Artificial intelligence is everywhere, it’s gone so far that we even have smart toilets now. Jokes aside, from voice assistants on our phones to ads that feel like they’re eavesdropping, and the algorithms that recommend what to watch on Netflix, AI has become a key player across industries. Healthcare companies use it to diagnose diseases, banks to detect fraud, and even the entertainment industry has been transformed with tools that can generate music, art, and content in seconds.
But despite its massive presence in our lives, one thing sets AI apart from traditional software: it’s not built the same way. Traditional software is programmed with clear and predictable rules. AI, by contrast, doesn’t follow fixed rules, it learns. Instead of telling a system exactly what to do, AI models learn patterns from large datasets. And because learning is never perfect on the first try, the development process isn’t linear. It’s a constant cycle of trial, error, and refinement.
This is where experimentation in data science comes in. Building an AI model isn’t just about writing code and running it, it’s about testing algorithms, tweaking parameters, training models with different datasets, and evaluating performance until you find the best possible version. AI is, at its core, a field driven by continuous experimentation.
Why AI Doesn’t Follow Traditional Development Processes?
If you think about it, traditional software development is quite structured. A team defines requirements, writes rule-based code, tests it, and deploys. If something breaks, it gets fixed. It’s predictable and directly controlled.
AI development is completely different. Here, you don’t program rules, you train models to learn on their own. Instead of specifying steps, data scientists feed the model massive amounts of data and refine it until it starts delivering useful results. This process is full of uncertainty, testing, and iteration.
AI Isn’t Programmed, It’s Trained
The key mindset shift is that AI isn’t programmed in the traditional sense. It’s trained. Think of it like teaching a child to recognize animals. You don’t give them a rulebook, you show them lots of pictures until they start spotting patterns on their own. Same with AI: feed it with data (images, text, audio, etc.) and it will learn from that.
But here’s the important part: learning is never perfect on the first try. Unlike conventional programs that work as expected once coded correctly, AI models need constant tweaking. They can be overfitted, biased, or just produce unexpected results. The process is never final.
The Role of Experimentation in AI
If there’s one thing that defines AI development, it’s constant experimentation. Unlike traditional software that just "works" once correctly implemented, AI models don’t come with guarantees. The process is closer to the scientific method: create a hypothesis, test it, adjust, repeat.
AI is a constantly evolving experiment
For a model to perform well, it’s not enough to just pick an algorithm and hit run. Many variables influence performance:
- Hyperparameters: These control how the model learns. Tuning them is like tuning an instrument: too loose, and it underperforms; too tight, and it overfits.
- Model architecture: Neural networks, for instance, require decisions on how many layers and neurons to include. More complexity can improve precision but also increase training difficulty.
- Data quality and volume: A great algorithm trained on poor data will yield bad results. Experimentation also means improving your dataset.
The AI Experimentation Cycle: Iteration After Iteration
AI development is cyclical. Each experiment refines the model. It usually includes six key steps:
- Understand the business problem: Define the use case, choose relevant data sources, and set success metrics (both technical and business).
- Data collection and analysis: Explore existing data (EDA). Often, the data isn’t rich enough, prompting a rethink of the project or data sources.
- Data preparation and feature engineering: Clean, transform, and extract valuable information. Poor prep here can sink the entire project.
- Modeling: Define goals, explore models iteratively, and refine based on feedback. This includes:
- Gathering more data
- Cleaning differently
- Creating new features
- Testing other models
- Tuning hyperparameters
Modeling is a deeply exploratory process full of trial and error.
- Evaluation: Test models on unseen data. If results fall short, go back and iterate.
- Deployment: After testing, verifying quality, meeting budget and performance needs, the model can go to production.
This process may repeat dozens or hundreds of times. There is rarely a single "right" solution. One model might be more accurate but slower, another more efficient but less precise. Experimentation helps find the right balance.
It All Starts With the Data
We often focus on models and algorithms, but the real star of AI is the data. No matter how advanced your model is, bad data will lead to bad outcomes.
That’s why data science and experimentation go hand in hand. Data scientists don’t just choose the best algorithm. They ensure training data is clean, representative, and bias-free through trial and adjustment.
1- Clean and Prep Your Data: The Foundation
Before even thinking about training a model, you need to make sure the data is reliable. In the real world, data is rarely perfect: it can be incomplete, contain errors, or be irrelevant.
Example: Imagine you want to train an AI to predict house prices, but your dataset contains properties with negative prices or missing location information. If these errors aren't corrected, the model will learn incorrect patterns and produce unreliable predictions.
Experimentation at this stage involves trying different cleaning techniques and seeing which one works best. Is it better to remove incomplete data or try to fill it in? Can you detect outliers that affect the model's accuracy? Every decision at this stage directly impacts the final performance.
2- Feature Engineering: Picking What Matters
Not all data is useful for an AI model. Feature selection (or feature engineering) involves identifying which variables truly add value and which just add noise.
Example: Let's continue with the case of house price prediction. Is the number of bathrooms or the distance to the city center more relevant? Does the year of construction have a greater influence than the size of the garden?
Data scientists experiment with different combinations of variables, testing how they affect model performance. Sometimes, creating a new variable by combining two existing ones (such as "price per square meter") can significantly improve results.
3- Evaluating Models and Identifying Bias
Once the data is ready, the model training and evaluation phase begins. But this isn't just about seeing which model has the best accuracy, but also about identifying potential biases in the data.
Example: An AI system designed to evaluate job applications might learn biased patterns if the training data reflects past inequalities. If men have historically been hired more than women in certain sectors, the model might reinforce that tendency rather than correct it.
Therefore, data scientists experiment with different approaches to minimize bias and ensure the model is fair and equitable. This may involve balancing the data, adjusting class weights, or even applying explainability techniques to better understand how the model makes decisions.
4- Small Data Tweaks, Big Impacts
One of the most fascinating (and challenging) aspects of data science experimentation is that a small tweak to the data can completely change a model's performance.
Example: In one experiment, a team of researchers found that removing just 5% of the training data, specifically the noisiest cases, improved model accuracy by 15%.
This shows that the key isn't always using more data, but rather using the right data. Experimentation in data science isn't a luxury; it's a necessity.
Challenges in AI Experimentation
If there’s one thing we’ve learned so far, it’s that artificial intelligence depends on constant experimentation. But experimenting in AI isn’t as easy as tweaking a few lines of code, every iteration can introduce new problems and obstacles. From reproducibility issues to the high computational costs, AI experimentation is a far more complex process than it might seem.
Let’s explore some of the most important challenges and how they impact AI model development.
1- Reproducibility: When Results Can’t Be Repeated
In science, an experiment is only valid if it can be replicated. But in AI, even using the same data and parameters, two training runs can yield different results. Why? Random initialization, hardware variation, or even minor differences like the version of a machine learning library.
Example: One team trains a model and achieves 92% accuracy. Another team tries to replicate it with the same config and gets only 88%. The culprit? Slight implementation differences, runtime environment, or untracked dependencies.
Solutions:
- Fijar semillas aleatorias (random seeds) para asegurar que los resultados sean consistentes.
- Documentar detalladamente los procesos de entrenamiento.
- Usar herramientas como MLflow o Weights & Biases para rastrear experimentos y comparar resultados.
2- Bias in Data and Models: Experiments Can Amplify Errors
One of the biggest risks in AI is that models learn from their training data. If that data is biased, the model will reflect and even reinforce those biases.
Example: An AI hiring system may favor certain groups if the training data contains historical inequalities. These biases often go unnoticed until the model is already live, reinforcing systemic discrimination.
Solutions:
- Audit and clean data for bias before training.
- Apply debiasing techniques like reweighting or synthetic data generation.
- Use explainability tools (LIME, SHAP) to better understand model decision-making.
3- High Computational Cost: AI Isn’t Cheap
Training AI models, especially deep neural networks, demands immense computing power. GPUs, TPUs, cloud storage, every experiment burns through energy and money.
Example: Training a single advanced language model (like those behind ChatGPT) can cost millions in infrastructure. Even smaller models can strain the budgets of many organizations.
Solutions:
- Use transfer learning to build on pre-trained models instead of starting from scratch.
- Apply model optimization (quantization, pruning) to shrink models without losing accuracy.
- Leverage cloud platforms with ML-optimized infrastructure.
4- Time and Scalability: Experiments Can Take Days
Training isn’t instant. Depending on model complexity and data size, one experiment might take hours, days, or even weeks. This makes process optimization essential.
Example: A computer vision model might need 72 hours to train. If the performance is poor, it needs to be adjusted and retrained, a major time sink, especially when running multiple experiments in parallel.
Solutions:
- Use early stopping to cut training when improvements plateau.
- Apply distributed training to split workloads across machines.
- Use AutoML for automated hyperparameter tuning.
AI Experimentation: More Than Trial and Error
Experimenting in AI isn’t just "try it and see." Each experiment introduces technical, ethical, and logistical challenges. Ensuring reproducibility, reducing bias, optimizing costs, and accelerating iterations are part of every AI team’s reality.
In the end, it’s not just about building models that work, it’s about building them efficiently, transparently, and responsibly. Because in AI, just like in science, it’s not enough to get results. You need to understand why they work and how to make them better.
All of this might sound inspiring, the kind of thing that makes you want to jump headfirst into AI. After all, it’s the cool kid at the dance everyone wants to partner with. But it’s also important to think about the ethical implications and the evolving role of humans in creating increasingly intelligent systems.
We love this topic as much as you do, which is why we went a step further and wrote a follow-up article titled "The Future of AI and Experimentation: Autonomy, Ethical Challenges, and the Human Role." Curious? Don’t miss out, go read it now!