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The list of AI models to choose from when developing an AI app is long—from Linear Regression to Decision Trees, Naive Bayes, K-means, and Random Forest—it goes like a laundry list. As the choice will affect your enterprise AI solutions and their performance, it is a pretty serious decision.
Entrepreneurs or CEOs often brainstorm ‘how do I choose the right AI model’ more often than anything else. Even then, the choices may not be the most informed.
This may make entrepreneurs and executive managers like you take even more time. That’s why we have decided to help you with this blog, where we talk about:
The blog will help you accelerate your decision-making and choose the most suitable AI model as per your business and app development goals.
AI models are programs that can recognize patterns, predict outcomes, and make decisions without human intervention.
What makes an AI model special is its ability to receive data, draw conclusions, and take actions based on those insights. If you think about it, it is like how our brain works. AI models use various algorithms, such as machine learning, deep learning, and rule-based learning.
When an input is given, the ML algorithm uses a specific data processing mechanism to enable the AI to analyze data and learn from it.
As a business looking to develop enterprise AI solutions and AI-powered chatbots, you can choose any AI model you prefer. However, you need to understand the top ML algorithms to make the decision.
Let’s look at some of the most popular types of AI models in 2024.
Linear regression is a simple yet machine-learning method. It assumes a linear link between input and output variables and predicts the latter as a weighted sum of the former plus a bias.
It is primarily used for continuous output prediction, such as house pricing based on attributes like size, location, and age. The weights assigned to features offer clear insights into their influence, enhancing interpretability.
However, linear regression relies on data assumptions. This means that if violated, the accuracy of the output you get may suffer. Yet, its simplicity and speed make it a favored starting point for prediction tasks. Hence, if your app leans in that direction, it is an effective model to consider.
Deep Neural Networks (DNNs) are AI models with multiple hidden layers sandwiched between input and output layers.
DNNs are framed after the human brain’s neural network and consist of interconnected artificial neurons. To grasp their potential, you need to explore their operations.
One of the advantages of DNNs is that they excel at detecting patterns and relationships in data. These capabilities make them suitable for apps in speech recognition, image recognition, and Natural Language Processing (NLP).
This is a statistical model which tackles binary classification tasks. Compared to linear regression’s continuous predictions, logistic regression measures an event’s probability.
Although the model struggles with complex relationships due to linearity, you can choose it for its efficiency and ease of implementation. Logistic regression serves in disease prediction, credit scoring, and customer retention forecasts. This is also why the model is often employed in healthcare, finance, and marketing.
The model is often deployed in machine learning as it offers insights with lower computational costs.
This is a supervised learning algorithm that performs well in tasks that involve classification and regression.
Decision trees iteratively partition data and it leads to building a decision tree that has decision nodes and leaf nodes. This structure offers a straightforward if/then logic, which is easy to understand and apply.
This simple logic makes it one of the most widely used algorithms, even during the formative years of predictive analytics.
Linear Discriminant Analysis (LDA) is another AI model adept at pattern recognition and prediction. And it is particularly capable of distinguishing between multiple groups. When data is input into LDA, it looks to uncover patterns or rules.
For instance, in medical diagnosis, LDA analyzes symptoms of diseases to predict the presence of disease. This is done by establishing a rule that LDA uses to look at data, analyze it, and make predictions.
Apart from this, LDA also simplifies complex datasets by condensing data for easier understanding without sacrificing relevance.
Learning Vector Quantization is a supervised machine learning algorithm categorized under artificial neural networks. It classifies data by comparing them to prototypes.
Initially, LVQ creates prototypes from training data and then assesses each data point’s similarity to prototypes. LVQ’s unique capability is in its iterative learning process, adjusting prototypes based on data and making it suitable for non-linear or complex decision boundary datasets.
You can use LVQ in image recognition, text classification, and bioinformatics in scenarios that are high-dimensional with limited data.
AI models have become essential for businesses as data has become a crucial commodity. However, companies face a lot of challenges due to the constant deluge of data. At the same time, it also offers new opportunities for businesses.
What you need to streamline your operations and accelerate tasks for efficiency is an Enterprise AI Solutions. You also need to choose the right AI model for the solution.
Here is why Machine Learning algorithms are crucial for AI enterprise solutions.
When it comes to AI, you need a lot of data to get the desired outputs. AI models do a great job by harnessing training data. Data collection is crucial in the competitive business arena for intelligent decision-making. AI models help businesses to tap into uncharted data territories and refine their models continuously. This leads to better accuracy and relevance of outputs.
AI models, like Generative Adversarial Networks, are excellent at creating new data that resemble the data they are trained with. This helps them create images and photorealistic videos akin to their training data.
Models like DALL-E, Midjourney, etc., use this to deliver creative outputs. These capabilities can accelerate innovations and creative endeavors in diverse industries.
AI models are excellent in interpretation even if the data is huge. They are capable of going through vast, convoluted data landscapes and extracting meaningful insights. What makes this impressive is that humans can never do this.
You can use this for your business advantage by using historical data to predict trends, customer behavior, etc. This results in more efficient decision-making and fewer chances of error.
AI has become synonymous with automating tasks. If you can efficiently integrate AI to your workflows, you can have the AI do mundane tasks. Along with accelerating the task, AI also ensures efficiency, consistency, and scalability.
By doing this, you can also free human resources to tackle more intricate and strategic issues in your business.
The AI model you choose is the heart and brain of your AI application. Picking the wrong or less suitable AI model can render the app less efficient. The performance of the app can also suffer.
This is why you need to choose an efficient and suitable AI model apt for the purpose for which your AI app is developed.
Here is a guide to help you choose the right AI model for your enterprise AI application.
This is the step towards making the right choice. It is key because it helps you figure out what kind of algorithm suits your needs.
If your data is labelled and you’re sorting it into categories, that’s supervised learning. If you’re working with unlabeled data to uncover patterns, that’s unsupervised learning. Then there’s reinforcement learning, where the goal is to optimize through interactions with the environment. Depending on whether your model predicts numbers or assigns data to classes, you are dealing with regression or classification problems, respectively.
What we are looking at here is the output you want, and your AI model must suit the same. Once you have figured out the problem you are looking to solve, pick an AI model that’s capable of doing it efficiently.
You want to build an AI that’s going to perform well. That will help you solve the issues efficiently and on time. Therefore, look at the performance of the AI models you are assessing. In addition to speed, you also need to look at the accuracy, recall, and precision of the output.
Another element to consider when doing the performance analysis is explainability. You need to understand why the AI model gave a particular output. There are AI models that don’t do a great job at this.
As a business, you need a certain degree of explainability from the AI app.
More complex models can solve intricate patterns. However, they are harder to maintain and understand. And the cost is also a challenge. More complex models can mean higher costs throughout the model’s lifecycle.
The best way to solve this issue is to look at your overall cost and requirements. Are your business operations going to benefit significantly in terms of quality and cost? If the answer is yes, then you may go with a complex AI model.
If your goal is to not use complex data sets and understand the outputs well, then a less complex AI model is the way to go.
Neural networks handle large volumes of data well, while a KNN model might do better with fewer data sets.
Hence, think about the type of data you need for training. Supervised learning models need labelled data, which can be pricey and time-consuming to get. Unsupervised learning models can work with unlabeled data, but they might not give you meaningful results if the data is noisy or irrelevant. On the other hand, reinforcement learning models need lots of interactions with the environment. This may become tough to simulate.
Hence, you need to look at the problem and the data volume needed to solve it. Doing this will help you pick a capable AI model.
You might think that your AI model needs to have all the features you can get. However, more features mean complexity of operations and more computing costs. The idea here is to get the features you need. Of course, more features might make your AI app better. However, consider if that’s something that you want.
Sacrificing performance and simplicity for features may not always work in your favor. Hence, find a balance between features and complexity.
Get those features that are necessary to help carry out the primary functions of the AI enterprise solution.
All AI applications require training, and it costs money and time. Therefore, look for a model that can deliver accurate results without incurring a lot of training expenses. Some AI models can deliver excellent accuracy, but their training costs are way too high. This may not work for some businesses, and it may not work for some.
The best way to make the decision is to look at what you want. If your primary function relies on accuracy, then training is a must-have. And the cost must not be the deciding factor.
The time taken by an AI model to analyze data and deliver an output is called inference speed. While picking the model, ask yourself this question—how quickly does the model process data and make predictions.
In scenarios where speed is crucial—like a self-driving car system—you need a fast model. In other scenarios where speed is not crucial, you can go with a model that’s not superfast.
Again, it all depends on what you need as a business and what you are trying to achieve. So, keep that in mind when making your choice.
AI models for apps are evolving rapidly. There’s no doubt about it. Staying updated on the latest trends shaping the future of AI helps you use it for your business growth.
Here are the top trends in AI models in 2024 to keep in mind.
Explainable AI is gaining traction as AI models grow more complex. It aims to provide transparency, allowing you to understand how decisions are made. This fosters trust and helps businesses meet regulatory requirements.
Reinforcement learning is another exciting trend. Here, the AI agents learn by interacting with their environment. This approach enables systems to tackle complex tasks through trial and error, continuously improving over time. Thanks to its potential, it is already used in robotics, gaming, and AI-powered chatbots by businesses.
Generative AI has been making waves in the world of business for a while now. It can empower enterprise AI solutions to create content resembling human creations—be it images, music, or text. This opens new opportunities in creative industries, offering artists and designers innovative tools to explore and innovate.
AI development is going nowhere. It is going to evolve and become a crucial part of every business. While picking AI models to create new generative AI solutions for your business is crucial, there’s one more thing that needs your attention, especially if you want to build the most efficient enterprise AI solutions—choosing the right consultant to build AI solutions. Don’t worry, with decades of combined experience in AI and app development, Fullestop can help you.
Our AI services are tailored to meet your business goals while also addressing the trends, technology transformation, and your target audience. We also use the latest advances and technologies in the AI industry to make your AI apps efficient and ready for the future.
Speak to our client support team to learn more about Generative AI solutions and start building your enterprise AI solution now.
