Data Creation for Machine Learning: A Step-by-Step Guide

Your brilliant AI idea needs one thing to succeed: great data. But finding, cleaning, and preparing that data is often the biggest hurdle, stalling projects before they even start. This foundational work, known as data creation for machine learning, is where most AI initiatives either win or lose. It’s about strategically building the strong, reliable dataset your model needs to perform accurately. This guide provides a clear roadmap for this critical stage, giving you the methods and best practices to set your project up for success from day one.

Key takeaways

• Make data quality your foundation: The success of your AI project hinges on the quality of your data. Dedicate the necessary time to cleaning, validating, and accurately labeling your dataset to build a model that produces reliable and trustworthy results.
• Strategically source your dataset: You have multiple options for acquiring data. Start by using your company's internal information, generate synthetic data to address privacy concerns, pull from public APIs for structured access, or crowdsource labeling for tasks that need a human touch.
• Adopt an iterative approach to data management: Your dataset is a living asset, not a one-time project. Continuously evaluate, refine, and version your data to keep your models effective over time and ensure every experiment is reproducible.

What is data creation for machine learning?

Think of dataset creation like preparing your ingredients before baking a cake. You wouldn't just throw flour, eggs, and sugar into a bowl and hope for the best. You measure, sift, and mix them in a specific order. Data creation is the exact same idea, but for your AI projects. It’s the essential process of gathering, cleaning, and organizing raw data so it’s perfectly prepared for a machine learning (ML) model to learn from. This is arguably the most critical stage of any AI initiative, setting the foundation for everything that follows.

Without high-quality, well-prepared data, even the most sophisticated algorithm will produce poor, unreliable results. This foundational work involves transforming messy, inconsistent information from various sources into a structured, clean, and usable dataset. It’s often the most time-consuming part of building an AI solution, but getting it right is the difference between a model that drives real business value and one that falls flat. At Cake, we help streamline this entire process, providing the infrastructure and tools to make data creation efficient and effective.

The time-consuming nature of data preparation

Let's be honest: preparing your data is going to take a while. It’s not uncommon for this stage to consume the majority of an AI project's timeline—sometimes months—before a single model is even trained. This isn't just busy work; it's the most critical investment you can make in your project's success. In fact, it's a well-known principle that data scientists can spend up to 80% of their time on this task alone. High-quality models are built on high-quality data, and rushing this step is a recipe for inaccurate, unreliable results. This is a major reason why so many AI initiatives stall, and it’s why we built Cake to help manage the entire stack, accelerating this phase so you can move from raw data to a production-ready model more efficiently.

Data creation vs. data wrangling vs. data munging

You'll hear a few different terms thrown around when people talk about getting data ready for AI, and it can get confusing. Often, you'll see "data preparation," "data wrangling," and "data munging" used to describe the same core activity. They all refer to the process of taking raw, messy data and cleaning, transforming, and organizing it into a structured format that a machine learning model can actually use. Think of "data creation" as the slightly broader umbrella term. It includes the entire process, from the initial sourcing or generation of the data all the way through those final preparation steps. It’s about building the complete, high-quality dataset from the ground up, ready for your AI initiative.

The building blocks of data creation

The core principle of data creation is simple: the quality of your model depends entirely on the quality of your data. It’s the classic "garbage in, garbage out" scenario. If you feed your algorithm inaccurate, incomplete, or irrelevant data, you can’t expect it to make smart predictions. The key components of this process focus on ensuring your data is clean, consistent, and relevant to the problem you’re trying to solve.

It’s also important to understand that data creation isn't a one-time task. It’s an ongoing, iterative process. As your business collects new information or as you refine your model, you’ll need to revisit and update your dataset. This continuous loop of improvement is what leads to robust and reliable AI systems that adapt and grow with your organization.

IN DEPTH: Dataset Creation Functionality, Built With Cake

A step-by-step look at the data creation process

Breaking down data creation into a series of steps makes the process much more manageable. While the specifics can vary, the journey from raw data to a model-ready dataset generally follows a clear path.

1. Data collection: First, you need to gather the raw data. This involves pulling information from all relevant sources, which could include internal databases, customer relationship management (CRM) systems, spreadsheets, or external APIs. The goal is to collect a comprehensive set of data that pertains to your project.
2. Data cleaning: This is where you handle imperfections. You'll identify and correct errors, fill in missing values, and remove duplicate entries. You also need to address outliers—extreme values that could skew your model's learning process.
3. Data transformation: Once your data is clean, you need to get it into the right format. This often involves converting categorical data (like text labels) into numerical values and scaling features so they are on a comparable range.
4. Data splitting: Finally, you divide your pristine dataset into at least two parts: a training set and a testing set. The model learns from the training set, and you use the testing set to evaluate its performance on new, unseen data.

Before you start: defining your strategy

Before you pull a single piece of data, you need a plan. It’s tempting to dive right in and start gathering information, but without a clear strategy, you risk wasting time and resources on data that won’t help you. The core principle here is simple: the quality of your model depends entirely on the quality of your data. It’s the classic "garbage in, garbage out" scenario. A well-defined strategy acts as your quality control, ensuring you only collect and prepare data that directly serves your end goal. This initial planning phase is where you map out exactly what you want to achieve, which informs every decision you make down the line, from data sourcing to model selection.

Think of your strategy as the blueprint for your entire AI project. It forces you to ask the tough questions upfront. What specific business problem are you trying to solve? What does success look like, and how will you measure it? Answering these questions helps you focus your efforts and avoid common pitfalls. This isn't just about technical specifications; it's about aligning your AI initiative with tangible business outcomes. Having a robust platform to manage your infrastructure and workflows, like the solutions we offer at Cake, allows your team to focus on executing this strategy instead of getting bogged down by operational complexities.

Define your machine learning goal

Your first strategic step is to define a clear, specific machine learning goal. This goal is the north star for your project, guiding your data collection and preparation efforts. Are you trying to predict which customers are most likely to churn? Or perhaps you want to categorize incoming customer support tickets to route them more efficiently? The success of your AI project hinges on the quality of your data, and you can't determine what "quality data" looks like without a precise objective. A vague goal leads to a vague dataset, which in turn leads to an unreliable model. Be as specific as possible to set a solid foundation for your work.

Classification, regression, and clustering models

Your goal will likely fall into one of three main machine learning categories. Understanding these helps you frame your problem and select the right kind of data. According to AltexSoft, machine learning models can be broadly categorized into three types: classification, regression, and clustering. Classification models predict a category (e.g., "Is this email spam or not?"). Regression models predict a continuous value (e.g., "How much will this house sell for?"). Clustering models identify natural groupings within your data without predefined labels (e.g., "What are our main customer segments based on purchasing behavior?"). Pinpointing which type of model you need is a critical step in defining your data requirements.

Understand explanatory vs. predictive modeling

It's also crucial to know whether your goal is explanatory or predictive. While they sound similar, they serve very different purposes. As Towards Data Science explains, explanatory models are designed to help you understand the relationship between different variables. They answer the "why" questions. For example, an explanatory model could help you understand the key factors that drive employee satisfaction. In contrast, a predictive model is focused on making the most accurate forecast possible. It answers the "what will happen" questions, like predicting which employees are at high risk of leaving in the next six months. Your choice between the two will fundamentally shape your approach to data selection and model evaluation.

Why high-quality data is the key to ML success

Your ML model is only as good as the data you feed it. You can have the most sophisticated algorithm and the most powerful compute infrastructure, but if your data is messy, incomplete, or irrelevant, your results will be disappointing at best and harmful at worst. This is why data preparation is often the most time-consuming part of any AI project—it’s also the most critical.

Getting your data right from the start prevents major headaches down the line. It ensures your model can learn the right patterns and make accurate, reliable predictions that you can trust to make business decisions. When you have a solid data foundation, you set your entire project up for success. This allows your team to focus on refining models and driving results, rather than constantly backtracking to fix underlying data issues. At Cake, we handle the complex infrastructure so you can dedicate your energy to what truly matters: building a high-quality dataset that powers incredible AI.

How quality data improves model performance

Think of your dataset as the textbook your model studies to learn a new skill. If the book is full of clear examples, well-organized chapters, and accurate information, the student will excel. The same goes for your model. High-quality, clean, and relevant data leads directly to better model performance. In fact, the quality and size of your dataset often impact the success of your ML model more than the specific algorithm you choose. A well-prepared dataset helps the model generalize better, meaning it can make accurate predictions on new, unseen data, which is the ultimate goal. This translates to more reliable insights, more effective applications, and a much higher return on your AI investment.

What happens when you use bad data?

Using low-quality data is like building a house on a shaky foundation—it’s bound to collapse. When a model is trained on inaccurate, biased, or incomplete information, its predictions will reflect those flaws. This can lead to skewed insights, poor business decisions, and a complete lack of trust in your AI system. In some industries, the consequences can be even more severe. For example, flawed data in a healthcare project could lead to incorrect diagnoses or treatment recommendations. Ultimately, bad data wastes valuable time and resources, erodes confidence in your project, and can cause the entire initiative to fail before it even gets off the ground.

IN DEPTH: MLOps, Built With Cake

How to create your ML dataset: five key methods

Once you know what kind of data you need, it’s time to go out and get it. Building a high-quality dataset is one of the most critical steps in any ML project, and thankfully, you have several options. The right method for you will depend on your project’s goals, your budget, and the resources available to you. Whether you’re using data you already own or creating it from scratch, the key is to choose a path that gives you clean, relevant, and reliable information to train your model.

This process is foundational; the quality of your data directly influences the performance and accuracy of your final AI model. Think of it as sourcing the best ingredients for a complex recipe. You can start by looking inward at the data your organization already collects, which is often the most relevant and cost-effective source. If that's not enough, you can generate new data, pull it from public sources on the web, or even hire a crowd to create it for you. Let’s walk through the most common methods to build your dataset so you can make an informed decision and set your project up for success from the very beginning.

1. Start with the data you already have

Your first stop should always be your own backyard. Your company is likely sitting on a treasure trove of data from sales records, customer support interactions, and user activity logs. This internal data is a fantastic starting point because it’s directly relevant to your business. Before you use it, make sure you have a solid data governance framework in place to handle compliance and security. If you need more, you can gather it directly from users by building feedback loops into your product or offering valuable features in exchange for data, which helps keep your dataset fresh and aligned with real-world behavior.

2. Create your own synthetic data

What if the data you need is too sensitive or rare to collect easily? That’s where synthetic data comes in. Think of it as a digital stunt double for real data—it’s artificially generated by computer algorithms to mimic the statistical properties of a real-world dataset. Because it contains no actual personal information, you can use it to train and test your models without navigating complex privacy hurdles. This approach is incredibly useful for filling gaps in your existing data, balancing out an imbalanced dataset, or creating edge-case scenarios that your model needs to learn from but rarely appear in the wild.

BLOG: What is Synthetic Data Generation? A Practical Guide

3. Scrape data from the web

If the data you need is publicly available online, web scraping can be a powerful tool. Using code libraries like BeautifulSoup or Scrapy, you can create custom crawlers that automatically pull information directly from websites. This method is great for gathering large volumes of data, like product reviews, news articles, or social media posts. However, it’s important to proceed with caution. Always check a website’s terms of service before scraping, and be mindful of the legal and ethical considerations to ensure you’re collecting data responsibly and respectfully.

4. Pull data directly using APIs

A more structured and reliable alternative to web scraping is using an API, or Application Programming Interface. Many services—from social media platforms to data providers like Bloomberg—offer APIs that allow you to programmatically request and receive data in a clean, organized format. Instead of manually scraping a site, you’re essentially asking for the data directly through an official channel. This is often the preferred method because it’s more stable and respects the provider’s terms of use. If a service you want data from offers a public API, it’s almost always the best place to start.

5. Crowdsource your data

Sometimes, creating a dataset requires a human touch, especially for tasks like labeling images or transcribing audio. This is where crowdsourcing shines. Platforms like Amazon Mechanical Turk allow you to outsource small data-related tasks to a large pool of remote workers. You can quickly gather vast amounts of labeled data that would be incredibly time-consuming to create on your own. This method is perfect for building datasets that rely on human judgment and interpretation. It’s a scalable and often cost-effective way to get the high-quality, human-powered data your ML model needs to succeed.

Simple rules for creating high-quality data

High-quality data is the foundation of any successful AI model, and skipping this step is a recipe for inaccurate results and wasted resources. Following a few key best practices ensures your data is clean, consistent, and ready to train a model that performs reliably. These steps aren't just about fixing errors—they're about strategically refining your most valuable asset to get the best possible outcome from your AI initiative. Let's walk through the essential practices for preparing a top-tier dataset.

Start with a smaller, manageable dataset

It might feel counterintuitive, but diving into a massive dataset from the get-go can actually slow you down. The reality is that data preparation is incredibly time-consuming and often takes up the majority of a project's timeline. By starting with a smaller, more manageable dataset, you give your team the ability to iterate quickly. You can build a baseline model, test your assumptions, and refine your approach without getting bogged down by the complexities of cleaning and processing terabytes of information. This agile method allows you to prove your concept and identify potential issues early on. Once you have a working model and a clear process, you can scale up and introduce more data with confidence.

Watch out for bias during data collection

Bias is one of the most subtle yet significant risks in data creation. If your collection methods systematically favor certain outcomes or groups, your model will learn and amplify those same biases, leading to skewed and unreliable results. This can happen in many ways—from collecting data only during business hours to using measurement tools that have known errors. To combat this, you must ensure your dataset is a representative sample of the problem you're trying to solve. Scrutinize your collection process for any potential sources of bias, whether they come from human error, technical glitches, or flawed sampling strategies. A model built on fair, balanced data is a model you can trust.

Make sure your data is valid

Before you start cleaning or labeling, you need to perform a quality check. Data validation is the process of auditing your dataset to understand its condition. This is where you look for common problems like human input errors, missing values, or technical glitches that might have occurred during data transfer. It’s also the time to ask bigger questions: Is this data actually suitable for my project? Do I have enough of it? Is the data imbalanced, meaning one category is overrepresented? A thorough data validation process gives you a clear picture of the work ahead and prevents you from building your model on a shaky foundation.

A practical guide to cleaning your data

Data cleaning is where you roll up your sleeves and fix the issues you found during validation. This step involves correcting errors, handling missing information, and ensuring all your data is consistent. For example, if some entries are missing a value, you might substitute them with a placeholder, the mean average, or the most frequent entry in that column. It’s also crucial to standardize your data. This means making sure all units of measurement are the same (e.g., converting everything to kilograms) or that all dates follow a single format. These data cleaning techniques create the consistency your model needs to learn effectively.

How to identify outliers with z-scores

Outliers are data points that are dramatically different from the rest of your data—think of a single day with a massive, unexplained spike in website traffic. These anomalies can throw off your model's training, so it's important to find them. A simple and effective way to do this is by calculating a z-score for each data point. The z-score tells you how many standard deviations a point is from the mean. As a rule of thumb, a z-score above 3 or below -3 often points to an outlier. Once you've identified these points, you can decide whether to remove them or adjust them so they don't skew your results.

Convert categorical data into numbers

Machine learning models work with numbers, not text. That means you need to convert categorical data—like product categories or customer types—into a numerical format. This process is called encoding. Two common methods are Label Encoding, where you assign a unique number to each category (e.g., "red" becomes 1, "blue" becomes 2), and One-Hot Encoding. With One-Hot Encoding, you create a new column for each category and use a 1 or 0 to indicate whether the category applies. This step is essential for making your data understandable to the algorithm.

Turn numerical data into categories

It might seem counterintuitive, but sometimes it’s helpful to group numerical data into categories. This process, often called binning, can simplify the data and help your model identify broader patterns. For example, instead of using exact ages, you could group them into ranges like "18-25," "26-35," and so on. This can reduce the impact of minor variations and make the relationships between features clearer for the model to learn. It’s a great way to manage continuous data and prevent the model from getting bogged down in details that aren't significant.

The right way to label your data

For many ML models, especially in supervised learning, data needs to be labeled. Labeling, or annotation, is the process of adding meaningful tags to your data so the model can understand what it's looking at. For an image recognition model, this could mean drawing boxes around objects in photos and tagging them as "car" or "pedestrian." For a language model, it might involve identifying the sentiment of a customer review. The accuracy of these labels is critical. If your data is labeled incorrectly or inconsistently, you're essentially teaching your model the wrong information, which will lead to poor performance and unreliable predictions.

Expand your dataset with augmentation

What do you do when you don't have enough data? You augment it. Data augmentation is a powerful technique for expanding your dataset and improving model performance. One approach is to create new data from your existing data—for example, by rotating, cropping, or altering the colors of images. Another method is to generate entirely new, synthetic data, which is especially useful when real-world data is sensitive or scarce. You can also supplement your dataset by incorporating publicly available datasets. This can add diversity to your training data and save you the time and expense of collecting and labeling everything from scratch.

Creating a high-quality dataset is often the most time-consuming part of any ML project. It's not uncommon for data preparation to consume 80% of a project's time, a reality that can slow down even the most ambitious AI initiatives.

Split your data for training and testing

After all the cleaning and labeling, your dataset is almost ready. The final step before training is to split it into at least two separate parts: a training set and a testing set. Think of this like preparing for an exam. The training set is the textbook and study materials your model uses to learn the patterns and relationships in the data. The testing set is the final exam—a set of new, unseen data that the model has never encountered before. This allows you to accurately evaluate how well your model performs in a real-world scenario. This division is crucial for ensuring your model can generalize its knowledge and isn't just memorizing the training data.

Use common ratios like 80/20

A standard and effective way to divide your data is the 80/20 split, where 80% of the data is used for training and the remaining 20% is reserved for testing. For more complex projects, you might use an 80/10/10 split. In this case, 80% is for training, 10% is for a validation set used to fine-tune the model's parameters during development, and the final 10% is for testing. These ratios are a great starting point, but they aren't rigid. The ideal split can depend on the overall size of your dataset; if you have a massive amount of data, you might be able to get away with a smaller percentage for your testing set.

Randomize your data before splitting

Before you split your data, it’s essential to shuffle it randomly. This step ensures that both your training and testing sets are representative samples of the overall dataset. Imagine your data is sorted in a specific order, like by date or customer type. Without randomization, you might accidentally train your model on one group and test it on a completely different one, leading to a skewed and inaccurate evaluation of its performance. Shuffling mixes everything up, guaranteeing a balanced distribution of data points across both sets and preventing any unintentional bias from creeping into your model's training process.

How to handle time-series data

The one major exception to the randomization rule is time-series data. If your data has a chronological component, like stock prices or monthly sales figures, you must preserve its order. Randomizing this type of data would be like giving a student the answers to an exam before they've learned the material—it breaks the logical flow of events. For time-series data, you should perform a chronological split. This means your training set will consist of older data, and your testing set will be made up of more recent data. This approach mimics reality, where you use past information to predict future outcomes.

The right tools and resources for data creation

Creating a high-quality dataset is often the most time-consuming part of any ML project. It's not uncommon for data preparation to consume 80% of a project's time, a reality that can slow down even the most ambitious AI initiatives. The right set of tools doesn't just make this process faster; it makes it more reliable and repeatable. Think of these tools as your support system, helping you build a solid foundation for your AI models without getting bogged down in manual, error-prone tasks. By streamlining data creation, you free up your team to focus on what really matters: building innovative models and driving business value.

The good news is that there’s a rich ecosystem of tools available, from open-source libraries to comprehensive cloud platforms. Choosing the right combination for your specific needs is the first step toward a more efficient and successful project. To help you find what you need, I’ve broken them down into four key categories: tools for collecting raw data, platforms for finding existing datasets, services for labeling your data efficiently, and systems for keeping track of it all as it changes. Let's look at some of the best options in each category.

The role of data scientists and domain experts

Creating a high-quality dataset is a team sport, and two key players are the data scientist and the domain expert. The data scientist is the technical architect who knows how to structure, clean, and prepare data for machine learning. The domain expert is the subject matter specialist with deep knowledge of the industry and the specific business problem. Their domain expertise provides critical context, helping decide which data is truly important. A successful project needs both working in tandem, as the quality of your model depends entirely on the quality of the data you feed it.

This collaboration is an ongoing, iterative process. The domain expert guides the data scientist by pointing out nuances that a purely technical analysis might miss—like why a certain outlier is a critical piece of information and not just noise. The data scientist uses that insight to perform more effective data cleaning and validation. This feedback loop ensures the final dataset is not only statistically sound but also contextually rich and aligned with business goals. Without this partnership, you risk building your model on a shaky foundation, leading to results that are technically correct but practically useless.

Helpful libraries and platforms for data collection

When the exact data you need doesn’t exist yet, you might have to create it yourself by gathering information from the web. This is where data collection libraries come in handy. If your team is comfortable with Python, you can use powerful open-source libraries to build custom web crawlers and scrapers.

This approach gives you complete control over the data you gather, ensuring it’s perfectly tailored to your project’s needs from the very beginning.

Use Robotic Process Automation (RPA) for repetitive tasks

Data preparation can feel like a never-ending chore, often taking up the majority of time in an ML project. This is where Robotic Process Automation (RPA) comes in to handle the heavy lifting. Think of RPA as a team of digital assistants you can program to manage tedious, repetitive jobs like copying information between systems or filling out forms. By automating these routine tasks, you can significantly reduce human error and free up your team for more complex, strategic work. The right automation tools don't just make the process faster; they make your data creation more reliable and repeatable—the exact foundation you need for a successful AI model.

Great open-source and cloud-based tools to try

Why build from scratch when you don't have to? There are incredible platforms that host thousands of ready-made datasets, which can give your project a massive head start. Websites like Kaggle and the Hugging Face Hub are treasure troves for ML practitioners, offering datasets for everything from image recognition to natural language processing. You can also find valuable public data on government portals like data.gov.

Save time with automated data labeling tools

Labeling data—especially unstructured data like images, audio, and text—can be a huge bottleneck. Doing it manually is slow, expensive, and can lead to inconsistent results. This is where automated data labeling tools can be a game-changer. These platforms use ML to assist with the labeling process, significantly speeding things up and reducing costs.

Tools like Label Studio and V7 allow you to combine model-assisted labeling with human-in-the-loop workflows, improving accuracy while reducing manual effort. For document-heavy workflows, Docling helps extract structured information from PDFs and scanned files. These tools often use techniques like active learning to intelligently identify the most challenging data points that require a human eye, making the entire workflow smarter and more efficient.

Data versioning tools

As your project evolves, so will your dataset. You might add new data, clean up existing entries, or change labels. Without a system to track these changes, it's easy to lose track of which dataset was used to train which model version, making your results impossible to reproduce.

This is where data versioning comes in. Think of it as Git, but for your data. Tools like DVC (Data Version Control) integrate with your existing workflow to help you version your datasets, models, and experiments. This practice is essential for maintaining sanity in a team environment, ensuring that every experiment is reproducible, and allowing you to reliably track your model's performance as your data changes over time. It’s a critical step for building professional, production-ready ML systems.

Even with the best strategy, you’ll likely run into a few bumps on the road to creating the perfect dataset. Data is rarely perfect from the start.

Common data creation mistakes to avoid

Even with the best strategy, you’ll likely run into a few bumps on the road to creating the perfect dataset. Data is rarely perfect from the start, and the process of whipping it into shape is where many well-intentioned AI projects get stuck. These common missteps can do more than just slow you down; they can introduce hidden biases, create unreliable models, and ultimately cause your entire initiative to fall short of its goals. Knowing what these pitfalls look like ahead of time is the best way to sidestep them and keep your project on a smooth path to success.

The good news is that most of these mistakes are completely avoidable with a bit of foresight. They often stem from a few common misconceptions about how machine learning actually works. By understanding why simply collecting more data isn't always the answer and recognizing when to lean on automation instead of manual effort, you can save yourself a lot of time and frustration. Let's walk through two of the most frequent mistakes teams make during data creation so you can learn to spot them—and steer clear.

Believing more data is always better

It’s easy to fall into the "more is more" trap, assuming that a massive dataset is the secret ingredient for a powerful model. But in reality, the quality of your data is far more important than the sheer volume. Pouring huge amounts of low-quality, irrelevant, or inaccurate data into your model doesn't just fail to help—it can actively harm its performance. The algorithm will try to find patterns in the noise, learning the wrong lessons and leading to skewed, unreliable predictions. It’s much better to have a smaller, meticulously cleaned, and highly relevant dataset than a vast data swamp that wastes compute resources and teaches your model bad habits.

Assuming manual preparation is the best approach

There's a certain appeal to the idea of manually combing through your data, believing that a hands-on approach is the most thorough. While human oversight is crucial, relying solely on manual preparation is a recipe for burnout and errors. This work is incredibly time-consuming and repetitive, making it a prime candidate for human error. Automated tools are designed to handle these tasks faster, more consistently, and at a much larger scale. This is why integrated platforms are so valuable; they provide the infrastructure and pre-built components to automate the tedious parts of data prep, freeing your team to focus on model strategy and analysis instead of getting lost in the weeds.

How to solve common data creation challenges

Even with the best strategy, you’ll likely run into a few bumps on the road to creating the perfect dataset. Data is rarely perfect from the start. You might find that your dataset is lopsided, has frustrating gaps, or contains sensitive information you can't use. These are common issues, not dead ends. The key is to know how to spot them and what to do when you find them. By addressing these challenges head-on, you can refine your raw data into a high-quality asset that sets your ML project up for success.

What to do with imbalanced datasets

An imbalanced dataset happens when one category in your data is much more common than another. Think of a dataset for detecting manufacturing defects where 99.9% of the items are perfect and only 0.1% are flawed. A model trained on this data might learn to just guess "perfect" every time and still be highly accurate, but it would be useless for its actual purpose.

Start by performing a thorough data quality check to ensure the imbalance isn't due to human error or technical glitches. If the imbalance is legitimate, you can use techniques like oversampling (duplicating examples from the smaller category) or undersampling (removing examples from the larger category) to create a more balanced training set for your model.

How to handle missing or incomplete data

It’s common to find gaps or missing values in your dataset. This can happen for many reasons, from data entry errors to problems during data transfer. Ignoring these gaps can lead to inaccurate models or cause your training process to fail completely. Before you can use the data, you need a plan to handle these missing pieces.

For a quick fix, you can sometimes remove the rows or columns with missing values, but this isn't ideal if it means losing a lot of valuable data. A better approach is imputation, which involves filling in the blanks. You can substitute missing values with the mean, median, or most frequent value in that column. Many modern AI platforms, like the solutions offered by Cake, can help automate data cleaning and preparation to make this process much smoother.

How to overcome data fragmentation

Data fragmentation happens when your information is scattered across different systems that don't talk to each other—think sales data in your CRM, support tickets in another system, and user activity logs somewhere else. This gives your model a disjointed, incomplete view of the world, which leads to weaker predictions. The key is to integrate your data sources to create a single, unified view. Your first move should be to look inward at your company's existing data, as it's often the most valuable and relevant source. By combining different types of information—like specific purchase events with general customer demographics—you can build a much richer and more comprehensive dataset. This isn't a one-time task but an ongoing, iterative process that ensures your data remains cohesive and reliable as your business evolves.

Keeping data private and secure

Working with data often means handling sensitive information, which brings major privacy responsibilities. Using real customer data for development and testing can expose your organization to significant legal and ethical risks. You need a way to train effective models without compromising individual privacy.

This is where synthetic data becomes incredibly useful. Synthetic data is artificially generated information that mimics the statistical properties of your real dataset but contains no actual personal details. It allows your team to build, test, and refine models in a secure, privacy-compliant environment. Using synthetic data lets you explore your dataset's patterns and potential without ever touching the sensitive source information, ensuring your project respects privacy from the ground up.

How to scale data creation for large projects

When you’re starting out, creating a dataset by hand might feel manageable. But as your AI ambitions grow, that manual approach quickly becomes a bottleneck. To handle large-scale projects effectively, you need to move beyond manual data entry and adopt strategies that can grow with you. Scaling your data creation isn't just about getting more data; it's about getting it more efficiently and intelligently. By focusing on automation, distributed processing, and smart feature engineering, you can build a robust data pipeline that fuels even the most demanding ML models without overwhelming your team.

Put your data collection on autopilot

The first step to scaling is to take the human element out of repetitive data-gathering tasks. Automating your data collection process saves an incredible amount of time and significantly reduces the risk of manual errors. Instead of having your team spend weeks or months on tedious data handling, you can build automated workflows that pull, clean, and organize data from various sources. This frees up your data scientists to focus on what they do best: analysis and model building. A solid guide on data preparation for ML notes that streamlining this process allows teams to concentrate on analysis rather than manual work. This could mean writing scripts to pull from APIs or using platforms that connect directly to your databases.

Speed things up with distributed data processing

When you're dealing with massive datasets, a single machine just won't cut it. Distributed data processing is a technique where you split a large data task across multiple computers, allowing them to work in parallel. This is how you handle terabytes of data without waiting days for a single script to run. To do this effectively, you need the right infrastructure. This often involves using data warehouses for your structured data and data lakes for a mix of structured and unstructured information. By setting up these systems, you can prepare your dataset for any need and ensure your collection methods can scale efficiently. It’s a powerful approach that makes big data manageable.

Leverage high-performance computing architectures

Once you’re working with distributed systems, the next step is to make them as powerful as possible. High-performance computing (HPC) architectures are designed for exactly this purpose. Think of it as moving from a team of regular cars to a fleet of Formula 1 race cars. HPC uses specialized hardware, like graphics processing units (GPUs), and advanced software to process enormous datasets at incredible speeds. This is especially critical when you're dealing with complex, unstructured data like high-resolution images or video files, where standard processors would take days or even weeks to complete tasks. By leveraging HPC, you can drastically cut down the time it takes to clean, transform, and prepare your data, directly addressing the bottleneck that consumes so much of a project's timeline.

At Cake, we help teams accelerate their AI initiatives by managing the entire compute infrastructure stack, which is essential for handling large datasets efficiently with methods like massively parallel processing (MPP) and in-memory databases.

Setting up and maintaining a high-performance computing environment is a complex, full-time job that requires deep expertise. This is where we come in. At Cake, we manage the entire compute infrastructure for you, so your team can focus on building great models instead of wrestling with hardware and configurations. We use techniques like massively parallel processing (MPP), which coordinates hundreds or thousands of processors to work on a single, massive data task simultaneously. We also utilize in-memory databases that hold data in super-fast RAM instead of slower disk drives, making data access and processing nearly instantaneous. By providing this production-ready infrastructure, we help you scale your data creation efforts efficiently and get your AI initiatives off the ground faster.

Use feature engineering to improve performance

More data isn't always the answer; sometimes, you just need better data. Feature engineering is the process of using your domain knowledge to transform raw data into features that your model can understand more easily. It’s a critical step for improving model performance without needing to collect a mountain of new information. For example, instead of just feeding a model a timestamp, you could engineer features like "day of the week" or "time of day" to help it spot patterns. By creating new features from existing data, you can uncover hidden relationships and make your model significantly more accurate. This is where your team's creativity and expertise can really shine.

By building evaluation and refinement into your workflow from the start, you create a strong foundation for long-term AI success.

Simplify your dataset with data reduction

It might sound counterintuitive, but sometimes the key to a better model is less data. Data reduction is the process of simplifying your dataset without losing important information. Think of it as decluttering—you're getting rid of the noise so you can focus on what's truly important. This process, often called dimensionality reduction, involves reducing the number of features (or columns) in your dataset to only the most impactful ones. The goal is to make your dataset leaner and more efficient without sacrificing critical information, which helps your model learn faster and more accurately.

A simpler dataset is easier for a machine learning model to process. With fewer variables to consider, the model can identify the underlying patterns more effectively. This is a crucial step because data preparation can take up the majority of a project's timeline. By focusing only on the most relevant features, you're not just improving your model's performance; you're also making the entire development process more efficient. It’s a strategic move that helps you get to a production-ready model much faster.

How to know if your dataset is good (and how to fix it)

Creating your dataset is a huge milestone, but the work doesn’t stop there. Think of your dataset not as a finished product, but as a living asset that you need to regularly check on and improve. The quality of your data directly impacts your model's performance, so evaluating and refining it is a critical, ongoing part of the ML lifecycle. This continuous loop of feedback and improvement ensures your model remains accurate, relevant, and effective over time.

As your project evolves, you’ll gather more data and gain new insights. These changes require you to revisit your dataset to maintain its integrity. By building evaluation and refinement into your workflow from the start, you create a strong foundation for long-term AI success. This proactive approach helps you catch issues early and adapt to new information, keeping your models from becoming stale or biased. At Cake, we help teams manage the entire AI stack, which includes establishing these crucial feedback loops for data quality.

Key metrics for measuring data quality

You can’t improve what you don’t measure. To get a clear picture of your dataset's health, you need to assess its quality using a few core metrics. Start by checking for completeness—are there missing values or gaps that could confuse your model? Then, look at accuracy to spot and correct any human errors or technical glitches from data transfer. Consistency is also key; you want to ensure the same data point is represented the same way everywhere (e.g., "CA" vs. "California").

Another crucial step is to assess data imbalance. If your dataset has far more examples of one class than another, your model might struggle to learn from the underrepresented group. Regularly measuring these aspects of your data helps you pinpoint specific weaknesses and take targeted action to fix them.

Use data visualization to find patterns and problems

Sometimes, staring at a spreadsheet of raw data feels like trying to read a foreign language. This is where data visualization becomes your best friend. By turning your numbers into charts and graphs, you can quickly spot trends, patterns, and outliers that are nearly impossible to see in a table. Tools like histograms can show you how your data is spread out, while scatter plots can reveal relationships between different variables. This visual approach helps you quickly assess the quality of your data and make informed decisions about cleaning and preparation. It’s the fastest way to understand the story your data is telling and identify any problems, like biases or anomalies, before they have a chance to derail your model.

Keep refining your data over time

Data preparation isn't a one-time task you check off a list. It’s a cycle of continuous improvement. As your model learns and you introduce new data, you’ll need to revisit and refine your dataset to keep it in top shape. This iterative process allows you to adapt to new findings and make your model smarter and more reliable over time. For example, you might discover that a certain feature isn't as predictive as you thought, or that your data labeling needs to be more specific.

Treating data preparation as an ongoing process is fundamental to building robust ML systems. Each time you refine your dataset, you’re not just cleaning up errors; you’re enhancing the raw material your model uses to learn. This commitment to iterative refinement is what separates good models from great ones and ensures your AI initiatives deliver lasting value.

What's next in data creation for machine learning?

The world of data creation is always moving forward, and the methods we use today are just the beginning. Staying aware of what's on the horizon can help you build more robust and effective ML models. One of the most significant trends is the growing reliance on synthetic data. Think of it as high-quality, artificially generated data created by algorithms to mimic the properties of a real-world dataset. This approach is a game-changer when you're dealing with sensitive information, like medical records, or when your existing data is sparse. Using synthetic data generation allows you to augment your datasets safely and train models on a wider variety of scenarios without compromising privacy.

Another key shift is the understanding that data creation isn't a one-time project. It's a continuous loop of refinement. As your models evolve and you collect more information, you'll need to revisit and improve your data preparation steps. The focus is moving toward better data quality management, ensuring compliance, and even blending real and synthetic data for the best results. This ongoing cycle of data preparation is what separates good models from great ones. Businesses that treat data as a living asset and continuously work to improve its quality will have a clear advantage. Having a streamlined platform to manage this entire lifecycle makes it much easier to adapt and stay ahead.

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Frequently asked questions

My data isn't perfect. How 'good' does it really need to be to get started?

That’s a great question, and the honest answer is that no dataset is ever truly perfect. The goal isn't perfection; it's quality and relevance. Your data should be clean enough that your model can learn the right patterns, and it must be directly related to the problem you want to solve. It's often better to start with a smaller, high-quality dataset than a massive, messy one. You can always build and improve upon it iteratively as your project progresses.

Data creation sounds like a lot of work. How much time should I expect it to take?

You're right, it is often the most time-consuming part of an AI project. It’s not uncommon for teams to spend the majority of their time preparing data rather than building models. However, the exact timeline depends on the state of your raw data and the tools you use. By automating collection and using platforms designed to streamline cleaning and labeling, you can significantly reduce this time and free up your team to focus on analysis and innovation.

I don't have much data to begin with. Can I still build a ML model?

Absolutely. A small dataset doesn't have to be a dead end. This is where techniques like data augmentation and synthetic data generation are incredibly useful. Augmentation allows you to create new data points by making small changes to your existing data, like rotating images or rephrasing sentences. Synthetic data goes a step further by creating entirely new, artificial data that follows the statistical rules of your original set. Both are excellent strategies for expanding your dataset to train a more robust model.

What's the real difference between web scraping and using an API for data collection?

Think of it this way: using an API is like ordering from a restaurant's menu. You make a specific request, and the kitchen sends you a well-prepared, structured dish. It's the official, reliable way to get data. Web scraping is more like going into the kitchen yourself to gather ingredients. You can get what you need, but it can be messy, the structure might be inconsistent, and you have to be careful to respect the owner's rules. When an API is available, it's almost always the better choice.

Do I need a dedicated data science team just to prepare my data?

Not necessarily. While data science expertise is always valuable, you don't always need a full team just for data preparation, especially at the start. Many modern tools and platforms are designed to make data cleaning, labeling, and versioning more accessible to developers and technical teams. These systems can automate many of the most tedious tasks, allowing a smaller team—or even a single person—to build a high-quality dataset efficiently.