Machine learning: its impact on processing large volumes of data 

The growth that machine learning has been experiencing in recent years is giving rise to the development of a series of Tools that transform markets and industries. Especially for sectors that manage large volumes of data, such as healthcare, financial services, telecommunications and retail.

Automation optimizes and enhances processes, making them much more efficient, and producing a positive impact in terms of processing times and resource utilization. 

It is therefore essential to understand what machine learning is, how it impacts companies and their customers, and what benefits it produces.

By reading this article you will be able to learn about the key components of machine learning and its development phases. We will also explain its main challenges and the emerging trends for big data. 

In addition, we will discuss how to evaluate and monitor machine learning models in production, and how to prevent financial fraud with this technology.

What is machine learning?

Machine learning is crucial in the management of big data, as it allows organizations analyze, interpret and extract value from them in an automated and efficient way.

Its impact is especially significant in the context of big data, where traditional analysis methods become insufficient due to the complexity and magnitude of the information to be managed. 

By applying machine learning, it is possible to identify hidden patterns, make accurate predictions and make informed decisions in real time. This not only optimizes processes and reduces costs, but also offers a competitive advantage in an environment where data is a key strategic asset.

Machine learning makes it possible to process millions of data in fractions of a second and create predictive models to make better decisions. 

These models learn progressively, and when additional information is incorporated, they analyze it based on the data they already have loaded, providing answers and generating results.

The model is trained with data and gives results when tested., showing a percentage of hits and misses. From there, it can be retrained to adjust the percentages and determine its viability and performance.

One of the areas where machine learning is being used the most is medicine. For example, from the records of patients with heart failure, the model learns and points out that a particular person, whose data is entered into the system, may be prone to experiencing this type of health problem.

Machine learning is also being used in credit risk to determine whether or not a person can be granted credit based on their characteristics. 

The model is trained by having information from thousands of clients who have received a loan and the risk that each of these people had. Based on this data, it will be known whether a new client may constitute a risk for the company in terms of default or delays in payments.

Difference between machine learning, artificial intelligence and deep learning

Artificial intelligence is a field of computer science that develops systems capable of performing tasks that normally require human intelligence. 

It includes capabilities such as reasoning, learning, perception, natural language processing, pattern recognition, and decision making. 

Machine learning is a branch of artificial intelligence which focuses on the development of algorithms and modelsThese allow machines to learn from data and improve their performance, without being explicitly programmed for each task. They are systems that identify patterns, make predictions and take decisions based on the information provided, adapting as they receive more data.

Within machine learning, Deep learning is a specialized area that uses deep neural networks, inspired by the structure of the human brain. 

It focuses on the processing large amounts of data and performing complex tasks, such as image recognition or natural language understanding. 

While AI covers the entire spectrum of simulated intelligence, machine learning and deep learning are specific techniques within this field, aimed at Achieving intelligence in an automated and scalable way.

Key components of machine learning

The success of a machine learning system depends on proper data preparation, the choice and tuning of the appropriate model or algorithm, and a rigorous training and validation process to ensure that the model is robust and capable of generalizing appropriately.

Data is the fundamental basis of any machine learning system. Its quality and quantity directly affect the accuracy and efficiency of the models, which need to have clean, relevant and well-structured data. 

Data preparation includes processes such as data collection, data cleaning, data transformation, and data normalization. This involves removing duplicates, correcting errors, and scaling variables so that algorithms can process them properly. 

In addition, it is important to select the most relevant features to reduce noise and improve model performance.

Machine learning models and algorithms, for their part, are the tools that process data to learn patterns and make predictions. 

A machine learning algorithm is the set of instructions that trains a model from data. While the model is the end result of that training, it can make predictions or take decisions based on new data.

From Question Pro and the Association for the Advancement of Management (APD) They identify the three groups of machine learning algorithms and some models associated with each of them.

Supervised learning 

The machine is trained by example. The operator provides the machine learning algorithm with a set of known data that includes the desired inputs and outputs, and the algorithm must find a method to determine how to arrive at those inputs and outputs. 

The algorithm makes predictions and is corrected by the operator, until it reaches a high level of accuracy and performance.

Among the supervised machine learning models, the following stand out:

• Linear regression: predicts continuous numerical output in regression tasks. 
• Logistic regression: used for binary classification tasks.
• Decision trees: They build a tree-like structure, in which each node reflects a decision based on a feature, and the leaves represent a final class label or a numerical value.
• Random Forest: ensemble learning strategy that combines numerous decision trees to increase prediction accuracy and reduce overfitting.
• Support vector machines: SVM is a sophisticated algorithm that can classify binary and multilevel data.
• K-NN: Basic but effective classification and regression algorithm. 
• Naive Bayes: Probabilistic classification algorithm based on Bayes' theorem, which performs text categorization tasks such as spam detection and sentiment analysis.
• Neural networks: They are used for image classification and natural language processing.

Unsupervised learning 

The algorithm studies the data to identify patterns. There is no answer key or human operator to provide instruction. The machine determines correlations and relationships by analyzing the available data.

Among the most common models are:

• K-Means: popular clustering method that divides data into groups based on similarities. 
• Hierarchical clustering: Creates a dendrogram, a tree-like cluster structure, that can represent hierarchical relationships between data points.
• Gaussian Mixture Models (GMM): combine different Gaussian distributions to represent data. They are often used in clustering and density estimation.

Reinforcement learning

It focuses on regimented learning processes, in which automatic algorithms are provided with a set of actions, parameters and final values. 

By defining rules, the algorithm attempts to explore different options and possibilities, monitoring and evaluating each outcome to determine which is optimal.

Some popular reinforcement learning models and algorithms:

• Q-Learning: Model-free reinforcement learning algorithm that helps agents learn the best action selection policy. 
• DQN: Q-Learning extension that uses deep neural networks to approximate Q values. Effective in solving complex tasks.
• SARSA (State-Action-Reward-State-Action): Model-free reinforcement learning algorithm. It determines the best policy by estimating Q values for state-action pairs and employing policy modifications.

Meanwhile, the training process of a machine learning model involves Feed the model a set of data, so that it learns to predict the desired outcomes. 

During training, the model adjusts its internal parameters to minimize the error between its predictions and the actual values. It uses techniques such as backpropagation in neural networks or gradient descent in linear regression.

Once trained, the model is validated using a separate data set that was not used during training. 

This validation process helps to evaluate the model's ability to incorporate new data. For this purpose, it is common to use techniques such as cross-validation, in order to ensure that the model is not overfitting to the training data, which could result in poor performance on real data.

Machine learning model development process

Machine learning involves a structured cycle involving several critical stages to build, deploy, and maintain models that solve specific problems:

1) Identification and understanding of the problem to be solved. It involves clearly defining the project objectives, such as improving the accuracy of predictions, automating a process, or discovering patterns in data.

2) Establishing specific project objectives, which may include desired performance metrics (such as precision, recall, etc.), expected outcomes, and constraints (time, resources, etc.).

3) Collection of relevant data from various sources, such as databases, CSV files, APIs, sensors, etc. The quality and quantity of the data are critical to the success of the model.

4) Data preparation: includes the elimination of duplicates, normalization or standardization and feature engineering to improve the model's capacity.

5) Selecting the right models and the algorithms that best adapt to each problem. 

6) Training the selected model, using a data set to adjust its internal parameters and minimize the error in the predictions.

7) After training, a validation data set is used to evaluate the model performance. 

8) Once the model is validated, it is implemented in a production environment. 

9) After implementation, it is crucial Continuously monitor model performance in production. 

This includes monitoring performance metrics, detecting data drift, and retraining the model if performance deterioration is observed. Monitoring allows you to react quickly to changes in data or business requirements.

This cycle can be repeated several times to continually adjust and improve the model, ensuring that it remains relevant and effective in solving the problem at hand.

How to evaluate and monitor machine learning models in production?

Once operational, evaluation and monitoring of machine learning models is crucial to ensure that they continue to deliver adequate performance and meet established objectives. 

The key is to store predictions and monitor performance in real time, observing abnormal distributions and establishing triggers for retraining based on significant changes in the data or in the model's performance.

The evaluation begins with the measurement of key metrics, which should be reviewed periodically. It is also important to perform Stress testing to evaluate how the model responds to atypical inputs or changes in operating conditions.

Continuous monitoring is essential for Identify problems in real time and make adjustments when necessaryThis involves implementing alert systems that notify if model performance falls below a predefined threshold or if anomalies in predictions are detected.

In addition, monitoring should include the collection of new data and its analysis to identify the need to retrain the model with more recent data or adjust it to new conditions. 

This process ensures that the model is maintained relevant and effective in a dynamic production environment, and that any potential issues are addressed before they impact business outcomes.

Benefits of machine learning

Machine learning offers multiple advantages that can transform the way organizations operate and make decisions. 

One of the key benefits is the Improving data-driven decision making. When analyzing large volumes of data, machine learning models can identify patterns and trends that are not obvious to humans. 

This allows for more accurate and informed predictions. For example, in the financial field, credit risks or market trends can be predicted, allowing companies to adjust their strategies more quickly.

Another significant benefit is the process automation, which allows for the optimization of routine operations that would otherwise require a lot of time and human resources. From the automation of customer service tasks using chatbots, to the automation of manufacturing and logistics processes. 

In addition, machine learning enhances the personalization of services and products, by analyzing user preferences and behaviors to offer them more relevant experiences. 

Finally, Increase efficiency and precision in various tasks, such as fraud detection, medical diagnosis, or inventory management, reducing the margin of error and improving the final results.

Machine learning challenges 

The biggest challenge of machine learning is have the data and be able to process it, ensuring that they are complete and properly structured, without errors or empty fields. For example, massive amounts of data may contain noise or inconsistencies, which complicate pre-processing and cleaning. 

The key is to keep in mind that without complete data, the model cannot be trained. 

Another of the main challenges lies in the Optimization of processing times and infrastructure resources. Two variables with a direct impact on operating costs. 

If you do data processing that doesn't perform well or has incomplete or erroneous data, it's like throwing away your invested money.

At this point it is important to note that Latency and processing speed issues can impact performance of the model. Especially in applications that require real-time responses, such as fraud detection or autonomous vehicle control. 

Minimizing latency and maximizing processing speed are essential to ensuring that machine learning models can operate effectively in scenarios where response time is critical.

Another crucial challenge is the interpretability and explainability of the models. 

As machine learning models, such as deep neural networks, become more sophisticated, It can be difficult to understand how a model arrived at a specific decision.. A situation that is problematic in areas where transparency is essential, such as in health or finance. 

Issues of bias and fairness in models are also a challenge and a growing concern. 

Models can learn and perpetuate biases that exist in training data, leading to unfair or discriminatory decisions. It is therefore essential to identify and mitigate these biases to ensure that models are fair and equitable.

Good practices in machine learning 

Among the practices that are usually recommended for managing machine learning models is the division of the data into batches of equal size, which include the input to the model. In this way, the model will then be able to make the respective interference to resume those predictions.

Depending on the amount of input data available, a certain number of predictions will be obtained. These predictions are stored in a database to later assist in decision making.

If you have a million records, you usually train the model with 80% of those records. Once it is trained, you use the other 20% to make the predictions and check if it was correct and at what percentage level.

Returning to the medical example, if you have a million records with different patient characteristics and in one column you identify whether that patient had a stroke or not, the model is trained with the 80% from that data set. And then you ask it to identify which of the remaining 20% people had a stroke. 

From this, the level of success produced by the model and its level of effectiveness can be observed. It can then be retrained with another segmentation with respect to the 80-20, and it can be trained and tested again.

Once a model has been implemented, new results are loaded into it and it is tested to see if it performs well. That is, if it achieves between 93 and 97% efficiency.

After a period of use, what is usually done is to retrain it with new data. For example, if it was trained with a million records and now there are 2 million, it is trained again with those two million.

This is how information improves over time.

Preventing financial fraud with machine learning

Credit card fraud losses worldwide are expected to reach $43 billion by 2026, according to a Nilson report.

This is just one example of the many forms of financial fraud, which not only harm organizations financially, but can also damage their reputation.

The same is true for frauds such as harvesting hacked data from the dark web to steal credit cards, or using generative AI to phish personal information and launder money between cryptocurrencies, digital wallets and fiat currencies. 

To keep up with these types of risks, financial services companies are Using artificial intelligence for fraud detection. 

As explained from NvidiaThis is because many of these digital crimes must be stopped in real time so that consumers and financial companies can block losses immediately.

For example, AI can enable businesses to predict and block fraudulent transactions before they happen, improve reporting accuracy and mitigate risks. 

However, these preventative actions can also create headaches for consumers when financial services companies' fraud models overreact and register false positives that close legitimate transactions.

However, generative AI can also be exploited by fraudsters to improve their fraud techniques, using advanced language models to create more convincing phishing emails and other criminal tactics. In other words, it can be used for fraudulent activities.

It is therefore important to take a two-way view when analysing the potential of artificial intelligence in relation to financial fraud, in order to prevent and stop criminal actions.

Big data: what are the emerging trends in machine learning?

Some of the trends that are driving the ability to process and analyze large volumes of data more effectively and efficiently, opening up new possibilities for machine learning applications, are the following:

1. AutoML (Automated Machine Learning). Facilitates the design and implementation of machine learning models by automating tasks such as feature selection, hyperparameter optimization, and choosing the most appropriate algorithm. 

It is especially useful in big data environments where the volume of data and the complexity of the models are greater.

2. Federated learning models. It allows you to train machine learning models on distributed devices (such as mobile phones) without the need to centralize data. 

This is crucial to preserve privacy and reduce bandwidth consumption. It is an important trend in big data, where the amount of data is massive and its distribution is wide.

3. Large Language Models (LLMs)Models like GPT-4 and similar are being adapted and applied to large data sets, enabling more advanced natural language processing (NLP), text generation, sentiment analysis, and the creation of more accurate recommendation systems.

4. Deep LearningDeep neural networks are evolving with new architectures that better handle large volumes of data and offer improvements in tasks such as computer vision and speech recognition.

5. Explainable AI (XAI). Approach within artificial intelligence that focuses on developing models and algorithms whose processes and decisions are transparent and understandable to humans. 

This is particularly important in critical applications, such as healthcare and finance. The trend is also reflected in machine learning models, especially in critical applications involving large volumes of data.

6. Edge Computing and MLThe combination of machine learning and edge computing makes it possible to process large volumes of data directly on the device or close to where they are generated, reducing latency and improving efficiency.

7. Hybrid models. The integration of machine learning techniques with rule-based models and other methodologies is gaining popularity. This allows organizations to take advantage of the best of both worlds, especially in big data environments where combining techniques can be more effective.

8. Augmentation of synthetic dataTo overcome the limitation of training data, synthetic data generation techniques are being used that imitate the properties of real data.

This is a valuable practice in big data, when the goal is to improve the quality and quantity of data available for model training.

9. Quantum Machine Learning (QML)It is an emerging field that combines quantum computing with machine learning techniques to improve and accelerate traditional algorithms. 

It uses quantum principles such as superposition, entanglement, and quantum interference to process information more efficiently and solve complex optimization problems. 

Although it has the potential to handle large volumes of data and develop new types of models, quantum technology still faces challenges in terms of scalability, integration with classical systems and accessibility.

10. Natural Language Processing (NLP)It focuses on the creation of models and algorithms capable of understanding, interpreting and generating human language in a sophisticated way. 

Through advanced techniques such as deep language models and architectures like Transformers, advanced NLP enables machines to perform complex tasks such as machine translation, generating coherent text, analyzing sentiment, and answering questions with a level of accuracy and fluency increasingly close to natural human language. 

Conclusion

Industries that process massive volumes of data benefit greatly from machine learning's capabilities to analyze complex patterns, predict trends, and automate processes. 

In the financial sector, for example, machine learning improves fraud detection. In the health sector, it facilitates early diagnosis of diseases and personalised treatments. 

In telecommunications, it drives efficiency in network management, and in retail, it enables highly personalized shopping experiences through advanced recommendation systems.

The implementation of machine learning in these sectors also poses challenges, such as the need for robust technological infrastructure, ethical data management, and the constant updating of models to maintain their effectiveness.

The transformative potential of machine learning is indisputable, and its integration into the management of large volumes of data will continue to be a key driver of innovation and competitiveness. 

Contact us to find out how we can help you. Help your organization turn data into valuable knowledge.