Whenever we discuss personalized Netflix recommendations, self-driving cars, fraud prevention in banks, or predictive analytics, we are essentially talking about technologies that are powered by machine learning. These advanced models quietly influence our daily lives, from providing weather forecasts based on historical data to offering personalized music suggestions during our daily commute. But how exactly do they work and learn?
In this interview with Oleksandr Boiko, our Director of Engineering, we’ll get down to the basics of machine learning. He’ll explain what machine learning models are, describe how they’re used in the real world, and even walk us through the step-by-step process of training one from scratch.
Can you explain what a machine learning model is in simple terms?
Basically, a machine learning (ML) model is a program that has been trained to recognize patterns in data. It infers generalized knowledge from specific examples that were previously processed to understand the relationships between things. For example, by seeing many pictures of cats, the model can learn to recognize what a cat is.
As with any learning process, the ML model needs good quality “study materials” (data). When someone creates a new ML model, they usually aren’t inventing new core algorithms for these models as these already exist; instead, they focus on preparing data. This process involves creating various datasets (a collection of data) for training, evaluation, and testing purposes. To be effective, these datasets must be balanced, free of outliers, and representative of the overall scenario.
During training, a ML model essentially learns to solve a complex system of equations with many variables. The model analyzes the data and learns the patterns and relationships between things. This new knowledge allows it to make predictions for new scenarios.
What are the different types of ML models, and how do they differ from each other?
There are actually a few different ways to categorize ML models, but let’s group them based on how they learn:
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Supervised learning. These models learn from labeled data, where each example in the training set has a corresponding correct answer. Supervised learning is great for tasks like predicting house prices or classifying emails as spam or not spam. Models like Linear Regression, Logistic Regression, Decision Trees, Random Forests, Support Vector Machines, and Neural Networks fall into this category.
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Unsupervised learning. Here, the model analyzes data without any labels and tries to find hidden patterns. It’s useful for things like customer segmentation, where you might uncover groups of customers with similar buying habits, or anomaly detection. K-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), and t-SNE are popular examples of unsupervised learning.
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Semi-supervised learning is, in fact, a combination of supervised and unsupervised learning. The model gets a few labeled examples but also processes a lot of unlabeled data to fill in the gaps. It’s handy for tasks like improving speech recognition or classifying images when there’s not enough labeled data. Vector Machines and Co-training techniques are used in this approach.
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Reinforcement learning. Unlike the other groups, reinforcement learning involves the model interacting with its environment and learning through trial and error. It gets rewards for good decisions and penalties for bad ones. Reinforcement learning is used in game-playing (like AlphaGo) and robot control, where the robot learns through exploration and rewards. Techniques such as Q-Learning, Deep Q-Networks, and Proximal Policy Optimization are commonly implemented here.
As you can see, these types differ mainly in the data they use, the learning approach (guided or exploratory), and the kinds of tasks they’re suited for. Supervised learning needs clear instructions (labeled data), while unsupervised models have to figure things out themselves using unlabeled data. Semi-supervised learning uses both and reinforcement learning needs interaction with the environment.
What are the key steps involved in building a ML model from scratch?
Although it is a complex task, building your own ML model usually boils down to 9 key steps:
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Defining the problem. The first step is figuring out what you want your model to do. Is it predicting house prices, classifying emails as spam, or something else? Knowing the goal will guide the whole process.
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Gathering and preparing data. Every model requires information for training. You can collect data yourself, use public datasets, or collaborate with data scientists. Once you have it, you need to clean it up to make sure there are no typos, duplicates, or missing values. Data preparation also involves creating new features, standardizing the data, and encoding categorical variables the model can understand. Finally, you need to split your data into training, validation, and testing sets. Training data teaches your model, validation data helps fine-tune it, and testing data assesses its performance on unseen information.
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Selecting a model. Different ML models have different strengths. You need to consider the problem you want to solve and choose the most suitable model for the task. For example, you might use Linear Regression for regression problems or Random Forest for classification tasks.
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Training the model. Use your training data to feed the model, adjusting parameters to learn patterns. Techniques like cross-validation and grid search for hyperparameter tuning are often used here.
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Evaluating the model. At this stage, you need to assess the model’s performance using the validation set. Metrics like accuracy, precision, recall, and F1 score will help you to measure how well it predicts, while tools like Confusion Matrices, ROC curves, and AUC come in handy to understand the model’s strengths and weaknesses. This stage is crucial for fine-tuning the model before it’s used for real-world cases.
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Tuning hyperparameters. This step is about optimizing the model’s parameters to improve its performance. Methods like grid search, random search, and Bayesian optimization can be used to find the best set of parameters.
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Validating the model. Once the model performs well on the validation set, it’s time to use a completely new dataset (the test set) to see how well the model performs on unseen information. This helps us to check the model’s generalization ability. Just like with the validation set, we use metrics like accuracy, precision, recall, and F1 score to assess its performance on this new data.
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Deploying the model. If the model performs well, it’s time to put it to use in the real world. Integrate the model into a production environment where it can start making predictions on new data.
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Monitoring and maintaining the model. After deployment, you need to monitor the model’s performance over time and make sure it’s still accurate. New data might become available, so you may need to retrain the model to keep it up-to-date.
What are the biggest challenges teams face when developing ML models?
It’s definitely data. Having good data is key to building a good model. The biggest challenge often lies in obtaining the right data: relevant, clean, organized, and unbiased. Without this foundation, the model’s results can be unreliable.
Another challenge is training complex models. These models often require massive amounts of data (gigabytes or even terabytes!). Processing all that information demands powerful computing resources like high-performance CPUs or GPUs. Luckily, there are techniques to speed things up, but the need for substantial resources remains a significant issue.
Finally, deployment can be tricky as well. Once your model is trained, it needs a home (deployment environment) to operate. The deployment strategies vary widely based on business requirements and technical considerations of the model itself.
Some companies might be okay with using a third-party service, while others, especially those dealing with sensitive data, might prefer to have everything in-house. Additionally, certain models require special hardware like GPU for inference, while others can run inference on a regular CPU.
Ultimately, the decision on deployment strategy is typically made early in the development cycle, guided by factors such as data security, operational costs, scalability requirements, and compliance obligations. Each approach presents its pros and cons that must be carefully evaluated to ensure the model’s successful integration into production environments.
How can businesses benefit from implementing ML models?
We’ve entered the AI era, and there’s no turning back. ML models are continually improving and each new version is significantly better than the last. Even before tools like ChatGPT became popular, we had developed ML models that helped businesses work more efficiently.
For instance, models have been used to predict rental property prices, measure market reactions to new or discontinued products, and categorize information to provide valuable insights, like identifying the most popular brands of cosmetics in the US.
The benefits of AI models now go far beyond these initial use cases. With AI LLM models, businesses can automate decision-making, reduce spending on tech support, provide real-time advice, and facilitate knowledge generalization and sharing.
What role do you think machine learning will play in the future?
Machine learning is already impacting almost every aspect of our lives, often in ways we don’t even notice. For example, technologies like FaceID use ML to recognize faces and provide security features.
Looking ahead, I think we’ll see even smarter devices in our homes. Imagine having a fridge with an internal camera that automatically orders groceries when supplies run low, tailored to your diet and preferences. We’re heading towards a future where our home appliances will be incredibly intuitive and proactive.
In many ways, the future is already here. ML models can already write songs, poems, and music, and create art. So, what’s next? I believe we’re getting close to a point where large language models (LLMs) might become collectively smarter than all the people on Earth. These models could start improving themselves, which is both exciting and a bit intimidating. I think one of the major challenges will be ensuring we maintain control over these advanced models. We need to develop robust patterns, guardrails, and safety mechanisms to prevent scenarios like Skynet from the Terminator movies.
Bottom line
Machine learning is no longer a futuristic concept, it’s the technology that is transforming businesses and shaping our daily lives. By predicting market trends, automating tasks, recommending movies, and personalizing customer experiences, ML already enhances our lives in myriad ways. And as we look into the future, the potential of machine learning will only continue to expand, offering new possibilities for growth and efficiency.