Mayank Patel
Jul 20, 2022
5 min read
Last updated Apr 18, 2024
Machine Learning and IoT (Internet of Things) are the most demanding and trending technologies of the era and today we will discuss them. Also, knowing the correct concept of technology boosts the performance of the business.
As far as Machine Learning and IoT are concerned, they generate business insights, automated responses along with decision-making capabilities. And we all know that these elements play an important role in the business.
In this series, Linearloop helps you to understand both the concepts individually as we are a well know software development company in USA. Our team consists of experts having dynamic experience in both technologies.
Hence if you are looking to explore more about IoT and ML (Machine Learning), the blog will play an important role. We have shared the concepts using our practical experiences.
Also by being in the industry for so many years, we are very well aware of the challenges as well.
So let us get started with the two leading technologies of the present scenario. And if any query comes to your mind, feel free to connect with us. Our team is always there to help you.
The concept of Machine Learning came in the year 1959 and Arthur Samuel is behind the foundation of this great technology. Data Science is the field where Machine Learning is broadly used because of its statistical approach.
Machine Learning is the most needed concept of the time. Its market size is 15.66 billion USD.
We know, that machine learning is sub-branch of AI (Artificial Intelligence) which enables systems to be more accurate in generating responses. Do you know how ML works?
The algorithm of Machine Learning uses historical data in order to generate the output. The patterns are thoroughly analyzed so that the prediction could be precise enough.
The concept of machine learning is majorly used to identify criminals, fraud detection, detection of malware threats, etc.
As technology is growing, positive and negative aspects both are getting enriched. On one side things are made easier for people and on the other side, some negative minds try to disturb that easiness with the help of technology.
For example, We all use online money transactions and we know how comfortable it is. Because we do not want to be in a bank queue and waste our time.
But nowadays lots of frauds are reported regarding online money transactions. Hackers take the control of your banking system and transfer entire funds to their own account.
Apart from that, lots of cyber crime takes place. So here comes the role of Machine Learning. It identifies the unusual patterns in the system and generates an alert. Using the response, companies can secure their data and avoid the hack.
Furthermore, with the help of Machine Learning, businesses can anticipate the customers’ behaviour along with business operational patterns. Along with this, they get a better decision-making approach in order to make their business result-driven and customer-oriented.
So the advantages of Machine Learning are:
Moreover, machine learning experts are in great demand across the industry. And if you are looking to make your career in ML, start focusing on its fundamentals. Also, the salary of Machine Learning experts is quite handsome.
Now, let us move toward the concept of IoT (Internet of Things).
IoT, as the name describes, the concept revolves around the network of physical objects. These objects can be anything including sensors, mobile devices, software, etc. With the help of the concept of IoT, the data is connected and exchanged between objects.
The market size of IoT is 157.9 billion USD and North America is the fastest-growing market for the technology.
Almost on daily basis, we interact with IoT. We all passionately use SMART WATCHES. The smart watches are designed in a way, we can connect it with our phones to get all important notifications. All our calls, music, messages, chats etc are visible on the screen of our smart watch.
Apart from that, IoT plays an important role in Home Securities. Yes, we are talking about SMART CAMERAS that became the necessity of the time.
Like Smart Watches, you can also connect these cameras with your cell phone. All the activities happening in your home or surrounding will be visible on the screen of your phone.
IoT has shown its importance in the field of Motion Detection, Electronics Gadgets Management, Kitchen Appliances and many more.
Seeing these examples, we can say, our life became more secure, smooth and fast with the foundation of IoT. Also, if we say IoT has acquired of our life, we will not be wrong.
In the present time, communication is controlled through IoT. At personal and professional level, everywhere IoT has played an important role.
Whether it is project discussion, project feedback, execution, or payment processing, everything is being taken care of with perfection. The process is saving time and cost for both parties.
So the implementation of IoT for businesses is a must. If you are ignoring such profit-driven technology, it is a big mistake.
The concept of the Internet of Things is the major attention of the 21st century because it has given a new vision to the technology. Even in the kitchen, the appliances are automatically connected and household activities become super easy.
Also, the working ladies are monitoring their kids using the concept of IoT, otherwise who had imagined live video monitoring through CCTV. Likewise, there are millions are examples available that explain the importance of IoT.
As our topic is more inclined towards businesses, IoT has played a major role there as well. IT companies in India are executing large-scale projects for the USA, UK, and other developed nations. Everything is virtually being done.
So the advantages of IoT are as:
As we have discussed the concept of Machine Learning as well as the Internet of Things (IoT), you must have an idea about the concept of both technologies. Implementing them in business operations will definitely boost the sale and performance of your business.
At Linearloop we have experts in ML and IoT. We are offering the services of IoT and Machine Learning in India and the USA. If you are looking for a service provider for the same, we are your constant service partner. Feel free to connect with us.
Who are AI Agencies?
Feeling lost in a world of AI? You are not alone. Many businesses understand the importance of AI but find it really difficult to understand how to use it. From machine learning to natural language processing, AI is pretty complex. That's where the AI agencies come in.
Work with AI agencies now because AI drives change at an exponential pace. Experts give you tools you might lack in-house. This helps you work smarter, solve problems faster, and understand your customers better. AI agencies help you board these changes far quicker than your competition. You don’t have to do it alone. But let’s start with the basics first.
AI agencies build solutions that can "learn" and make decisions based on data, often automating tasks and improving over time. Traditional agencies typically focus on rule based or static solutions with limited ability to adapt, learn, or improve over time. Here's how a typical project might differ when built by a traditional software agency versus an AI agency:
Traditional Software Agency:
A traditional agency would likely build a chatbot using pre-configured templates. The bot would follow a script with predefined responses for specific keywords or actions.
AI Agency:
An AI agency would develop an intelligent, machine-learning-powered chatbot capable of understanding and responding to customer queries dynamically. The chatbot could read and learn user interactions and improve its responses over time based on data.
Key Features:
Traditional Software Agency:
A traditional agency might build a simple recommendation engine based on predefined rules like "customers who bought X also bought Y." This system uses static rules for recommending products, which can be limited and often doesn't evolve or adapt to new data.
AI Agency:
An AI agency would use machine learning algorithms like collaborative filtering or deep learning to create a dynamic recommendation system that personalizes product suggestions based on each customer's behavior, preferences, and past interactions.
Key Features:
Traditional Software Agency:
A traditional agency might build an image recognition system using basic image processing techniques like edge detection or template matching to identify defects in products.
AI Agency:
An AI agency would build a computer vision system using deep learning algorithms like convolutional neural networks (CNNs) to identify product defects in images. The system could be trained to recognize defects from various angles, lighting conditions, and types of products.
Key Features:
Here’s how these new AI agencies can serve you:
These new agencies build new learning models just for your organization. That means using your private data to make sure the model is built on accurate and relevant parameters.
At the centre of every successful AI initiative is data. AI agencies have the expertise and experience in conducting extensive data analysis. They help you make the most of your data.
Also Read: Overcoming AI Implementation Hurdles: Why Simple API Calls Fall Short in Conversational AI
AI agencies can build specific action-driven AI agents. These agents can improve efficiency and responsiveness whilst helping to alleviate human staff burdens by acting autonomously.
Integrating AI into business has its own set of unique challenges. Herein are some of the top reasons why one needs to partner with an AI consulting agency ASAP:
Many organizations lack the in-house expertise to execute a successful AI strategy. AI agencies fill this gap by offering skilled professionals who understand the nuances of AI and can guide businesses through the implementation process
Every business faces unique challenges, and there’s no one-size-fits-all solution. An AI agency collaborates closely with clients to create strategies tailored to their specific needs and goals.
Also Read: AI Software Development: Key opportunities + challenges
Deploying AI is time-consuming and resource-intensive. Partnering with an AI agency helps you speed up implementation and capitalize on emerging opportunities much faster.
AI agencies can be confusing—what do they do, how do they work, and how to choose the right one. At their core, they are specialized teams that help you unlock value with AI, from strategy to implementation—but it’s not always as simple as it sounds.
This is where Linearloop.io comes in: we simplify AI for you, and help you understand their role in your business pipeline. We develop the right-fit AI strategy and models that integrate seamlessly with your existing tech stack.
Mayank Patel
Jan 16, 20256 min read
How to build AI agents with Ruby
Ruby offers an excellent launch pad to develop AI agents. Known for its simplicity and developer-friendly syntax, Ruby enables you to create highly sophisticated AI agents without the burden of overly complex code.
AI agents come in various forms, each with different levels of sophistication and decision-making capabilities. Some common types include:
AI agents are finding diverse applications across various industries:
While often associated with web development, Ruby offers compelling advantages for AI agent development, especially for beginners:
NOTE: In Ruby, gems are like small add-ons or tools that you can plug into your code.
Before you start building your AI agents, you'll need to set up your development environment. Here are the essential tools and libraries:
Setting up your development environment means installing Ruby, then using RubyGems to install the needed libraries. A very simple way to check if everything is installed properly is to open your terminal or command prompt and type ruby -v to check the version of Ruby installed.
Building an AI agent, regardless of the language, generally follows a structured process:
The first important step is to define the problem domain in which your AI agent will work. What precisely will it do? For example, will it be an agent to recommend interesting articles to the users or just a simple agent to automate some repetitive task? Having defined the problem, you need to define the objectives of the agent. What precisely are the goals that the agent should accomplish in that domain? Goals should be measurable and clearly define what a success for the agent is.
An important decision you have to make here is whether you take a rule-based approach or a learning-based approach. Rule-based agents follow a set of predefined rules to make a decision. They excel in problems for which the decision logic is well-defined and very clear. On the other hand, learning-based agents learn from data in making decisions. This approach is better for more complex problems in which the rules are not very clear. You will also want to pay attention to the structure of the inputs of the agent, how it gathers information, the outputs, the actions it takes, and the decision processes that connect them.
This is where you would begin to implement the core logic for your AI agent in Ruby. It's far easier to do so since Ruby syntax is clear. You will call upon the installed libraries to handle either the data processing or the implementation of machine learning algorithms—provided you have chosen a learning-based approach—or define the rules for your rule-based agent. Numo::NArray can be used in manipulating numerical data, while TensorFlow.rb may be used to build and train a neural network.
This means feeding your learning-based agent a lot of training data. The data will help the agent learn patterns and fine-tune its algorithms. After training—be it whatever type of agent—testing becomes an important step. You need to test how the agent performs under various scenarios, ensuring it behaves in a manner expected of it; that its goals as set are achieved.
Building AI agents with Ruby, like any development endeavor, comes with its own set of challenges:
Building AI agents can be intimidating at first, but Ruby makes it a pretty approachable goal with its friendly syntax and burgeoning ecosystem of powerful libraries. The future of intelligent solutions is in your hands, and you will be able to embark confidently into this new exciting era equipped with the right approach and an ideal partner in your endeavor.
Whether it is tapping into the potential of AI agents for business process simplification or developing next-generation applications, Linearloop is there to help you move forward. Discover how our software development expertise and emerging technologies can accelerate your AI initiatives and guide you through the exciting landscape of intelligent automation with confidence.
Mayank Patel
Jan 8, 20255 min read
