Tag: N|Solid

Measuring latency from the client side using Chrome DevTools and N|Solid

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Almost every modern web browser includes a powerful suite of developer tools. In our previous blog-post we covered __How to Measure Node.js server response time with N|Solid__, read more ???? HERE.

The developer tools have a lot of capabilities, from inspecting the current HTML-CSS and Javascript code to inspecting the current ongoing network communication client-server.

To open the devtools and analyze the network, you can go to:

“More Tools” > “Developer Tools” > “Network”

Being on the devtools screen now, you can visit your Fastify API(or express) http://localhost:3000 after you get an HTTP response, you will see the request itself, the HTTP status code, the response size, and the response time.

GIF 1 – devtools.gif

An explanation for the time measured by the Chrome DevTools, HERE.

Let’s measure the Client-Side Latency.

Remove the delay timer logic on your application and restart the Node.js process.

import Fastify from “fastify”;

const fastify = Fastify({
logger: true,
});

// Declare the root route and delay the response randomly
fastify.get(“/”, async function (request, reply) {
return { delayTime: 0 };
});

// Run the server!
fastify.listen({ port: 3000 }, function (err, address) {
if (err) {
fastify.log.error(err);
process.exit(1);
}
});

NOTE: Discover another useful code snippet in our ‘Measure Node.js server response time with N|Solid’ article! This time, learn how to simulate server-side latency to further test your application’s performance. Check it out: ???? HERE

After this, you will get the fastest response times of ~1 ms, I was able to execute 139k requests in 10 secs using autocannon.

npx autocannon localhost:3000

The main point here is to see how it behaves when we have a poor internet connection on the client side and the best possible performance on the server side; for this, we can simulate high latency and slow internet connections on the client side, using Chrome devtools which has this option out of the box.

Simulate Client-Side latency

In the Chrome Devtools:

Go to the “Network Conditions” option > Select the option “Network Throttling” > Set it to __“Slow 3G”__.

If you request your browser to the URL http://localhost:3000/, you’ll see a long response time, in our case, __~2 seconds__.

This response time doesn’t mean the server takes that long to process the request and return an answer that was the amount of time that the answer took to transfer over the network till it arrived at the client side.

If you check your Fastify logs of the N|Solid metrics, you’ll see the server only took ~1 ms to return.

Check your logs with N|Solid
HERE

In our case, the response time was 0.3 ms

“responseTime”:0.3490520715713501

Can I improve/help the client-side latency?

Well, it is possible to improve the user experience on client-side devices with high latency when you use a Content Delivery Network to cache content on edge locations geographically near to users’ devices; even implementing some simple compression mechanism will improve the load times on users’ devices with high latency.

Look at this Jonas, our Principal OSS Engineer, blog-post and see ???????? __How to create a fast SSR application__.

Connect with NodeSource

If you have any questions, please contact us at [email protected] or through this form.

To get the best out of Node.js and experience the benefits of its integrated features, including OpenTelemetry support, SBOM integration, and machine learning capabilities. Sign up for a free trial and see how N|Solid can help you achieve your development and operations goals. #KnowyourNode

Experience the Benefits of N|Solid’s Integrated Features
Sign up for a Free Trial Today

Measure Node.js server response time with N|Solid

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As software developers, we constantly face new challenges in an ever-changing ecosystem. However, we must always remember the importance of addressing performance and security concerns, which remain at the top of our priority list.

To ensure that our applications based on Node.js can meet our performance and scalability needs without compromising security or incurring costly infrastructure changes, we must be aware of the importance of network optimization in Node.js.

The Impact of Latency/Ping Time on the Performance and Speed of Your Node.js Application

IMG – Ping Cats – via GIPHY

This communication, known as network ping time or latency, is a crucial factor that impacts the performance and speed of your application. Knowing how to measure network ping time between the browser and the server is essential for developers who want to optimize their applications and provide a better user experience. _Have you ever wondered how long it takes for your application to communicate with the server? _

Network Optimization in Node.js

To ensure the optimal performance and scalability of our Node.js applications, we must accurately measure our HTTP server’s connection and response time. Doing so enables us to identify and address potential bottlenecks without compromising security or incurring unnecessary infrastructure changes.

Before delving deeper into measuring connection and response time, let’s explore fundamental concepts and critical differentiators in the network landscape.

HTTP vs. WebSocket:

HTTP and WebSocket are communication protocols used in web development but serve different purposes. HTTP is a stateless protocol commonly used for client-server communication, while WebSocket enables full-duplex communication between clients and servers, allowing real-time data exchange.

Types of Connections and Versions:

When creating APIs, HTTP as a protocol and standard has different versions, such as HTTP 1.1 and 2.0. Additionally, APIs may use alternative protocols like gRPC, which offer different features and capabilities. Understanding these options empowers developers to choose the most suitable tools for their web servers.

TCP/IP Basics:

The Transmission Control Protocol (TCP) and Internet Protocol (IP) are fundamental protocols that form the backbone of computer networks. Among TCP’s critical processes is the three-way handshake, which plays a vital role in establishing a secure and dependable connection between two endpoints. This handshake ensures the orderly and reliable transmission of data. TLS/SSL encryption enhances security, adding an extra layer of protection to the communication between the client and the server.

HTTP vs. HTTPS:

HTTP operates over plain text, which exposes the data being transmitted to potential eavesdropping and tampering.
HTTPS, on the other hand, secures communication through the use of SSL/TLS encryption, providing confidentiality and integrity.
Understanding the trade-offs between HTTP and HTTPS is crucial to making informed data security decisions.

Building a Solid Foundation: Understanding the Three-Way Handshake for Reliable Connections

To evaluate the performance of our HTTP server, we need to differentiate between connection latency and server response time. Connection latency refers to the time it takes for the initial three-way handshake process to complete before data transmission can occur. On the other hand, server response time measures the duration from when the server receives a request to when it generates and sends the response back to the client.

The three-way handshake is a fundamental process in establishing a TCP (Transmission Control Protocol) connection between a client and a server in a network. It involves three steps, a “three-way handshake.” This handshake establishes a reliable and ordered communication channel between the two endpoints.

Here’s a breakdown of the three steps involved in the three-way handshake:

__SYN (Synchronize)__: The client initiates the connection by sending an SYN packet (synchronize) to the server. This packet contains a randomly generated sequence number to initiate the communication.
__SYN-ACK (Synchronize-Acknowledge)__: Upon receiving the SYN packet, the server acknowledges the request by sending an SYN-ACK packet back to the client. The SYN-ACK packet includes its own randomly generated sequence number and an acknowledgment number equal to the client’s sequence number plus one.
__ACK (Acknowledge)__: Finally, the client sends an ACK packet (acknowledge) to the server, confirming the receipt of the SYN-ACK packet. This packet also contains the acknowledgment number equal to the server’s sequence plus one.

Once this three-way handshake process is completed, the client and the server have agreed upon initial sequence numbers, and a reliable connection is established between them. This connection allows for data transmission with proper sequencing and error detection mechanisms, ensuring that the information sent between the client and server is reliable and accurate.

The three-way handshake is essential to establishing TCP connections and is performed before any data transmission can occur. It plays a critical role in ensuring the integrity and reliability of the communication channel, providing a solid foundation for subsequent data exchange between the client and server.

Create a self-serve diagnostic tool for a server-rendered page in Node.js.

The idea is to share an easy-to-follow recipe that will help you create your tool, so let’s start with the ingredients and end with the steps to create a self-serve diagnostic tool for a server-rendered page in Node.js.

Ingredients:

Node.js & NPM installation – https://nodejs.org/

Fastify.js – https://www.fastify.io/

Instructions:

1. Setup a Node.js Project
Use NPM to create your Node project:

$ mkdir diagnostic-tool-nodejs
$ cd diagnostic-tool-nodejs
$ npm init -y

2. Install your NPM packages.
We have Fastify in our recipe, so we must install them first:

$ npm i fastify

3. Create the index.mjs
Create an index.mjs file in the project’s root directory and paste this fastify HTTP server sample code.

import Fastify from “fastify”;

const fastify = Fastify({
logger: true,
});

// Randomly create a timer from 100ms up to X seconds
function timer(time) {
return new Promise((resolve, reject) => {
const ms = Math.floor(Math.random() * time) + 100;
setTimeout(() => {
resolve(ms);
}, ms);
});
};

// Declare the root route and delay the response randomly
fastify.get(“/”, async function (request, reply) {
const wait = await timer(5000);
return { delayTime: wait };
});

// Run the server!
fastify.listen({ port: 3000 }, function (err, address) {
if (err) {
fastify.log.error(err);
process.exit(1);
}
});

This will start the server on port 3000, which you can access by going to http://localhost:3000 in your web browser.

Integrate with N|Solid Console

Be sure you already have N|Solid installed and running on your environment; otherwise, go to https://downloads.nodesource.com and get the installer.

Also, run the console using docker as an alternative to the local installation.

docker run -d -p 6753:6753 -p 9001:9001 -p 9002:9002 -p 9003:9003 nodesource/nsolid-console:hydrogen-alpine-latest

With the application already initialized with npm, Fastify installed, and our index.js in place, we can connect our process with N|Solid

Run the HTTP server with the NSOLID RUNTIME following the instructions on the principal console page.

IMG – Connect N|Solid

In this case, we ran the process by passing the config via environment variables and running a local installation of the Nsolid console.

NSOLID_APPNAME=”NSOLID_RESPONSE_TIME_APP” NSOLID_COMMAND=”127.0.0.1:9001″ nsolid index.mjs

If you instead use our SaaS console, you need to use the NSOLID_SAAS env instead of __NSOLID_COMMAND__.

NSOLID_APPNAME=”NSOLID_RESPONSE_TIME_APP” NSOLID_COMMAND=”XYZ.prod.proxy.saas.nodesource.io:9001″ nsolid index.mjs

After completing those steps, you should be able to watch the app and process connected to the console.

IMG – Connect N|Solid Process

GIF 1 – Connect N|Solid Process

Go to the application process and add the HTTP(S) Server 99th Percentile Duration metric to see in near-real time the HTTP server latency response time and also we have the HTTP(S) Request Median Duration.

GIF 2 – Monitor Process Metrics

After this, we should be able to generate some traffic and see how the response times behave with the sample code provided, generating some response time randomness from 100ms up to 5 secs.

To generate the traffic, we can use autocannon

npx autocannon -d 120 -R 60 localhost:3000

After running autocannon for some minutes, we can see the P99 metric of the HTTP Server. The median and compare them.

IMG – http-latency-response-time-metrics

IMG – http-request-median-duration

IMG – p99-metric

To fully utilize the metrics provided by N|Solid, it is crucial to have a comprehensive understanding of their significance. Two critical metrics offered by N|Solid are the 99th Percentile and the HTTP Median metric. These metrics play a vital role in assessing the performance of Node.js applications in production environments. By getting deeper into their practical application and importance, we can unlock the actual value of these metrics in N|Solid and make informed decisions to optimize our production systems. Let’s explore this further.

The 99th Percentile metric

The 99th percentile is a statistical measure commonly used to analyze and understand response time or latency in a system.

Imagine you have a web application that handles incoming requests. To understand how fast the server responds, you measure the time it takes for each request and gather that data. You can find the 99th percentile response time by looking at the data.

For example, __the 99th percentile response time is 500 milliseconds__.
This means that only 1% of the requests took longer than 500 milliseconds to get a response. In simpler terms, 99% of the requests were handled in 500 milliseconds or less, which is fast.

It helps you identify and address any outliers or performance bottlenecks affecting a small fraction of requests but can significantly impact the user experience or system stability. Monitoring the 99th percentile response time helps you spot any slow requests or performance issues that might affect a few users but still need attention. but can have a significant impact on user experience or system stability.

The HTTP median metric

When sorted in ascending or descending order, the median represents a dataset’s middle value.

To illustrate the difference between the 99th percentile and the median, let’s consider an example. Suppose you have a dataset of response times for a web application consisting of 10 values:
[100ms, 150ms, 200ms, 250ms, __500ms__, 600ms, 700ms, 800ms, 900ms, 1000ms].

The median response time would be the middle value when the dataset is sorted, which is the 5th value, 500ms. This means that 50% of the requests had a response time faster than 500ms, and the other 50% had a response time slower than 500ms.

Connect with NodeSource

If you have any questions, please contact us at [email protected] or through this form.

Experience the Benefits of N|Solid’s Integrated Features
Sign up for a Free Trial Today

To get the best out of Node.js and experience the benefits of its integrated features, including OpenTelemetry support, SBOM integration, and machine learning capabilities. Sign up for a free trial and see how N|Solid can help you achieve your development and operations goals. #KnowyourNode

Unleashing the Power of NCM: Safeguarding Node.js Applications with Next-Generation Security in N|Solid

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In the world of Node.js, application development, speed, flexibility, and scalability are critical for modern software development. However, the risk of vulnerabilities and security breaches looms with the increasing reliance on open-source Node packages. NCM (NodeSource Certified Modules) is the next-generation security solution that empowers Node.js developers to safeguard their applications easily and confidently.

This article will explore how NCM, a key N|Solid platform feature, revolutionizes how Node.js applications are secured, offering advanced security features, enhanced visibility, and peace of mind. Get ready to unleash the power of NCM and take your Node.js applications to new heights of security and reliability with N|Solid.

_Image 1 – Security Vulnerabilities in N|Solid View
_

Don’t miss out on this opportunity to try N|Solid for free and unlock the full potential of your Node.js applications.✍️ Sign up now and take your monitoring to the next level!

What is N|Solid?

_Image 2 – N|Solid Product View
_

N|Solid provides enhanced security for Node.js applications in production environments. It is built on top of the Node.js runtime. It provides a secure environment for running Node.js applications and advanced features such as worker threads monitoring, memory leak detection, and CPU profiling. We have +15 features in our product, including OpenTelemetry support, SBOM integration, and Machine Learning capabilities. Discover More HERE ‘__Top 10 N|Solid —APM for Node— features you needed to use__’ – HERE: ???????? nsrc.io/TopNSolidFeatures.

N|Solid offers many benefits over the standard Node.js runtime, including improved security through features like runtime vulnerability scanning, access control, and enhanced monitoring capabilities that allow developers to identify and address issues in real-time.

N|Solid is well-suited for enterprise applications requiring high performance, scalability, and security levels. It is widely used in finance, healthcare, and e-commerce. It is developed and maintained by __NodeSource__, a company specializing in enterprise-grade Node.js solutions.

In the previous section, we discussed N|Solid as a solution that provides enhanced security for Node.js applications in production environments. Let’s discuss the difference between NSolid Console, N|Solid Runtime, and N|Solid SaaS. It’s important to differentiate between these components for several reasons, including functionality, user experience, and flexibility.

What is the difference between NSolid Console, N|Solid Runtime, and N|Solid SaaS?

Differentiating between the Console, Runtime, and SaaS setup in N|Solid is essential for a few reasons: functionality, user experience, and flexibility.

Users can deploy N|Solid in multiple ways, including using the N|Solid Console, N|Solid Runtime, or N|Solid SaaS setup, depending on their requirements and infrastructure setup. It is essential to provide distinct functionalities to enhance user experience and offer flexibility in deployment options, allowing scalability, customization, and integration with existing workflows. Here’s a brief description of each:

N|Solid Runtime is the runtime environment for Node.js applications. It includes a modified version of the Node.js runtime, enhanced with additional security, monitoring, and debugging features. These features include advanced profiling and tracing capabilities, heap and CPU profiling, and runtime vulnerability scanning.
???????? https://bit.ly/NSolidRuntime-npm

_Image 3 – N|Solid Runtime Installation
_

__N|Solid Console__, on the other hand, is a web-based dashboard that provides a graphical user interface for monitoring and managing Node.js applications running on N|Solid Runtime. It lets users view their applications’ real-time metrics and performance data, monitor resource utilization, and set alerts for specific events or thresholds. N|Solid Console also provides features for managing user access and permissions, configuring application settings, and integrating with third-party tools and services. It can manage multiple N|Solid Runtimes across a distributed environment, making it ideal for large-scale enterprise deployments.
???????? https://nsrc.io/NSolidConsole

_Image 4 – N|Solid Console Overview
_

__N|Solid SaaS__: N|Solid also offers a SaaS (Software-as-a-Service) setup so users can leverage N|Solid’s enhanced security and performance features without managing their own infrastructure. With N|Solid SaaS, users can simply sign up for a subscription and use N|Solid’s features through a cloud-based service without needing on-premises installation or maintenance. ???????? https://nsrc.io/NSolidSaaS

_Image 5 – N|Solid SaaS Overview
_

N|Solid offers multiple deployment options; these components provide distinct functionalities, user experiences, and deployment flexibilities, catering to the diverse needs of enterprise Node.js applications.

But, What about NCM?

NodeSource Certified Modules (NCM) is another product developed by NodeSource that provides you and your teams with actionable insights into the risk levels of using third-party packages. Using a series of tests, we score packages on npm to look for several weighted criteria. With NCM CLI, you can scan your projects for existing security vulnerabilities, license concerns, code risk, and code quality. This helps you understand the level of risk exposure and how to mitigate it. NodeSource Certified Modules (NCM) also work in offline mode. Explore Further ‘__Avoiding npm substitution attacks using NCM__’ HERE ????????https://nsrc.io/AvoidAttackswithNCM

_Image 6 – NCM CLI Report
_

NodeSource Certified Modules (NCM) is a security, compliance, and curation tool around the 3rd-Party Node.js & JavaScript package ecosystem. It is designed to be used with npm to provide protection against known security vulnerabilities and potential license compliance issues and provide general quality or risk assessment information to improve your ability to work with the 3rd-Party ecosystem.

Since the release of N|Solid 4.1.0, we have consolidated NCM into a single product with NCM’s features being pulled into N|Solid Runtime, N|Solid SaaS, and the N|Solid Console for optimal user experience. It also provides alerts and notifications when new vulnerabilities are discovered in modules used by an organization’s applications and helps users quickly identify and remediate any potential security risks.NCM is a valuable tool for organizations that rely on Node.js and open-source modules, helping to ensure that their applications are secure, reliable, and compliant with industry standards and regulations.

NCM now assesses packages based on multiple attributes: security, compliance, risk, and quality. These attributes are combined to generate an overall risk level for each package, providing valuable insights to manage third-party code in your Node.js applications effectively. With NCM’s scoring system, you can:

__Manage acceptable risk levels__: NCM helps you assess the risk associated with third-party packages by providing an overall risk level for each package. This allows you to make informed decisions about the level of risk you are willing to accept in your application.
__Understand security vulnerabilities__: NCM identifies and highlights security vulnerabilities in third-party modules, allowing you to understand the severity of the vulnerabilities and take appropriate actions to address them in your code.
__Manage license and compliance risks__: NCM helps you identify potential license and compliance risks introduced by third-party modules, ensuring that your application adheres to licensing requirements and compliance standards.
__Identify potential risk vectors__: NCM goes beyond known security vulnerabilities and identifies potential risks that may not have surfaced in security vulnerabilities yet. This helps you proactively identify and address potential risks in your code.
__Improve code quality__: NCM provides insights into quality attributes that align with best practices, helping you improve the quality of your code and make it more manageable and secure.

Together, these attributes in NCM’s scoring system (security, compliance, risk, and quality.) provide a comprehensive assessment of third-party packages, enabling you to effectively manage and secure your Node.js applications by addressing security vulnerabilities, managing compliance risks, assessing package risk, and provides insights to improve code quality. Find Out More about ‘Vulnerability Scanning & 3rd-Party Modules Certification’- HERE ???????? nsrc.io/VulnerabilityScanningNS

The Importance of Node.js Application Security

Selecting the right tools and applications for your developer pipeline requires careful consideration of your team’s workflow and project needs. This might involve assessing your tech stack, deployment processes, and the number of steps in your pipeline and identifying areas where guardrails can be implemented to improve security and reliability.

_Image 7 – NCM Criteria
_

Fortunately, numerous tools and applications are available to assist in managing your pipeline and ensuring the security and compliance of your applications. One powerful tool in this regard is NCM (NodeSource Certified Modules). NCM is a comprehensive security, compliance, and curation tool that offers advanced capabilities for managing dependencies in Node.js applications. By integrating NCM into your pipeline, you can effortlessly scan for vulnerabilities, track package dependencies, and ensure compliance with licensing requirements.

NCM enables you to elevate your pipeline to the next level, enhancing your application’s performance, reliability, and security while safeguarding against __SUPPLY CHAIN ATTACKS__. With the consolidation of NCM into N|Solid, you can now seamlessly access these powerful capabilities through the N|Solid Console for a streamlined user experience.

Note: Supply chain attacks are a type of cyber attack that targets the weakest link in a software supply chain. Instead of directly attacking a target, hackers infiltrate a trusted third-party vendor, supplier, or service provider to gain access to their customer’s systems and data. This allows the attackers to distribute malicious code or compromise software updates, which can then infect the entire supply chain and cause widespread damage. Supply chain attacks can be difficult to detect and prevent, making them a growing threat to organizations of all sizes and industries.

The importance of NCM

The consolidation of NCM 2 into N|Solid represents a significant milestone in providing a comprehensive solution for ensuring the security, reliability, and performance of Node.js applications. With features such as:

Projects & Applications Monitoring – https://nsrc.io/ProjectApplicationsMonitoringNS

Process Monitoring – https://nsrc.io/ProcessMonitoringNS

CPU Profiling – https://nsrc.io/CPUProfilingNS

Worker Threads Monitoring – https://nsrc.io/WorkerThreadsNS

Capture Heap Snapshots – https://nsrc.io/HeapSnapshotsNS

Memory Anomaly Detection – https://nsrc.io/MemoryAnomalyNS

Vulnerability Scanning & 3rd party Modules Certification – https://nsrc.io/VulnerabilityScanningNS
HTTP Tracing Support – https://nsrc.io/HTTPTracingNS

Global Alerts & Integrations – https://nsrc.io/GlobalAlertsIntegrationsNS

Distributed Tracing – https://nsrc.io/DistributedTracingNS

Open Telemetry Support – nsrc.io/AIOpsNSolid

SBOM Support – nsrc.io/SBOM-NSolid

Machine Learning Support – nsrc.io/ML-NSolid

N|Solid offers a robust and all-encompassing solution for managing the entire lifecycle of Node.js applications. By incorporating NCM’s powerful capabilities for security, compliance, and curation, N|Solid empowers developers and organizations to proactively identify and address vulnerabilities, track dependencies, and ensure licensing compliance, ultimately elevating the overall performance, reliability, and security of their applications. With N|Solid, organizations can confidently build and deploy Node.js applications with peace of mind, knowing their software is protected against potential risks and supply chain attacks.

Conclusion:

Securing Node.js applications is paramount in today’s software development landscape. With the powerful features of NSolid, including the N|Solid Console and N|Solid Runtime, combined with the cutting-edge security capabilities of NCM, developers can safeguard their Node.js applications with next-generation security measures or simply leaving the maintenance and infrastructure to us by selecting our N|Solid SaaS option. By leveraging the power of NCM in the N|Solid platform, developers can proactively mitigate vulnerabilities and ensure the reliability and stability of their Node.js applications. Embrace the power of NCM in N|Solid today and unleash the full potential of your Node.js applications with advanced security measures.

NodeSource’s Products:

N|Solid Runtime is the Node.js runtime environment with enhanced security, monitoring, and debugging features.

N|Solid Console is a web-based dashboard for managing and monitoring Node.js applications running on N|Solid Runtime.
__N|Solid SaaS__: Benefit from N|Solid’s advanced security and performance features through a cloud-based subscription service, eliminating the need for on-premises installation or maintenance.

NCM is a cutting-edge security feature integrated into the N|Solid platform that provides continuous monitoring, vulnerability scanning, and risk assessment of open-source Node.js packages used in Node.js applications.

To get the best out of Node.js and experience the benefits of its integrated features, including OpenTelemetry support, SBOM integration, and Machine Learning capabilities. ✍️ Sign up for a free trial and see how N|Solid can help you achieve your development and operations goals. #KnowyourNode

Nodesource introduces Machine learning on its N|Solid platform to help make better Node Apps

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N|Solid is an incredibly versatile platform for helping developers and devops engineers build and manage highly performant and secure Node.js web applications. With the advancement of machine learning you can unlock even more potential. Our M/L solution is a powerful tool that can increase the quality of user experience and boost efficiency for organizations with their Node.js applications. In this article, we’ll explore what machine learning is and how you can use it within N|Solid, pluswe’ll provide tips and best practices for leveraging this new capability to get the most out of your Node.js project.

AI – growing in value in the software development lifecycle

Img #1 AI vs ML concepts

Put in context, artificial intelligence refers to the general ability of computers to emulate human thought and perform tasks in real-world environments, while machine learning refers to the technologies and algorithms that enable systems to identify patterns, make decisions, and improve themselves through experience. — https://ai.engineering.columbia.edu

The technology world has been abuzz with the growing hype of artificial intelligence (AI). This is understandable as AI promises to revolutionize business and everyday life; from self-driving cars to automated customer service, AI will shape the future of our civilization. As technology continues to advance, the potential applications for AI are seemingly endless.

AI and ML (Machine Learning) are closely related, but not identical. AI is the broader concept of machines being able to perform tasks that would normally require human intelligence, such as visual perception, speech recognition, decision-making, and language understanding. ML is a specific subset of AI that is focused on the development of algorithms and statistical models that allow computers to “learn” from data, without being explicitly programmed. In other words, ML is a method for achieving AI.

ML and AI can help developers build better software in several ways. Some examples include:

Automating repetitive tasks: ML algorithms can be used to automate repetitive tasks that would otherwise require human intervention. For example, a ML model could be trained to automatically classify and categorize emails, reducing the need for manual sorting.

Improving software performance: ML algorithms can be used to optimize the performance of software systems. For example, a ML model could be trained to predict the load on a server, allowing the software to dynamically adjust its resource usage in response.

Enhancing the user experience: AI-powered software can provide a more personalized and intuitive experience for users. For example, a chatbot powered by natural language processing (NLP) could be used to provide customer service, or a recommendation system powered by ML could be used to suggest products to customers.

Predictive Maintenance: AI and ML algorithms can be used to predict when a machine or equipment is likely to fail, allowing maintenance to be performed before the failure occurs.

Identify and Fix Bugs: AI and ML can be used to automatically identify and fix software bugs, reducing the need for human intervention.

Improve Cybersecurity: AI and ML can be used to identify and mitigate cyber threats and detect suspicious activity on a network, which help to improve cybersecurity.

We believe there is great promise for developers to leverage new tooling that helps them focus on the solution and resolve issues as fast as possible, reducing security risks and deliver amazing user experiences. We see AI and ML as a major step forward to build better software.

Node.js expose the potential of AI.

Img 2 – AI Frameworks

We believe Node.js is a powerful technology for leveraging the potential of AI. It allows developers to easily create and manage AI applications, as it features extensive APIs for interacting with AI-related services. With Node.js, developers can create AI-backed applications that can be deployed across various platforms, making it an invaluable asset for businesses looking to leverage the power of AI.

The combination of Node.js and AI will also make it possible to create sophisticated applications that can interpret data in real-time, allowing businesses to improve their customer experience dramatically. As AI advances, Node.js will be a key tool in helping developers make the most out of the technology.

Recently there are several AI projects that are ushering a massive wave of exploration. OpenAI and its ChatGPT has become one of the fastest tools ever adopted. We are impressed with the incredible progress of the OpenAI project and many others,we continue to study, experiment, and review implementations of these technologies and their potential for the ecosystem.

Links to other cool resources

GitHub OpenAI: https://github.com/openai/openai-quickstart-node

OpenAI Docs: https://beta.openai.com/docs/quickstart

Already, Node.js is being used by many companies to power their AI-driven applications, and this trend will only continue as more companies seek to take advantage of the power of AI. Node.js also allows developers to quickly set up and deploy AI-driven applications, further accelerating the development process. With Node.js and AI, businesses can create smarter, faster, and more efficient applications.

Nodesource Introduces Machine learning in N|Solid platform

N|Solid is a Node.js platform with an integrated AI development environment.

This feature allows for training models that will later detect similar patterns in your application data and fire custom events.

It also offers advanced analytics capabilities and support for various AI technologies, making it a powerful tool for businesses looking to capitalize on the potential of AI.

Img 3 – ML Feature Cover

N|Solid is part of a larger trend toward making AI and ML more accessible to developers, helping to utilize these advancements to deliver software solutions.. By providing an integrated platform for Node.js in production, N|Solid is making it easier for businesses to create sophisticated AI-driven models and reap the benefits that come with them.

Developers can start using this new feature in N|Solid immediately to:

Identify performance issues and present insights to resolve quickly
Apply insights across multiple applications
Smart analysis and detection of common Node.js performance issues with the bundled models we provide
Training of custom models to detect specific problems
Global notifications and events tracking for processes and applications

Below you will see ML in action inside N|Solid.

Machine Learning UI

In the N|Solid Console, the Machine Learning feature can be accessed from the app summary or process detail views.

Each handles different data sets and will have a different effect on the model you train.

Training ML Models

The Machine Learning models can be trained using two kinds of data sets. The models trained in the app summary view will use the aggregated data of all the processes running inside the app.

On the other hand, the models trained in the process detail view will use process-specific data.

Train a model in the app summary view.

When a process/app is first connected, it will take a certain amount of data to be successfully trained; you will find a progress loader under process configuration:

To train a model in an app summary page, click on Train ML Model button.

Train a model in a process detail view.

To train a model in a process detail page, click on Train ML Model button.

Modal creation and training

After clicking on the Train ML Model button, a modal will open; here, you can create, filter, and train models; this modal is the same for both pages.

To create a model, click on CREATE NEW MODEL.

Name and briefly describe the model, then save.

Select the created modal and click on ‘TRAIN.’

When the trained model finds a data pattern similar to the one it was trained with, it will fire an event and show a banner on top of the navbar.

Click on View Event to be redirected to the events tab; here, you will find the most recent machine learning event.

The events will also appear in the application status section; clicking on VIEW ANOMALIES will redirect to the events tab.

Manage the default and custom models.

Machine Learning models can be administered in the settings tab, where you will find a set of default models and the user-trained models; here, the frequency of events being fired can be modified, and the custom user models can be deactivated, deleted, or edited.

For a full reset of the created models, click on RESET MODELS.

Custom user models have edit and delete icons; these models are found beneath the default models.

PLEASE NOTE Only the name and description of the user-created model can be edited; if you want to change the model data, please retrain the model in-app summary or in the process detail pages. Default models are activated by default; these can only be activated or deactivated.

Our Machine learning feature has been live since November 2022; if you want to review the official documentation, you can do it here.

One Last Thing…

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