulent activity in financial transactions or insurance claims.
3. Customer Segmentation
   • Use clustering and classification techniques to group customers based on their behavior and preferences.
4. Supply Chain Optimization
   • Optimize inventory levels, forecast demand, and improve logistics by analyzing historical and real-time data.
5. Healthcare Insights
   • Build models to predict patient outcomes, identify at-risk individuals, and improve treatment recommendations.
6. Natural Language Processing (NLP)
   • Create applications that extract insights from unstructured text data, such as customer feedback or legal documents.
7. Churn Prediction
   • Identify customers at risk of leaving and implement targeted retention strategies.
8. Image Recognition and Analysis
   • Train deep learning models to classify images, detect objects, and analyze visual data for various industries.
9. Energy Consumption Forecasting
   • Analyze historical energy usage data to predict future consumption and optimize energy distribution.
10. Marketing Campaign Optimization
    • Leverage data to segment audiences, predict campaign performance, and allocate resources more effectively.

Features of IBM Watson Studio

• Collaboration Tools: Enables teams to work together on datasets, models, and notebooks in a unified environment.
• Flexible Deployment: Supports multiple deployment options, including cloud, on-premises, and hybrid setups.
• Integration with Watson APIs: Connects easily to Watson services for NLP, speech-to-text, image recognition, and more.
• Open-Source Compatibility: Integrates with popular open-source frameworks and libraries like TensorFlow, PyTorch, and scikit-learn.
• AutoAI: Automates key steps of the AI workflow, from data preparation to model selection and hyperparameter tuning.
• Data Preparation and Refinement: Offers tools for cleaning, transforming, and enriching datasets.
• Scalable Infrastructure: Provides access to IBM’s powerful cloud resources for large-scale training and deployment.

How IBM Watson Studio Works and Architecture

IBM Watson Studio is designed as a modular platform, allowing users to select the components that best fit their workflow. Its core architecture includes:

1. Data Access and Preparation: Connect to various data sources, including databases, cloud storage, and on-premises systems. Use built-in tools to clean, normalize, and transform data.
2. Development Environments: Work with Jupyter Notebooks, RStudio, SPSS Modeler, or the AutoAI graphical interface.
3. Machine Learning and Deep Learning: Build, train, and evaluate models using integrated machine learning libraries and frameworks.
4. Model Management and Deployment: Store models in a centralized repository, track version history, and deploy models as APIs or batch jobs.
5. Integration with IBM Cloud Services: Leverage additional Watson services, data storage solutions, and security features to enhance workflows.

By combining these components, IBM Watson Studio supports the entire AI lifecycle, from data exploration to production deployment.

How to Install IBM Watson Studio

IBM Watson Studio is a cloud-based platform, and it does not require installation in the traditional sense. However, if you’re looking to use it programmatically (e.g., through APIs, SDKs, or from a Python environment), you can interact with Watson Studio using the IBM Cloud SDK or directly through APIs.

1. Create an IBM Cloud Account

Before proceeding, ensure you have an IBM Cloud account. You can create one for free on the IBM Cloud website.

2. Install IBM Cloud CLI

To manage your IBM Cloud services from the command line, install the IBM Cloud CLI (Command Line Interface):

• Go to IBM Cloud CLI Installation and follow the instructions for your operating system.
• Once installed, open your terminal or command prompt and log in to IBM Cloud using: ibmcloud login

3. Install IBM Watson SDK for Python (Optional)

If you want to interact with IBM Watson services programmatically in Python, you can install the Watson SDK for Python. For example, to interact with Watson Studio, you will likely need the ibm-watson Python package for accessing various Watson services.

To install the IBM Watson SDK, use pip:

pip install ibm-watson

You may also want the ibm-cloud-sdk-core package for authentication and more advanced SDK features:

pip install ibm-cloud-sdk-core

4. Interact with IBM Watson Studio via APIs (Using Python SDK)

You can now interact with Watson Studio using the IBM Watson APIs. Below is an example code to interact with Watson Studio services programmatically.

First, set up your credentials (such as your API key and service URL). Then, use the Watson SDK to interact with Watson Studio.

Example (Python code):

from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
from ibm_watson import VisualRecognitionV3

# Set up your IBM Watson credentials
api_key = 'YOUR_API_KEY'
url = 'YOUR_SERVICE_URL'

# Set up the authenticator and service
authenticator = IAMAuthenticator(api_key)
visual_recognition = VisualRecognitionV3(version='2018-03-19', authenticator=authenticator)
visual_recognition.set_service_url(url)

# Example of analyzing an image
with open('example_image.jpg', 'rb') as image_file:
    result = visual_recognition.classify(images_file=image_file).get_result()

print(result)

Replace 'YOUR_API_KEY' and 'YOUR_SERVICE_URL' with the actual credentials from your IBM Cloud Watson service.

5. Access Watson Studio

To access Watson Studio via the API, you typically work with different Watson services (such as Watson Machine Learning, Watson Visual Recognition, and Watson Natural Language Understanding). You will use the corresponding Python SDKs to integrate these services with your Watson Studio workflows.

Basic Tutorials of IBM Watson Studio: Getting Started

To get started with IBM Watson Studio, here are some initial steps:

1. Create a Project:
   • Open Watson Studio, go to the Projects page, and click “Create Project.”
   • Choose “Standard” or “Enterprise” and provide a name for your project.
2. Add Data Assets:
   • Upload a CSV file or connect to a data source.
   • Use the Data Refinery tool to clean and transform your data.
3. Launch a Notebook:
   • Open the Notebooks tab and create a new notebook.
   • Choose a runtime environment, such as Python or R.
4. Build a Simple Model:
   • Use the AutoAI feature to automate model building.
   • Explore different algorithms, compare their performance, and select the best one.
5. Deploy Your Model:
   • After training, save your model to the Model Repository.
   • Deploy it as a REST API and test it with sample inputs.

The post What is IBM Watson Studio and Its Use Cases? appeared first on Artificial Intelligence.

What is Azure?

Azure is launched by Microsoft in February 2010.

It is a cloud computing platform including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) and also these can be used for services such as analytics, virtual computing, storage, networking, etc.

The Azure cloud platform has more than 200 products and cloud services designed to help the users and to solve today’s challenges and create the future.

What is the use of Azure?

There are 10 uses of Azures –

• Infrastructure as a Service (IaaS) and Platform as a Service (PaaS) capabilities:
• Security Offerings:
• Scalability and Ductility:
• Unmatched Hybrid Capabilities:
• Integrated Environment with Other Microsoft Tools:
• Analytics and Intelligence capabilities:
• Easy learning curve:
• Cost Efficient Platform:
• Interoperability:
• Identity & Access Management (IAM):

Compare Google Cloud Certification Vs Azure Certification Vs AWS

Amazon Web Services (AWS) –

AWS is the most widely used cloud platform, having a very vast experience with an expansive range of services tailored for different use cases. AWS is currently at the top, holding the world’s largest public cloud market share. It has been introduced in 2006 and was the first to offer infrastructure services through the Internet.

AWS incorporates many different cloud computing products and services comprising compute, storage, analytics, databases, networking, mobile, developer tools, management tools, and IoT.

Key Features of AWS

• Reliable, scalable, and low-cost infrastructure platform
• Over 200 fully-featured services
• A flexible and secure cloud computing environment
• 81 availability zones in 25 geographic regions across the world

Microsoft Azure–

Azure is the second-largest Cloud Service Provider after AWS. Microsoft Azure is Microsoft’s public cloud computing platform that was introduced in October 2008 and became available in public in February 2010. It is the biggest competitor of AWS. The platform provides a broad range of products and cloud services to allow businesses to find today’s challenges and create the future. Azure’s product portfolio comprises compute, AI & Machine Learning, analytics, databases, DevOps, IoT, networking, storage, web, and security.

Key Features of Azure

• More than 200 products and cloud services
• 90+ compliance offerings
• 95 percent of the Fortune 500 companies rely on Azure
• Datacentres in 60+ global regions

Google Cloud Platform–

GCP is new to cloud computing offered by Google. Many leading companies over industries keep faith on Google Cloud to solve their business problems. Some of its popular cloud services comprise computing, data analytics, data storage, networking, IoT, and machine learning.

Between all three cloud platforms, GCP provides the low-cost pricing model and has flexible cost-control, enable you to try the different services and features.

Key Features of GCP

• Open source, multi-cloud, and hybrid cloud
• Over 100 products
• A global network spanning across 200+ countries & territories, 85 zones
• Trust and security

Certification

The certification confirms your basic knowledge of cloud services and how those services are offered with Azure. Candidates should be competent to demonstrate a fundamental knowledge of cloud concepts, alongside with Azure services, workloads, security, privacy, pricing, and support.

A Microsoft azure certification allows you to chase a wide range of career options. You can become a cloud architect, a developer or solution architect. Additionally, the certification allows you to work in different industries at different locations.

List of Azure Certification

• Microsoft Certified: Azure – Fundamentals Exam AZ-900
• Microsoft Certified: Azure AI – Fundamentals Exam AI-900
• Microsoft Certified: Azure Data – Fundamentals Exam DP-900
• Microsoft Certified: Azure Administrator – Associate Exam AZ-104
• Microsoft Certified: Azure Developer – Associate Exam AZ-204
• Microsoft Certified: Azure Security Engineer – Associate Exam AZ-500
• Microsoft Certified: Azure AI Engineer – Associate Exam AI-100
• Microsoft Certified: Azure Data Scientist – Associate Exam DP-100
• Microsoft Certified: Azure Data Engineer – Associate Exams DP-200 and DP-201
• Microsoft Certified: Azure Database Administrator – Associate Exam DP-300
• Microsoft Certified Solutions Architect – Expert Exams AZ-303 and AZ-304
• Microsoft Certified: Azure DevOps Engineer – Expert Exam AZ-400

Azure Certification Path

Fundamentals-Level Certifications

• Microsoft Certified: Azure Fundamentals AZ-900
• Microsoft Certified: Azure Data Fundamentals DP-900
• Microsoft Certified: Azure AI Fundamentals AI-900
• Microsoft Security, Compliance, and Identity Fundamentals Azur SC-900

Associate-Level Certifications

• Microsoft Certified: Azure Administrator Associate AZ-104
• Microsoft Certified: Azure Developer Associate AZ-204
• Microsoft Certified: Azure Database Administrator Associate DP-300
• Microsoft Certified: Azure Security Engineer Associate AZ-500
• Microsoft Certified: Azure Data Engineer Associate DP-203
• Microsoft Certified: Azure AI Engineer Associate AI-102
• Microsoft Certified: Azure Stack Hub Operator Associate AZ-600

Expert-Level Certifications

• Microsoft Certified: Azure Solutions Architect Expert AZ-303
• Microsoft Certified: DevOps Engineer Expert AZ-400

Specialty Certifications

• Microsoft Certified: Azure IoT Developer Specialty AZ-220
• Microsoft Certified: Azure for SAP Workloads Specialty AZ-120

Azure Certification Cost

• Azure Fundamentals AZ-900 – ( $99 USD* )
• Azure Data Fundamentals DP-900 – ( $99 USD* )
• Azure AI Fundamentals AI-900 – ( $99 USD* )
• Microsoft Security, Compliance, and Identity Fundamentals Azur SC-900 – ( $99 USD* )
• Azure Administrator Associate AZ-104 – ( $165 USD* )
• Azure Developer Associate AZ-204 – ( $165 USD* )
• Azure Database Administrator Associate DP-300 – ( $165 USD* )
• Azure Security Engineer Associate AZ-500 – ( USD 165.00* )
• Azure Data Engineer Associate DP-203 – ( USD 165* )
• Azure AI Engineer Associate AI-102 – ( $165 USD* )
• Azure Stack Hub Operator Associate AZ-600 – ( $165 USD* )
• Azure Solutions Architect Expert AZ-303 ( USD 165.00* )
• DevOps Engineer Expert AZ-400 ( USD 165.00* )
• Azure IoT Developer Specialty AZ-220 ( USD 165.00* )
• Azure for SAP Workloads Specialty AZ-120 ( USD 165.00* )

Best salary for Azure Certified Professional

The average salary of Azure Solutions Architects in the U.S. $152,142 and Rs 1,575,000 in India.
The Azure Fundamentals certification average salary is $126,653 and Rs. 9,00,000 in India.
Azure Administrator Associate average salary is $125,993 and 3.0 Lakhs to Rs 16.9 Lakhs in India.

Best Azure Certification Tutorials

https://www.tutorialspoint.com/microsoft_azure/cloud_computing_overview.htm

Best Azure Certification Video Tutorials

Best Azure certification excercise dumps

https://www.examtopics.com/exams/microsoft/az-900/view/

https://www.prepaway.com/az-104-exam.html

Best Azure certification Ebooks

https://azure.microsoft.com/en-in/blog/free-ebook-the-developer-s-guide-to-microsoft-azure-now-available/

The post Complete Azure Certification Guide & tutorials appeared first on Artificial Intelligence.

Hi dear, welcome to the fascinating way of Jira’s concept of learning. Today I will tell you about the creation of projects in Jira. This is the most common and important feature because if you are using Jira then it is very important for you to know how to create the project in Jira so that you can start your journey with Jira. You will see what is the agenda in this article? So, in this article, we will learn how to create a project, what the classic project is and how to work with that, and how to create boards so, we will start from the first one. You will see first how to create projects so, let’s go to the cloud instance. There will be the cloud instance that you have created. In this section, you will see there is the default page of a Jira. If you want to change your default page in Jira then you can do this I will explain the setting later in this course in the article

How to create a project?

We will, we are going to create the projects so, you will go into the project section and you will see, you will haven’t create any project yet so, it will show you currently have no projects so for creating the project you will click on the create project button and you will see, it gives you options to enter your project name and to choose the template right now it’s showing the scrum template but if you want to change your project template then you can simply click on the change template and you will get a load of options available. There, you can choose according to your requirements the Kanban, scrum, task tracking, and bug tracking templates and many more are available there but you will use the scrum template so. You will go with that scrum one and select that scrum one. You will mention the project name and project name is project a. Once you will click on the advanced option you will see the key of a project. The key of a project should be very descriptive and easy to use. What is the logic behind the project key? It takes the first letter of our project name and if you want to name and if you want to change the project key then you would recommend changing it at the time of the creation of the projects because it is very difficult to change the project T later once you will create the project and adding issues or your requirements in the project so I would recommend you to change your project T then please change at the time of the creations so you will go and click on create button it will land you in that page where you will see the backlog options in the backlog you can create your requirements in the form of a story in the form of the task and you can create the epics.

I will explain to you what are the effects stories and tasks you can see the other issues types are available story tasks bugs and there are the options of creating the epic so, what are these issues types? So, you will see, what are options are available in that project you will see when you will create the project in Jira then it creates automatically a bowl. You can say this is a PA board if you want to create your own board then you will click on the plus icon and you can create the board it could be a scrum or it could be a Kanban board it’s up to you but for now, you will cancel it and the second option is a backlog as I told you, you can collect your requirements in the backlog and the third one is the active sprint then you don’t have any experience available there.      

What is a classic project?

So, it will look like as there are the three columns are available to do in progress and then I will explain to you how you can change these columns and how can you add more columns in this active sprint board and there are report options are available. As you will see, there are a lot of reports are available burndown charts, burnup charts sprint reports velocity, and the reports which are related to the issue analysis forecast and management and the others. You will see, there are no available sprints for this board that’s why this is the reason it will not show any report there so once you will start your sprint then how can you start your sprint? You will be able to see the report there so, you will back to the other one. You will see relays this is the best option. With the help of these release options you can create the versions and how you will create diversion you can manage your releases there next option is issues and filters all issues that you will create in that project will be listed down there so, you can choose all issues and it will show you all the issues in a list. I will show you how but this time you don’t have any issues so, you cannot see it and you will see there the other options are available. You will see open issues your reported issues recently viewed issues or recently created resort or updated issues as well so, I shall show you once you will create issues in the project and the next project is pages. You can show any pages which are related to your project there and the next one is the components. You can create the components and you can categorize your issues as for the components that are the best feature in the Jira and you can add the other items may be.

If you will click on that then you can add the repose repository link pages Ning or any shortcut which are frequently used so you will go into the last one and which is a project setting in that option that contains a lot of features. So first, we will start from the details you will see that you can change the project name and the project key there but you will be able to see that option because you will be admin and have access to change that project key but you will haven’t admin access then you can’t change it you can mention any URL but you can’t change the project type. If you want to change the project type then you cannot do it there for this you will have to create a new project and move your all the issues on that particular project type and there you can select an image and you may be put your project low logo and any other related images. You can write their description and mention the project lead there and you can set the default assignee as well the next option is people. In this option, you can add the people which are going to work into your project. So simply, you can add the people by clicking that arrow options you can choose the role and type the name then it will show automatically has done. I will explain to you later how you will create the roles and how can you add more team members? So that you can add the people to your project. The third one is a summary where you will be able to see the complete summary of your project. 

Related videos:

The post How to create a new project in Jira? appeared first on Artificial Intelligence.

Check out 10 top rising cloud robotics start-ups

Cloud robotics is an area of robotics that attempts to entreat cloud technologies such as cloud computing, cloud storage, and other internet technologies. It is not an easy task to run a cloud robotics start-ups. But it is amazing to see the rising number of start-ups specializing in cloud robotics. These start-ups are seen to work on innovative technologies.

Here is the list of 10 cloud robotics start-ups that are working on the innovation of technologies and products.

Graphcore

It was founded in 2016. The company is situated in the United Kingdom. The company’s IPU accelerators and popular software together make the fastest and most ductile platform for current and future machine intelligence applications, which lowers the cost of AI in the cloud and datacentre, improves performance and efficiency by between 10x to 100x. Graphcore systems shine at both training and reasoning. The highly collateral computational resources along with graph software tools and libraries enable researchers to explore machine intelligence across a much wider front than current solutions. This technology lets recent success in deep learning evolve rapidly towards useful, general artificial intelligence.

Arctoris

The company was founded in 2016 and is situated in the United Kingdom. Arctoris serves academic research groups and biotech companies, enabling scientists to directly configure a broad range of cancer research experiments online, and collects their results and detailed methods report instantly. It is not only effective for screening but is created for fundamental research, surrounding a wide range of techniques to cover cellular and molecular biology. Arctoris enables researchers and entrepreneurs to create and implement cutting-edge cellular and molecular biology research experiments and offers a complete system to recognize, analyze and visualize research data.

INVOLI

The company was founded in 2017, Switzerland. INVOLI serves in building infrastructure that enables drone revolution, ensuring their safe consolidation into today’s aviation world and avoiding collisions with aircraft. It offers an online platform, which collects data from a network of scaled-down control towers gathering maximum information on the position of every flying object in the sky, manually or automatically. Its technology enables everybody to see the air traffic encompassing them and take the most appropriate measures. Thus, what INVOLI brings to the aviation world is intelligent air traffic instruments and automatic collision avoidance tools, allowing low-risk flight plans and avoidance strategies and thus creating a safer, smarter sky for all.

Salty Cloud, PBC (Public Benefit Company)

It was founded in March 2017 in, USA. The company offers next-generation security automation technology to assist organizations around the globe to protect their cyber adversaries. Today, the company provides three security & risk automation tools to the public. It continues to find ways to provide free versions of its tools for educational institutions, state & local governments, and non-profits. One such offering is Dorkbot, the web vulnerability scanning service.

Bright Machines

It was founded in 2018 in, USA. The company aims at making manufacturing smarter and more efficient. It merges intelligent software with flexible factory robots and machine learning to help the customers deliver the next-generation products

Soar Robotics

The company offers independent operations at scale for urban and industrial applications with seamless connectivity and cloud intelligence. It uses hyper-realistic visual and physical simulation environments to safely test hardware by recreating real-life scenarios in virtual worlds. It helps in executing custom applications for proprietary use cases.

Freedom Robotics

It was founded in 2018, headquartered in San Francisco. Freedom Robotics integrates development and management tools in its cloud-based robotics management software (RMS) to help in prototype, creating, operating, and scaling robot fleets.

Covariant

It was founded in 2017, headquartered in Berkeley, Calif.

Covariant is creating AI called the “Covariant Brain” to make robots smarter. Covariant is competing directly with other cloud robotics start-ups such as Kindred and RightHand Robotics. Its technique is to use a variety of AI methods to train its robots, including reinforcement learning. It merges that with an off-the-shelf robot arm, a suction gripper, and a simple 2D camera system.

Aitomation – Workflow Automation

This company leverages robots (physically and virtually) for automating specific tasks. It utilizes the power of cloud computing and process automation to create and modify cloud robotic solutions. The company develops software robotics solutions, lean strategies, artificial intelligence (AI), and low-code applications, as well as cloud services. It allows the automation of long workflows and processes that run across multiple systems and organizational extremities.

RosHub – Robotic Fleet Management

It is a US-based start-up. It develops connectivity solutions for robot fleets. The company is also involved in prototyping, testing, and applying robotic solutions in unstructured public spaces. It allows the link of an open-source ROS (robotics operating system) that is combined efficiently with the support of existing tools and APIs.

The post TOP 10 CLOUD ROBOTICS START-UPS TO KEEP AN EYE ON appeared first on Artificial Intelligence.

How does cloud computing lead to improved healthcare outcomes?

The Healthcare industry is one of the fastest-growing sectors with a CAGR of 5% from 2019 to 2023. This is due to issues like the spread of chronic diseases, the aging population, and more. In the healthcare industry, a huge amount of data is created on a daily basis. Therefore, there is a need for the creation, storage, usage, and sharing of healthcare data. This caters to the need for cloud computing for quick, cost-effective and safe, and secure solutions.

Cloud computing to be precise, is the delivery of computing services (such as servers, networking, databases, analytics, intelligence, and storage) over the cloud (internet) to provide flexible resources, economies of scale, and faster innovation. Cloud computing helps in reducing operating costs, running infrastructure more effectively, etc. Thus, cloud computing enables the healthcare industry to break free from limitations while offering better patient outcomes.

Shifting to the cloud computing service is advantageous for both healthcare providers as well as for patients. The healthcare cloud computing trend is expected to grow to 64.7 billion US dollars by 2025.

Cloud Computing in Healthcare Sectors

Cloud computing assists healthcare providers in reducing operational expenses while providing improved, personalized care. Also, it helps to drive efficient workflows thus leading to better service. Parallel to this, cloud computing helps the patients to receive quick responses from the healthcare industry and also helps them to keep track of their health better through healthcare data offered by cloud solutions.

Healthcare providers and hospitals across the globe are increasing the application of cloud computing to solve problems like care coordination, data security, and population health.

Advanced cloud computing allows medical research innovation by helping researchers access important data sets under solid control. Cloud has also proved a powerful tool in hospital settings.

Advanced compute, storage capability, and database, combined with artificial intelligence and machine learning tools help the healthcare industry in building solutions and providing services to the patients. Innovative cloud computing technology will serve to equal access to care, enable the healthcare provider to make the best decision, and also access to significant medical research.

Enhanced database capabilities and advanced computing power are making preventative population health analysis a reality.

How does Cloud Computing lead to improved healthcare outcomes?

Cost Reduction

The basic theory of cloud computing is the availability of computer services like data storage and computing power. Therefore, this frees the hospitals and healthcare providers from the purchase of hardware and servers for storage. There are no charges kindred with cloud storage of data. The healthcare industry can only pay for the resources they use and thus results in huge cost savings. Cloud computing also offers an efficient environment for scaling which is highly desirable by healthcare providers for improved healthcare service.

Easy Interoperability

Cloud computing helps in establishing data combinations (irrespective of the point of origin or storage) throughout the healthcare system. This process is called Interoperability. As a result of interoperability data of the patients become easily available for distribution. This helps in gaining visions to advance healthcare planning and delivery. Thus, cloud computing enables healthcare providers to get easy access to the patient data collected from various sources and provide prescriptions and treatment protocols on time.

Approach to high powered analytics

The Healthcare industry contains data that are both structured as well as unstructured. Cloud helps in collecting and collating important data of the patients from different sources. With the modern computing power of the cloud, the processing of large datasets becomes more viable. The application of Big Data analytics and Artificial Intelligence on patient data also paves the way for developing more advanced care plans for patients.

Patient’s access to data

Cloud computing allows patients to get control over their own health. Cloud computing makes it accessible for the patients to get hold of their data from the cloud and this leads to patient participation in making decisions of their own health.

Telemedicine Capabilities

Cloud storage of data offers remote accessibility of data. The integration of cloud computing with the healthcare industry carries the ability to improve a number of healthcare-related functions such as telemedicine, virtual medication, and post-hospitalization care plans. Telemedicine apps add the element of convenience to healthcare delivery while upgrading the patient experience. Cloud-based telehealth systems and applications allow easy sharing of healthcare data, improve accessibility and provide healthcare coverage to the patients during the preventative, treatment as well as recovery phase.

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Artificial intelligence is coming to the cloud… including cloud storage – and it’s hard to even imagine how much things could change as a result. Hundreds of millions use a file-sharing app for personal reasons – family photos, documents, etc. But businesses need storage space, too. Plenty of providers offer huge amounts of cloud storage for as little as US$100 per year, but the day is coming when it won’t matter much which provider gives more space for a cheaper price. We’ll all likely be hunting for the service with the best AI. 

 As of this month, Google Photos no longer offers unlimited free storage. But in that somewhat sad, although inevitable news, is some missed the news about the huge leaps Google Photos has taken with machine learning. The “memories” feature is a stand-out. Photos that show up in “memories” are automatically selected and sorted with AI help, but Google is finding ways to give users more control over how they are sorted and shown. For example, the service will pick out pics that might have ‘rather subtle patterns’ and will then generate a gallery or “story” for you with them. Even something as simple as a “visual trend” might be all the AI needs to offer you a selection or a “story.” And AI is helping photos be more sensitive as well. A sad event – such as a breakup – might trigger the hiding of certain photos that feature certain people.

 In a blog post, the Vice President of Google Photos Shimrit Ben-Yair wrote about another interesting feature that wouldn’t be possible without AI. “Last December,” Ben-Yair wrote in May 2021, “we launched Cinematic photos, which use machine learning to create vivid, 3D versions of your photos. Taking this a step further, we’re using computational photography to really bring these memories to life. When you’re trying to get the perfect photo, you usually take the same shot two or three times — trying to get all my kids smiling and looking at the camera at the same time is a challenge, to say the least. Using neural networks to synthesize the movement between two nearly identical photos and fill in the gaps with new frames, we can create vivid, moving images called Cinematic moments.”

Does all that scare or excite you? For some, things like the above evoke a chapter out of a dystopian novel of the future, but for others, they seem like nothing more than a logical conclusion of computing. We want machines that serve us, and if we train them how to do that really well, should the fact that they hear, read and analyze our lives, photos, videos, conversations, and more give any cause for concern? Is a “filled in” photo fake or creative?

How would you feel about the following: You just finished a phone conversation with your sister during which you discussed a trip your families made to Italy in 2005. You also talked about an upcoming anniversary and the need to purchase a gift. A little later that same day you receive a folder from your AI cloud-based assistant that contains images of the 2005 trip to Italy and an offer to send them to your sister. Then, a few hours later as you are driving – or hopefully being automatically driven – up pops on one of the vehicle’s screens a selection of appropriate anniversary gifts.

 This is a future that we taste already. Alexa, for example, sits in many homes listening to us and being directed by us. Voice technology, facial recognition, the synchronization of calendars, and schedules are all so commonplace that they’re hardly worth commenting on. Soon, many believe, artificial intelligence is going to do a lot more than just make “smart” suggestions or connections. Artificial intelligence alongside cloud computing could revolutionize business. Call it a merger. Cloud computing and AI allow users to analyze and draw conclusions from stored data. This could result in significantly improved productivity. Think streamlined workflows using AI tools that make corrections to inefficient systems, without a human ever lifting a finger.

Most of us would think 5T of cloud storage could last a lifetime but think about the storage needs of, say, Tesla. Big businesses – with the aid of AI – are already combining cost-effective public and private clouds as well as what’s being called “hybrid clouds,” ideas that will need to be perfected as the amount of data that will need to be stored is going to be astronomical.

Artificial intelligence or machine learning is set to and is already disrupting life as we knew it just a decade ago. For a futurist, it’s exciting to know they will likely live to see big leaps forward, as even a person living to the year 2040 is going to experience at least a sampling of the radical changes coming in the second half of the century. The “AI cloud” is now more of a concept than a firm reality and is very slowly being used by a select number of companies. But AI tools and software keep delivering value to cloud computing and an integrated reality is just around the corner.

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Cirrascale Cloud Services, a premier cloud services provider of deep learning infrastructure solutions for autonomous vehicles, natural language processing, and computer vision workflows, announced its dedicated, multi-GPU deep learning cloud servers support the NVIDIA A100 80GB and A30 Tensor Core GPUs. With record-setting performance across every category on the latest release of MLPerf, these latest offerings provide enterprise customers with mainstream options for a broad range of AI inference, training, graphics, and traditional enterprise compute workloads.

“Model sizes and datasets in general are growing fast and our customers are searching for the best solutions to increase overall performance and memory bandwidth to tackle their workloads in record time,” said Mike LaPan, vice president, Cirrascale Cloud Services. “The NVIDIA A100 80GB Tensor Core GPU delivers this and more. Along with the new A30 Tensor Core GPU with 24GB HBM2 memory, these GPUs enable today’s elastic data center and deliver maximum value for enterprises.”

The NVIDIA A100 80GB Tensor Core GPU introduces groundbreaking features to optimize inference workloads. It accelerates a full range of precision, from FP32 to INT4. Multi-Instance GPU (MIG) technology enables up to 7 instances with up to 10GB of memory to operate simultaneously on a single A100 for optimal utilization of compute resources. Structural sparsity support delivers up to 2X more performance on top of the A100 GPU’s other inference performance gains. A100 provides up to 20x higher performance over the NVIDIA Volta® and on modern conversational AI models like BERT Large, A100 accelerates inference throughput by 100x over CPUs.

Also available through Cirrascale Cloud Services is the NVIDIA A30 Tensor Core GPU, which delivers versatile performance supporting a broad range of AI inference and mainstream enterprise compute workloads, such as recommender systems, conversational AI and computer vision. The A30 also supports MIG technology, delivering superior price/performance with up to 4 instances containing 6GB of memory, perfectly suited to handle entry-level applications. Cirrascale’s accelerated cloud server solutions with NVIDIA A30 GPUs provide the needed compute power — along with large HBM2 memory, 933GB/sec of memory bandwidth, and scalability with NVIDIA NVLink® interconnect technology — to tackle massive datasets and turn them into valuable insights.

“Customers deploying the world’s most powerful GPUs within Cirrascale Cloud Services can accelerate their compute-intensive machine learning and AI workflows better than ever,” said Paresh Kharya, senior director of Product Management, Data Center Computing at NVIDIA.

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One of the initial hesitations in many enterprise organizations moving into the cloud in the last decade was the question of security. Significant amounts of money had been put into corporate firewalls, and now technology companies were suggesting corporate data reside outside that security barrier. Early questions were addressed, and information began to move into the cloud. However, nothing stands still, and the extra volume of data and networking intersects with the increased complexity of attacks, and artificial intelligence (AI) is being used to keep things safe.

The initial hesitation for enterprise organizations to move to the cloud was met by data centers improving hardware and networking security, while the cloud software providers, both cloud hosts and application providers, increased software security past what was initially offered in the cloud. Much of that was taking knowledge from on-premises security and scaling it to the larger systems in the cloud. However, there’s also more flexibility for attacks in the cloud, so new techniques had to be added. In addition, most organizations are in a hybrid ecosystem, so the on-premises and cloud security must coordinate.

This means an opportunity for AI to provide enhanced security. As mentioned with other machine solutions, security is a mix different AI and non-AI techniques to fit the problem. For instance, there’s deep learning. Supervised learning can be used for known attacks, while unsupervised learning can be used to detect anomalous events in a sparse dataset. Reinforcement learning classification can even be done with statistical analysis in time series, and not always require AI. That can provide faster performance in appropriate cases.

On a quick tangent, let’s talk about supervised learning and reinforcement learning. Some folks present them as different; I think of the latter as an extension of the former. “Classic” supervised learning is when input is labeled and the labels are important for the AI system, as they are used to understand and organize the data. When there are errors, humans add more annotations and labels to existing data, or they add more data. In reinforcement learning, feedback for the neural network is given as to how far the results of an iteration are from a set goal. That feedback can be put back into the system by programmers changing weights or, in more advanced systems, by the AI software doing the comparison and adapting on its own. That is a type of supervision, but I’ll admit it’s a philosophical argument.

Back on track, let’s add another complexity. In the early days of the cloud, applications were larger but still followed a similar pattern of scale-up and scale-out. Now there’s something changing both environments: containers. Simply put, a container is a piece of software that wraps around an application, it has basic services and even a virtual operating system. That allows containers to run on multiple operating systems regardless of internal application code. It also allows cloud platforms and servers to more finely control services to their clients in order to meet service level agreements (SLA’s) that provide quality performance to the end customer.

“As more applications migrate to a container architecture, it’s important for security to keep up,” said Tanuj Gulati, CTO, Securonix. “Light weight collectors can run within application containers, such as with Docker, collecting and sending relevant event logs to the more robust security monitoring applications running separately. This provides strong security in the new environments without significant burden being added to application performance.”

 In my discussion with Tanuj Gulati, he explained that they first worked in the virtual machine (VM) environment in local data centers. That provided both an understand that helped extend security to Docker, but also in integrating security between on-premises and cloud systems in a hybrid environment.

Detection V Response

Artificial intelligence is focused on detection, but a complete system must also address the response to a perceived threat. The basic system can detect attacks, and based on known problems rules can then determine responses. Unknown problems have unknown responses. Humans must be flagged to handle those questionable transactions, then feedback can be given to reinforce the system. Depending on how complex a system is created, those new rules can be incorporated into the neural network or added to a rules set.

The state of the industry, both in technology and human comfort levels, shows that there will continue to be human oversight before responses to new attacks as the predominant method in the next few years. Advances will push the security industry into more system action and then reporting, review, and adjustment by humans, but that will happen slowly. What will help is that better explainability will be required, as the deep learning “black box” will have to become more transparent.

Cloud computing and artificial intelligence are growing in parallel. The complexity of the cloud is driving the need for AI, but the complexity of AI is also creating the need for it to work better in the cloud environment with efficiency, transparency and control.

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The Databricks Unified Data Platform, using the Google Kubernetes Engine, can be deployed in a containerized cloud environment and link to Google BigQuery and other GCP services.

Databricks’ Unified Data Platform will be available on the Google Cloud Platform beginning in April, the two companies said Wednesday, completing a trifecta for Databricks whose software already runs on the Amazon Web Services and Microsoft Azure cloud platforms.

Databrick’s software also will be integrated with the Google BigQuery business analytics system and leverage the Google Kubernetes Engine – enabling businesses and organizations to deploy Databricks in a containerized cloud environment for the first time.

With Databricks available on Google Cloud, customers can create “data lakehouses” – a combination of data lake and data warehouse systems that Databricks has been promoting – for a range of business analytics, data engineering, data science and machine learning tasks.

The alliance with Google comes at a time when Databricks has been gaining momentum – and a lot of attention – in the big data space. Earlier this month the company raised $1 billion in a Series G round of financing, boosting its post-money valuation to $28 billion in a move many see as a precursor to an IPO sometime this year.

The alliance also comes as more businesses and organizations are choosing to store their data – especially data managed for analytical tasks – in the cloud and run their business analysis and data visualization tools on cloud platforms.

“The cloud is huge now. But it’s really the beginning of the journey for a lot of customers – especially when it comes to data and data warehouses,” said David Meyer, Databricks senior vice president of product management, in an interview with CRN.

“We’re seeing a very significant acceleration of, specifically, those data workloads moving to the cloud,” said Kevin Ichhpurani, Google corporate vice president and head of Google Cloud global ecosystem and business development, also in the interview with CRN.

Ichhpurani said those efforts to move data and business analytics to the cloud are being driven by rapidly growing data volumes and the role that data analytics, data orchestration and machine learning are playing in digital transformation initiatives and the re-invention of business processes.

The Google executive also emphasized that the alliance goes beyond joint development to include go-to-market and sales activities – including working with channel partners.

The two companies said that members of their partner ecosystems have committed to support Databricks on Google Cloud including systems integrators Accenture, Cognizant and Deloitte; strategic service providers such as Slalom and SADA; and software developer partners including Qlik, Tableau, Informatica and Confluent.

Meyer noted that many businesses and organizations are decommissioning older data warehouse and analytics systems and moving them to the cloud. “It’s a great time for these SIs [systems integrators] to lean in and help these customers figure out where they want to be three or four years from now.”

Ichhpurani said several of the systems integrators who partner with Google and Databricks are interested in launching practices around the Databricks-on-Google Cloud offering.

The Databricks Unified Data Platform on Google Cloud is currently in beta testing and is expected to be generally available in April. It will be available through the Google Cloud Marketplace for simpler procurement, user provisioning, sign-on and unified billing.

Databricks on GCP will allow customers to rapidly deploy and scale Databrick’s software on Google Cloud’s global network and easily adjust the usage rate based on their current needs.

The two companies also said the advanced security and data protection controls provided by the GCP will make the combination especially attractive for use within highly regulated industries.

By using the Google Kubernetes Engine as the operating environment for running Databricks on Google Cloud, Databricks can leverage Kubernetes managed services for security, network policy and compute, according to the two companies. It also speeds the release of new features at scale and at lower cost.

The companies have also developed connectors to integrate Databricks with Google BigQuery, Google Cloud Storage, Google’s Looker data exploration and discovery tool, and Google’s Pub/Sub messaging and data ingestion system.

“By combining Databricks’ capabilities in data engineering and analytics with Google Cloud’s global, secure network—and our expertise in analytics and delivering containerized applications—we can help companies transform their businesses through the power of data,” said Thomas Kurian, Google Cloud CEO, in a statement.

The availability of Databricks on Google Cloud “deliver[s] on our shared vision of a simplified, open and unified data platform that supports all analytics and AI use-cases that will empower our customers to innovate even faster,” said Databricks CEO Ali Ghodsi in the same statement. “This is a pivotal milestone that underscores our commitment to enable customer flexibility and choice with a seamless experience across cloud platforms.”

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Latest released the research study on Global Cloud Microservices Market, offers a detailed overview of the factors influencing the global business scope. Cloud Microservices Market research report shows the latest market insights, current situation analysis with upcoming trends and breakdown of the products and services. The report provides key statistics on the market status, size, share, growth factors of the Cloud Microservices. The study covers emerging player’s data, including: competitive landscape, sales, revenue and global market share of top manufacturers are AWS (United States),CA Technologies (United States),Contino (United Kingdom),CoScale (Belgium),IBM (United States),Idexcel (United States),Infosys (India),Kontena (Finland),Macaw Software (United States),Marlabs (United States),Microsoft (United States),Netifi (United States),NGINX (United States),OpenLegacy (United States),Oracle (United States),Pivotal Software (United States).

Definition:
A cloud microservices is known as a software development technique and a collection of services which are loosely coupled. Cloud Microservices is a method of developing software systems which tries to emphasis on developing single-function modules with the well-defined operations and interfaces. It is also known as the microservice architecture. Cloud microservices are gaining popularity among companies that need greater scalability and agility. In businesses across the industries, i.e., from telecommunications and retail to the financial services and manufacturing, IT teams are choosing cloud microservices, to develop new applications and break down monoliths

Keep yourself up-to-date with latest market trends and changing dynamics due to COVID Impact and Economic Slowdown globally. Maintain a competitive edge by sizing up with available business opportunity in Cloud Microservices Market various segments and emerging territory.

Market Trend:
Digital transformations

Market Drivers:
Proliferation of the microservices architecture
Customer-oriented business

Restraints:
Security and compliance

The Global Cloud Microservices Market segments and Market Data Break Down are illuminated below:
by Type (Stateless, Persistence, Aggregator), Industry (BFSI, IT and ITeS, Telecommunications, Government, Healthcare, Retail and eCommerce, Media and Entertainment, Transportation and Logistics, Manufacturing, Others (Education, Energy & Utilities, and Travel & Hospitality)), Services (Consulting services, Integration services, Training, support and maintained services), Organisation (SMEs, Large Enterprises), Deployment model (Public Cloud, Private Cloud, Hybrid Cloud), Component (Platform, Services)

Analyst at AMA have conducted special survey and have connected with opinion leaders and Industry experts from various region to minutely understand impact on growth as well as local reforms to fight the situation. A special chapter in the study presents Impact Analysis of COVID-19 on Global Cloud Microservices Market along with tables and graphs related to various country and segments showcasing impact on growth trends.

Region Included are: North America, Europe, Asia Pacific, Oceania, South America, Middle East & Africa
Country Level Break-Up: United States, Canada, Mexico, Brazil, Argentina, Colombia, Chile, South Africa, Nigeria, Tunisia, Morocco, Germany, United Kingdom (UK), the Netherlands, Spain, Italy, Belgium, Austria, Turkey, Russia, France, Poland, Israel, United Arab Emirates, Qatar, Saudi Arabia, China, Japan, Taiwan, South Korea, Singapore, India, Australia and New Zealand etc.

Strategic Points Covered in Table of Content of Global Cloud Microservices Market:
Chapter 1: Introduction, market driving force product Objective of Study and Research Scope the Cloud Microservices market
Chapter 2: Exclusive Summary – the basic information of the Cloud Microservices Market.
Chapter 3: Displaying the Market Dynamics- Drivers, Trends and Challenges of the Cloud Microservices
Chapter 4: Presenting the Cloud Microservices Market Factor Analysis Porters Five Forces, Supply/Value Chain, PESTEL analysis, Market Entropy, Patent/Trademark Analysis.
Chapter 5: Displaying market size by Type, End User and Region 2015-2020
Chapter 6: Evaluating the leading manufacturers of the Cloud Microservices market which consists of its Competitive Landscape, Peer Group Analysis, BCG Matrix & Company Profile
Chapter 7: To evaluate the market by segments, by countries and by manufacturers with revenue share and sales by key countries (2021-2026).
Chapter 8 & 9: Displaying the Appendix, Methodology and Data Source

Finally, Cloud Microservices Market is a valuable source of guidance for individuals and companies in decision framework.

Data Sources & Methodology

The primary sources involves the industry experts from the Global Cloud Microservices Market including the management organizations, processing organizations, analytics service providers of the industry’s value chain. All primary sources were interviewed to gather and authenticate qualitative & quantitative information and determine the future prospects.

In the extensive primary research process undertaken for this study, the primary sources – Postal Surveys, telephone, Online & Face-to-Face Survey were considered to obtain and verify both qualitative and quantitative aspects of this research study. When it comes to secondary sources Company’s Annual reports, press Releases, Websites, Investor Presentation, Conference Call transcripts, Webinar, Journals, Regulators, National Customs and Industry Associations were given primary weight-age.

What benefits does AMA research study is going to provide?
• Latest industry influencing trends and development scenario
• Open up New Markets
• To Seize powerful market opportunities
• Key decision in planning and to further expand market share
• Identify Key Business Segments, Market proposition & Gap Analysis
• Assisting in allocating marketing investments

Definitively, this report will give you an unmistakable perspective on every single reality of the market without a need to allude to some other research report or an information source. Our report will give all of you the realities about the past, present, and eventual fate of the concerned Market.

Thanks for reading this article; you can also get individual chapter wise section or region wise report version like North America, Europe or Southeast Asia.

Craig Francis (PR & Marketing Manager)
AMA Research & Media LLP
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Advance Market Analytics is Global leaders of Market Research Industry provides the quantified B2B research to Fortune 500 companies on high growth emerging opportunities which will impact more than 80% of worldwide companies’ revenues.
Our Analyst is tracking high growth study with detailed statistical and in-depth analysis of market trends & dynamics that provide a complete overview of the industry. We follow an extensive research methodology coupled with critical insights related industry factors and market forces to generate the best value for our clients. We Provides reliable primary and secondary data sources, our analysts and consultants derive informative and usable data suited for our clients business needs. The research study enables clients to meet varied market objectives a from global footprint expansion to supply chain optimization and from competitor profiling to M&As.

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