In a nutshell:
- The debate over AI replacing humans in the workforce misses the point; the focus should be on how humans who embrace AI can outperform those who do not.
- AI is a companion, not a substitute, and offers new opportunities for data analysts and business professionals.
- An AI model is a computer program that analyzes data to make predictions and can be used in various industries.
- Building an AI model requires high-quality data and can be done through different methods, including no-code/low-code platforms, AutoML, and traditional programming.
- The choice of method depends on coding expertise, customization needs, and time availability.
The debate over whether artificial intelligence (AI) will replace humans in the workforce often misses the point.
Harvard Business School professor Karim Lakhani suggests a more empowering question: How will humans who embrace AI outperform those who do not?
“Instead of pushing against it … you need to embrace it,” Lakhani said. “The first step is to begin, start experimentation, create the sandboxes.”
At Pecan, our exploration of AI’s influence on data analytics has led us to a similar conclusion: AI hardly signifies the end for data analysts.
Instead, it opens doors to new opportunities. And those who embrace them will leap light-years ahead of their peers.
AI is a companion, not a substitute. A way to enhance what’s possible, not a replacement for decision-makers and answer-seekers.
Anyone can build their own AI model with the right tools. And it’s time for data analysts to experiment — whether they’re just curious about AI or they’re looking for an advantage in their career.
Let’s explore a few different ways to build an AI model — from easy to hard — but first, what is an AI model, anyway?
What is an AI model?
An AI model is a computer program that analyzes data to make predictions. For the model to work effectively, it must be fed data and trained to recognize patterns and relationships. These unique models can predict outcomes in almost any situation across any industry.
For instance, fintech companies like banks use AI models to catch fraudulent activities. Medical experts use them to identify cancer or anticipate medical outcomes, and marketers use predictive AI models to predict how customers will respond to marketing activities and move through the sales funnel.
But how do you ensure these predictions are accurate and relevant? It starts with having high-quality data. Models are trained on historical data to identify patterns that frequently relate to specific outcomes.
For example, models might see patterns in customer activities that often occur before a fraudulent transaction. When those patterns occur again, the model will likely predict that fraud is about to occur.
That makes it possible for the model to “see into the future” by recognizing that similar patterns of activity are happening that could signal a similar outcome.
Advanced techniques like deep learning and neural networks make it possible to interpret additional types of data to enhance AI models’ abilities.
In some cases, these methods provide even greater ability to interpret data and deliver precise results. Predictive AI models can even provide insights into the future.
What is needed to build your own AI model?
The first step in crafting your own AI model is pinpointing the specific problem and understanding how AI can tackle it.
By focusing on what's bothering the user and figuring out the model's value proposition, you can shape an AI model that truly helps its users. Do you want to analyze customer behavior?
Automated marketing campaigns? Improve customer service? Whatever the business objectives, be clear on how your model will support them. Once you've got the initial version or the minimum viable product (MVP), you must check for any issues and fix them pronto.
This ensures that your AI solution effectively tackles the identified problem and delivers value. Getting the hang of the problem's details and how AI can work its magic helps in picking the right AI technology – be it machine learning algorithms, natural language processing, speech recognition, or computer vision.
To build any of these models, you must ensure you have enough relevant data. Cleaning and organizing data is a big deal in AI system-building. The quality of the data used for training, whether it's structured or not, plays a huge role in how well your AI system performs.
Cleaning data is like giving it a good tidy-up before AI model training. You sort it out, chuck out the incomplete bits, and put it in order to make sense of it. The aim is to fix or remove errors, ensuring the AI model learns from accurate and reliable information in the training data.
Once your data is squeaky clean, it's time to create algorithms. These are like math instructions telling the computer what to do, processing data and making predictions.
Now, it's training time. You feed your data into the algorithms, letting them learn the ropes. They adjust themselves to get better – tweaking parameters and weights for peak performance.
Optimizing these algorithms is key for high accuracy during training. Fine-tune those parameters, adjust the model setup – the goal is top-notch performance.
To ensure your AI model hits the mark, set a minimum acceptable threshold for the performance metric that matters most to you (e.g., accuracy, precision, or recall). This is the level of performance considered good enough for the model.
Finally, once your AI model is trained and fine-tuned, it's time to deploy the model and ensure its performance is monitored. Monitoring and maintenance are vital to keep the model performing well. Regular checks allow for any tweaks or improvements needed.
Does this sound like a lengthy process? It can be, if it's necessary to do it with hand-crafted code — or it can all be handled seamlessly with automated tools. Which approach is right for you? Read on to explore these options.
Choosing the right method for building your AI model
Crafting your own AI model offers a host of benefits. Data analysts can tailor predictions to incorporate domain-specific knowledge, and they can fine-tune models to meet the ever-evolving needs of the business.
Building an AI model also unleashes creativity, empowering builders to find the solution that’s just right.
Whether you're a coding expert or a total beginner, here are three ways to build your first AI model. You can choose the one that best fits your use cases, tech stack, existing systems, and data types.
1. No-Code/Low-Code Platforms (Easiest)
Data analysts can skip major coding hurdles with no-code or low-code platforms. (Pecan offers a free trial where analysts can build a model in minutes.)
As the most straightforward approach, it’s a bit like buying several cakes and frostings with different flavors to see what tastes best, rather than baking from scratch. It empowers you to focus on the flavor the cake ultimately delivers (a.k.a. the business value of predictive modeling), without getting hung up on the details of the baking process.
Constructing an AI model becomes as simple as dragging, dropping, and clicking. With complete guidance throughout the process, anyone can design workflows, connect data sources, and configure model parameters.
In the case of Pecan, familiarity with SQL is all you need to use your data for predictive modeling. Our Predictive GenAI capabilities will kickstart your projects and make it faster and easier to get going.
While they lack the flexibility of low-code platforms, they are still powerful solutions that can quickly understand data patterns, make predictions and guide decisions. These platforms are ideal for real-time decision-making and quick setup without the hassle of coding.
Pros:
- Accessibility: With intuitive user interfaces, these platforms open up AI model building to anyone, not just programming experts.
- Faster Results: Quickly gleans insights from historical data without coding hurdles.
Cons:
- Limited Scope and Customization: Sacrifices high levels of customization and flexibility for ease of use and streamlined functionality.
- Data Knowledge Requirement: Benefits from a basic understanding of data, and sometimes SQL, to ensure meaningful insights.
2. AutoML (The Middle Ground)
If no-code platforms are like buying a designer cake and programming is like baking from scratch, then automated machine learning (AutoML) is akin to a box of premixed cake mix. Simply add the wet ingredients, mix, and bake.
It's a balanced approach, both convenient and customizable. Platforms like Google AutoML, H2O.ai, and Azure AutoML automate the training process, including feature selection, hyperparameter tuning, and model evaluation.
This approach benefits from domain knowledge and some technical expertise to ensure optimal results.
Pros:
- Resource Streamlining: Automates feature engineering, algorithm selection, and hyperparameter tuning.
- Reduced Manual Error: Minimizes errors from manual coding, thereby providing more accurate results.
- Cons:Black-Box Models: A lack of insights into an automated model’s inner workings could hinder teams from explaining its outcomes.
- Limited Customization and Domain Expertise: Unable to capture subtle industry nuances for AI projects with highly specialized requirements.
3. Traditional Programming and Machine Learning Libraries (Hardest)
Are you well-versed in Python and popular libraries like scikit-learn, TensorFlow, or PyTorch?
If yes, you can use your coding skills to build your own AI model. It’s like baking a cake from scratch (without a recipe!): Think ingredients, precise measurements, and baking times. It involves trial and error, experimenting until you achieve perfection.
As an experienced data analyst or data scientist, you can flex your data preprocessing, algorithm selection, training, and evaluation skills. It’s the ideal method for individuals aiming to create models directly implementable within the organization. But as the hardest method, it requires advanced technical prowess.
Pros:
- Ultimate Customization: Gain complete control over everything. Experiment with algorithms and capabilities, even including computer vision or natural language procession (NLP), and test the outcomes.
- Field Nuance Application: Engineer features that capture domain expertise and industry nuance for better performance.
Cons:
- Steep Learning Curve: Takes significant time to learn programming languages, libraries, and algorithms.
- Resource-Intensive: Requires time and resources to fine-tune code, which could detract from other AI projects.
How much does it cost to develop an AI model?
Developing an AI model can vary in cost depending on various factors, such as the complexity of the model, the level of customization required, and the resources needed. Here is a breakdown of how you'll likely incur costs for building your own AI model:
- Custom or Off-the-Shelf: The cost of developing a custom AI model depends on the complexity and specific requirements. Alternatively, an off-the-shelf AI model can be a more cost-effective option, with prices varying depending on the provider and the features included.
- Prototype Development: The development of a prototype AI model includes the initial design and implementation of the model, including data collection and preprocessing, algorithm development, and testing.
- Software Cost: The cost of the software required to develop and deploy an AI model can range widely. This includes the cost of AI development platforms, programming tools, and other software components necessary for building and running the model.
- Maintenance: AI models require ongoing maintenance to ensure their performance and accuracy. This includes regular updates, bug fixes, monitoring, and optimization to keep the model up-to-date and functioning effectively.
Costs can vary significantly depending on the AI model's specific requirements and the development team's specialized knowledge and experience.
Consider the ethical implications
Embarking on the journey of creating your own AI model is thrilling, but it comes with some important ethical considerations.
One big concern is the chance of unintentional biases creeping into your AI. These biases often come from the data used to train the model and can mirror existing societal prejudices. Responsible developers must be aware of this and work towards building AI systems that are fair, unbiased, and inclusive.
Another thing to keep in mind is privacy. As we give machines the ability to analyze a ton of data, it's vital to find a balance between innovation and protecting people's privacy.
When building your AI model, it's important to be careful about collecting, storing, and using data. This isn't just a legal obligation – it's a moral duty to ensure that the tools we create respect people's privacy rights.
So, as you venture into AI development, remember to be not just a tech enthusiast but a guardian of fairness and privacy.
Try it out for yourself
There's no one-size-fits-all method for building your own AI model. Your approach depends on the time you have available, your level of coding expertise, and your customization needs.
If you’re looking for an easy way to get started immediately, a low-code platform could be a great solution, enabling you to capitalize on your existing data and domain expertise.
Whatever path you choose to build your first AI model will undoubtedly open doors to new opportunities, equip you with valuable insights, and fuel your data professional career trajectory. Remember, the future isn't AI replacing us — it's us, with AI, shaping the future.
Ready to build your own AI model today? Sign up for a free trial of Pecan, and let our Predictive GenAI guide you to success.
