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I'll tell you some knowledge shear about Learn about Depoly AI Models to Real-Time Applications
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Introduction?
Artificial Intelligence (AI) is no longer the stuff of science fiction; it's a tangible reality that's already shaping our world. From virtual assistants to self-driving cars, AI is becoming an integral part of our lives. But what's the secret sauce behind making AI work in real-time applications?
What is an AI application?
AI applications work by using artificial intelligence (AI) algorithms to process data and make predictions or decisions. AI algorithms are trained on large datasets of labeled data, which allows them to learn patterns and relationships in the data. Once an AI algorithm is trained, it can be used to make predictions or decisions on new data.
There are a variety of different AI algorithms, each with its own strengths and weaknesses. Some of the most common AI algorithms include:
Machine learning algorithms: Machine learning algorithms are able to learn from data without being explicitly programmed. This makes them ideal for tasks such as image recognition, natural language processing, and fraud detection.
Deep learning algorithms: Deep learning algorithms are a type of machine learning algorithm that uses artificial neural networks to learn from data. Artificial neural networks are inspired by the structure and function of the human brain, and they are able to learn complex patterns and relationships in data. Deep learning algorithms are used in a wide range of AI applications, including self-driving cars, medical diagnosis systems, and recommendation engines.
Natural language processing (NLP) algorithms: NLP algorithms are able to process and understand human language. This makes them ideal for tasks such as machine translation, text summarization, and sentiment analysis. NLP algorithms are used in a variety of AI applications, such as virtual assistants, chatbots, and social media monitoring tools.
AI applications are typically developed using a software development framework such as TensorFlow or PyTorch. These frameworks provide a set of tools and libraries that make it easier to develop and deploy AI applications.
AI applications are still under development, but they have the potential to revolutionize many industries and aspects of our lives. As AI technology continues to evolve, we can expect to see even more innovative and groundbreaking AI applications emerge in the future.
What is Website & Web app & Android mobile app & IOS mobile app & Windows app & MAC OS app & Linux app............................?
1. Website:
A website is a collection of web pages containing information, media, or other content accessible through a web browser.
A website is a collection of web pages that are accessed through a web browser (e.g., Chrome, Firefox).
It is typically hosted on web servers and is accessible over the internet.
Websites can be static (with fixed content) or dynamic (with content that can change based on user interactions or database queries).
Examples include news websites, e-commerce sites, and blogs.
Advantages:
Accessibility: Websites are easily accessible over the internet from various devices.
Low Cost: They are cost-effective to develop and maintain.
Cross-Platform Compatibility: Websites work on most devices and operating systems.
Disadvantages:
Limited Interactivity: Websites are typically less interactive compared to web apps.
Offline Access: They require an internet connection for access.
2. Web App:
A web app is a software application that runs in a web browser, providing functionality similar to traditional software.
A web app, short for web application, is a type of software application that runs on web browsers.
It functions like traditional software but is accessed through a web browser, eliminating the need for installation.
Web apps can range from simple online calculators to complex tools like Google Docs and web-based email clients, ChatGPT (Saas) like chat Bots
Advantages:
Cross-Platform: Works on different devices and operating systems without installation.
Real-Time Updates: Updates are instant as they occur on the server, eliminating manual downloads.
No Installation: No need for installation, reducing storage and compatibility issues.
Disadvantages:
Performance: Can be slower than native apps due to browser limitations.
Limited Offline Functionality: Some features may not work without an internet connection.
3. Android Mobile App:
Android apps are software applications developed for Android OS, running on smartphones and tablets.
An Android mobile app is a software application developed for devices running the Android operating system.
These apps can be downloaded and installed from the Google Play Store.
Android apps cover a wide range of categories, including games, productivity tools, social media, and more.
Advantages:
Large User Base: Android has a vast user base, offering a broad reach for your app.
Customizability: Developers have more freedom to customize Android apps.
Access to Hardware: Android apps can access a wide range of device features.
Disadvantages:
Fragmentation: Compatibility challenges due to various Android versions and devices.
Security Concerns: Android is more susceptible to malware and security risks.
4. iOS Mobile App:
iOS apps are designed for Apple's iOS operating system, running on iPhones, iPads, and iPod Touch devices. An iOS mobile app is a software application designed specifically for Apple's iOS operating system, which runs on iPhones, iPads, and iPod Touch devices.
These apps are available for download and installation from the Apple App Store.
iOS apps include games, productivity apps, social networking apps, and much more.
Advantages:
High-Quality User Experience: iOS apps are known for their user-friendly and smooth interfaces.
Security: iOS is considered more secure, reducing the risk of malware.
Stable Environment: A consistent platform with a more predictable user experience.
Disadvantages:
Limited Customization: Apple imposes strict guidelines, limiting app customization.
Limited Market Share: Smaller user base compared to Android.
5. Windows App:
Windows apps are designed for Microsoft Windows, suitable for desktop and tablet devices. A Windows app, also known as a Windows application or Windows Store app, is designed for the Windows operating system, typically for desktop and tablet devices.
Windows apps can be downloaded from the Microsoft Store.
These applications can include utilities, games, and productivity tools.
See this Link
Advantages:
Familiarity: Users are accustomed to the Windows environment.
Integration: Apps can integrate well with Windows features and services.
Desktop Use: Suitable for productivity and business applications.
Disadvantages:
Limited Mobile Presence: Limited reach on mobile devices.
Compatibility Issues: Different Windows versions may impact compatibility.
6. Mac OS App:
Mac OS apps are created for Apple's macOS, running on Macintosh computers. A Mac OS app, specifically built for macOS, runs on Apple Macintosh computers.
Mac apps can be downloaded and installed from the Mac App Store.
They cover a range of categories, including productivity, multimedia, and design software.
Advantages:
User Experience: Known for their user-friendly and visually appealing interfaces.
Security: macOS is relatively secure against malware.
Targeted Audience: Ideal for users within the Apple ecosystem.
Disadvantages:
Limited Market Share: Smaller user base compared to Windows.
Restricted Customization: Apple imposes strict design and functionality guidelines.
7. Linux App:
Linux apps are software applications developed for the Linux operating system. A Linux app is a software developed for the Linux operating system.
Unlike some other platforms, Linux doesn't have a centralized app store, so these applications may be distributed through various package managers or downloaded from the internet.
Linux apps encompass a wide variety of software, including development tools, system utilities, and desktop applications.
Advantages:
Open Source: Many Linux apps are open source, fostering a collaborative development environment.
Customization: Users can modify and customize software to suit their needs.
Stability: Linux apps tend to be stable and perform well on Linux systems.
Disadvantages:
Limited Commercial Software: Fewer options for commercial applications.
Fragmentation: Diverse distributions may lead to compatibility issues.
Above all Development process can be done TWO ways?
1- Traditional (Means Learn How to code and work with More Tools)
2- New 2023 Approach (Means Use NO-CODE (or) Low-Code Tools)
Embedded AI at the Edge & Libraries & API & Chrome Extensions?
Embedded AI at the Edge(IoT)?
You know how to build a great AI application, but you want more. You don’t want to
be limited to just running AI software on some computer, you want to bring it out in
the real physical world.
Embedded AI (or) Edge deployment at the edge involves integrating AI algorithms and models directly into IoT devices, such as sensors, cameras, or edge servers, rather than relying on remote cloud-based AI processing.
These AI algorithms enable IoT devices to process and analyze data locally, making real-time decisions and taking actions without the need for constant internet connectivity.
Example---->Ai based IOT(Tiny):- MCU net Link
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| Architecture of IoT |
1- Depoly ai model to NVIDIA Jetson Nano: Link
2- Depoly ai model to Raspberry Pi: Link
Libraries?
Libraries work like A collection of modules that are linked together is also known as a library. It has code bundles that can be used again and again in different programs. For programmers, it makes Python programming easier and simpler. Since then, we will not need to compose the same code for various projects
Libraries: I am not writing about not only TensorFlow; Pytorch.........etc
I write about building AI models to convert Python Libraries Like (Kalman filter lib & yolo lib & optical flow lib etc.......)
So many interesting researchers or others create Python AI Models to Convert libraries and publish them PyPI website...
How To Create a Python Library?
API?
AI API (I mean Someone creates the AI model and uses inference to deploy it as an API to serve everyone)
Like RAPIDAPI website is the best example of this deployment.
Chrome Extensions?
AI model Chrome extensions have gained popularity for several reasons, as they offer unique functionalities and benefits that cater to users' needs. Here are some of the key reasons behind their popularity:
Enhanced Browsing Experience:
AI-powered Chrome extensions enhance the overall browsing experience. They provide features such as content recommendations, smart searches, and personalization, which help users discover relevant content quickly and efficiently.
Productivity Boost:
Many AI model extensions are designed to boost productivity. They can automate repetitive tasks, manage emails, schedule appointments, and assist with time management. This can significantly improve efficiency and reduce manual workload.
Content Recommendations:
AI-powered extensions can analyze a user's browsing history and preferences to provide tailored content recommendations. This helps users discover new articles, products, or services that align with their interests.
Language Processing:
Some Chrome extensions leverage Natural Language Processing (NLP) to assist users with language-related tasks. They can provide grammar and spelling suggestions, translate text, or summarize content, improving communication and comprehension.
Data Analysis:
Extensions that integrate AI can analyze data from websites and present insights or visualizations, making data-driven decisions more accessible to users.
Security and Privacy:
AI extensions can enhance security and privacy by identifying potential threats or malicious websites, blocking ads and trackers, and helping users manage their online data footprint.
E-commerce Assistance:
Many users rely on AI-powered extensions when shopping online. These extensions can automatically find and apply coupons, compare prices, and even suggest relevant products, making online shopping more convenient and cost-effective.
Multilingual Support:
AI language tools can provide multilingual support, assisting users in understanding and communicating in different languages. This is especially valuable for international users or travelers.
Learning and Education:
AI model extensions offer support for learning and education. They can provide language learning tools, suggest educational content, and assist with research by summarizing articles or providing relevant references.
Automation and Integration:
AI extensions can integrate with other tools and platforms, automating actions across multiple apps or websites. This streamlines workflows and improves user convenience.
Customization:
Users appreciate the ability to customize their browsing experience. AI extensions allow for fine-tuning settings to align with individual preferences, making Chrome a more user-centric browser.
Constant Improvement:
Developers regularly update AI model extensions to enhance their capabilities and address user feedback. This commitment to improvement keeps users engaged and loyal to the extensions.
Overall, AI-model Chrome extensions have gained popularity because they bring intelligence, automation, and personalization to users' browsing experiences. They save time, increase efficiency, and provide valuable features that cater to the diverse needs of users in the digital age.
That's why the deployment of Chrome Extensions is the most popular thing
AI is Everywhere; Find it Problem & integrate it......................................!
Deploy AI Models In the Cloud Platform?
AWS (Amazon Web Services): Link
AWS boasts a vast community of developers, data scientists, and machine learning practitioners. This community provides valuable resources, tutorials, and support, making it easier to overcome challenges and find solutions to ML deployment issues.
AWS stands out as a comprehensive and versatile platform for deploying ML models. Its unique advantages include global scalability, a robust ecosystem, serverless computing, security, automation, and strong support from the developer community. These features empower businesses to efficiently deploy and manage ML applications while optimizing performance and cost.
AZURE (Microsoft Azure): Link
deploying ML models on Microsoft Azure offers a robust and versatile platform with an integrated ecosystem, enterprise-grade security, a dedicated ML service, and a strong focus on AI and IoT. Azure empowers businesses to efficiently deploy, manage, and scale ML applications while benefiting from extensive support and resources.
GCP (Google Cloud Platform): Link
Google maintains strong ties with the global AI and ML research community, ensuring access to the latest advancements and collaborative opportunities for innovation.
deploying ML models on Google Cloud Platform leverages Google's expertise in AI and ML, along with a wealth of advanced services, global reach, security, and cost transparency. This comprehensive platform enables businesses to harness the full potential of machine learning for a wide range of applications while benefiting from Google's leadership in the field.
What is the difference between the Traditional Approach vs AI based Approach?
Traditional Approach:
Rule-Based Systems:
Methodology: In the traditional approach, systems are typically rule-based. Human experts define rules and decision trees to solve problems.
Flexibility: The system's behavior is determined by predefined rules and logic.
Scalability: Adding new rules or modifying existing ones can be complex and time-consuming.
Data Processing:
Data Handling: Traditional systems may rely on structured databases and manual data entry.
Analysis: Data analysis often involves statistical methods or basic arithmetic operations.
Limited Learning: Traditional systems have limited learning capabilities and don't adapt to changing data patterns.
Low Automation: These systems often require human intervention for decision-making and problem-solving.
Repetitive Tasks: Automation is typically limited to repetitive, rule-based tasks.
Use Cases:
Common in Legacy Systems: The traditional approach is prevalent in legacy software systems and industries like manufacturing and finance.
Simple Applications: Best suited for applications with well-defined, rule-based processes.
AI-Based Approach:
Machine Learning and Neural Networks:
Methodology: AI-based systems, especially in machine learning and deep learning, use algorithms that can learn from data.
Flexibility: These systems adapt and learn from new data, improving their performance over time.
Scalability: AI models can scale with large datasets and complex tasks.
Data Variety: AI-based systems handle structured and unstructured data, including text, images, and audio.
Advanced Analysis: AI models use advanced statistical and mathematical techniques to recognize patterns and make predictions.
Continuous Learning: They learn continuously and adapt to changing data patterns.
High Automation: AI systems can make decisions, solve complex problems, and perform tasks autonomously.
Cognitive Tasks: Automation extends to cognitive tasks, such as natural language understanding and image recognition.
Diverse Applications: AI-based approaches are used in various fields, including healthcare, finance, autonomous vehicles, and more.
Complex Problem Solving: Ideal for applications where data is complex, patterns are not easily defined, or decision-making requires constant adaptation.
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| Basic diagram |
Advantages and Disadvantages:
Traditional Approach:
Advantages:
Simplicity: Easier to design and implement for well-structured problems.
Transparency: Rule-based systems are often more transparent, making it easier to understand their decision-making process.
Established: Proven in various industries and settings.
Disadvantages:
Limited Adaptability: Not suitable for tasks with changing, unstructured data.
Scalability Challenges: Adding rules can become unmanageable in complex systems.
Manual Data Entry: Often requires extensive manual data input and maintenance.
AI-Based Approach:
Advantages:
Adaptability: AI systems adapt to new data and changing environments.
Handling Complexity: Suitable for complex problems with large datasets.
Automation: Capable of automating cognitive tasks and decision-making.
Disadvantages:
Lack of Transparency: AI models like deep neural networks can be opaque in their decision-making.
Data Dependency: Highly reliant on quality and quantity of data.
Resource-Intensive: Training and maintaining AI models can be computationally expensive.
What is the difference between methods of Offline vs Online?
The difference between offline (no internet) and online (internet used) methods for AI applications is primarily related to how data is processed, where computing takes place, and the availability of real-time connectivity. Here's a breakdown of the distinctions between these two approaches:
Offline AI Applications (No Internet):
Data Processing:
Locally Processed: Offline AI applications perform data processing and analysis locally on the device or within a closed network environment, without internet connectivity.
Data Dependency:
Self-Contained Data: These applications rely on self-contained data sources or datasets that are available on the local device or network. They do not fetch or send data to external sources in real-time.
Latency:
Latency Isolated: Since offline AI applications do not depend on real-time internet data, they typically have lower latency, providing quick responses without the need for external data retrieval.
Use Cases:
Remote Locations: Offline AI applications are suitable for scenarios where there is limited or no internet access, such as in remote areas, or where data privacy and security are paramount.
Resource Management:
Resource-Optimized: These applications are designed to be resource-efficient and work effectively without relying on external servers or the cloud.
Online AI Applications (Internet Used):
Data Processing:
Cloud or Internet-Connected: Online AI applications often rely on cloud-based or internet-connected services to access and process data. They may utilize external servers or resources for computation and data retrieval.
Data Dependency:
Real-Time Data Access: These applications depend on real-time access to internet resources, databases, or external APIs for data collection, analysis, and decision-making.
Latency:
Variable Latency: Online AI applications may experience variable latency, depending on the speed and stability of the internet connection, as well as the response time of external services.
Use Cases:
Web-Based Services: Online AI applications are commonly used for web services, social media platforms, real-time communication, and collaborative tools, which require internet connectivity.
Resource Management:
Scalable: Online AI applications can leverage the computational power of cloud servers, making them suitable for tasks requiring significant computing resources.
Hybrid Approaches:
In some cases, a hybrid approach is used, which combines elements of both offline and online AI. For example, an application may perform data preprocessing and model training offline but use online services for real-time updates, cloud-based storage, or connectivity to external data sources.
In summary, the choice between offline and online AI methods depends on factors such as data availability, connectivity, latency requirements, and the specific use case. Offline AI is suitable for scenarios with limited or no internet access, while online AI excels in applications that require real-time data access and internet connectivity. Hybrid approaches provide flexibility to meet various requirements.
TOOLS USED TO DEPLOY AI MODEL
1- Flask
2- Django
3- Fast API
4- TensorFlow Serving - Link
5- Heroku
6- IoT (or) Single Board Computers
7- Chat Bots
8- Deploying your trained model using the Triton link
9- Torch serve/ ray.io Link
10- Streamlit
11-
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-- Must Read💥💥💥💥
1- Challenges in Deploying Machine Learning: A Survey of Case Studies: Link
2- Model Deployment Challenges: From ML Engineers: Link
3- How to Deploy Large-Size Deep Learning Models into Production: Link
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| Must Read These books for beginners |
LAST WORDS:-
One thing to keep in the MIND Ai and self-driving Car technologies are very vast...! Don't compare yourself to others, You can keep learning..........
Competition And Innovation Are Always happening...!
so you should get really Comfortable with change...
So keep slowly Learning step by step and implement, be motivated and persistent
Thanks for Reading This full blog
I hope you really Learn something from This Blog
Bye....!
BE MY FRIEND🥂
I hope you really Learn something from This Blog
Bye....!
BE MY FRIEND🥂
I'M NATARAAJHU
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