Machine learning (ML) has become one of the key elements of digital transformation in organizations worldwide. Machine learning has become a key tool in analyzing market trends and predicting business changes.
The rapid evolution of machine learning technology is driving innovation across industries, while the demand for machine learning skills is rising as organizations seek professionals capable of leveraging advanced analytical methods.
According to Gartner’s 2023 report, 70% of organizations worldwide declare using ML or plan to implement it within the next two years. The global ML market value in 2022 was estimated at 21 billion dollars, and forecasts indicate that by 2030 it will reach over 209 billion dollars, corresponding to an average annual growth rate of 38.8%.
Why do organizations invest in ML?
Companies invest in ML to gain a competitive advantage. These technologies are used in various scenarios, such as:
- Predicting customer behaviors: Churn prediction allows companies to better understand why customers leave services, offering a chance to take preventive actions. Predictions also include forecasting failures, market trends, or prices, enabling companies to better manage risk, prevent technical issues, and make strategic business decisions.
- Optimizing logistics operations: Dynamic route planning or inventory management enable cost reduction and increased efficiency. Leveraging real world data, including sensor data from supply chains and vehicles, is crucial for accurate predictions and dynamic decision-making in these scenarios.
- Dynamic pricing: Adjusting prices to changing market conditions in real-time helps increase revenues.
- Personalizing user experiences: Real-time recommendations build customer loyalty and increase engagement.
However, to succeed in ML implementation, organizations must overcome many challenges, such as integrating with existing processes or ensuring data quality. That is why following best practices in building and implementing ML models is crucial.
What is the difference: ML, AI, DL?
In the world of modern technologies, terms like machine learning (ML), artificial intelligence (AI), and deep learning (DL) often appear together, but they are not synonyms. It is worth understanding the difference between them to consciously use their potential in data analysis and business process automation.
Artificial intelligence is a broad field encompassing all technologies that allow machines to imitate human abilities, such as image recognition, decision making, or information processing. Machine learning is a subset of artificial intelligence and focuses on creating algorithms that learn from data – instead of rigidly programmed rules, these systems independently discover patterns and dependencies in huge databases.
Even deeper in this hierarchy is deep learning, which uses complex neural networks inspired by the human brain. Deep learning algorithms differ from traditional machine learning approaches by automatically extracting features from large datasets, whereas traditional machine learning relies on manual feature engineering and explicit algorithms. Thanks to deep learning algorithms, it is possible to solve complex problems such as image recognition, predictive analysis, or process automation in changing conditions. Neural networks allow processing vast amounts of data and extracting valuable information, which is used among others in face recognition, speech recognition, or product recommendations.
In summary, machine learning is a subset of artificial intelligence, and deep learning is a subset of machine learning. All these technologies, based on the use of data and algorithms, have enormous potential in data analysis, process automation, and implementing innovative business solutions.
Understanding the Business Problem
Defining the ML Project Goal
Every ML project should start with a precise definition of the business goal. Fundamental questions to ask are:
- What decisions do we want to make based on the model’s results?
- What processes do we want to optimize?
- How should the ML model contribute to achieving business goals?
- What specific desired outputs will the ML model provide and what action will it translate into?
For example, if the goal is to increase customer retention, it is worth considering how to define a customer at risk of leaving (e.g., how high the probability must be that they will cancel their subscription soon), what actions should be attempted to retain them, and when these actions should be taken.
Understanding Business Needs
Stakeholders should clearly define the problems they want to solve, while ML teams must thoroughly understand the specifics of business and technical processes, considering their potential limitations. The model use case should support making accurate business decisions at the right time and be focused on effective optimization actions.
A key condition is a deep understanding of the problem and acquiring domain knowledge. To achieve this, a data scientist should actively collaborate with stakeholders, obtaining from them the expert knowledge and know-how necessary to develop an effective ML model.
Joint work on the use case enables creating a solution that not only meets technological requirements but above all responds to real needs and business goals. A harmonious combination of business and technical perspectives is the foundation for achieving measurable benefits. This field is handled by specialists in many economic sectors, demonstrating the universality of machine learning.
KPIs and Success Metrics
A key element of every ML project is defining success indicators:
- Technical metrics: Accuracy, precision, recall, F1-score allow measuring model effectiveness from a technical perspective.
- Business metrics: For example, reducing churn rate by 10% or increasing revenues thanks to better product recommendations. Predictions made by ML models may also include forecasting failures, market trends, or product demand.
The most important element in evaluating ML-based activities is using business metrics that reflect the actual impact of the model on the organization. Classic technical metrics are undoubtedly crucial at the training, tuning, and model selection stages, as they inform about its performance under specific conditions and on selected data sets. However, their role ends at the technical aspect – they do not indicate how the model actually influences business processes and whether it supports achieving key goals.
The real success of an ML project should be assessed based on its integration with business processes. Key questions are: does the model enable better decisions, does it support optimization of specific areas, and does its use actually improve organizational results? Success metrics should be defined individually for each business issue, e.g., by comparing process results with and without using the model. This way, effectiveness evaluation focuses on the real business impact, not just technical performance.
In summary, technical metrics are a valuable tool for data scientists during model building, but business metrics ultimately decide the value of deployment in practice. Defining these metrics at the project's start helps clearly set expectations and the direction of activities.
The Role of the Data Scientist in ML Projects
The data scientist is a key figure in every machine learning project. This person is responsible for translating raw data into practical business solutions, using advanced machine learning algorithms and analytical tools.
In practice, the data scientist’s role covers the entire ML project lifecycle: from data acquisition and preparation, through analysis and exploration, to building, testing, and deploying machine learning models. Data scientists often use statistical algorithms to extract insights and build robust models. The data scientist must not only be well versed in machine learning algorithms but also be able to select appropriate methods for a specific problem, optimize model parameters, and assess their effectiveness based on test data.
After model deployment, the data scientist monitors its operation in the production environment, analyzes results, and introduces necessary corrections to ensure continuous business value. Key skills include programming abilities, understanding statistics, mathematics, and the specifics of analyzed data. Essential competencies for data scientists also include statistical analysis, data mining, and data manipulation, which are fundamental for preparing data, discovering patterns, and applying analytical techniques. Thanks to this, the data scientist becomes a link between the data world and real business needs, enabling organizations to effectively harness the potential of machine learning.
Data Preparation
Understanding Available Data
One of the first steps in every data science project is a thorough understanding of available data and the processes of its collection. It is important to identify potential inconsistencies resulting from errors in ETL processes, differences between data presentation in source systems, or simply from imperfections of data sources. Such problems may limit the possibility of fully utilizing information in models. Typical challenges include missing data, duplicates, inconsistent identifiers, unexpected or simply incorrect values in data columns.
To address these challenges, various techniques are used, such as creating dictionaries to unify values in text columns, identifying and removing outliers, or filling missing data using statistical methods, e.g., medians observed in data segments. Analyzing random variables helps to understand data distributions and manage uncertainty in the dataset. It is also crucial to fix data collection processes if problems are identified in this area.
Data Exploration
Data exploration is an essential stage in which the data scientist analyzes what the data says about the business problem. The goal is to identify indicators key to solving the problem, recognize significant patterns and dependencies, and identify segments with similar properties. Unsupervised learning algorithms, such as k-means clustering and density-based methods, are often used to group data and uncover hidden structures within unlabeled datasets. Data analysis and classification are basic data scientist skills that allow effective solving of business problems. In this phase, expert hypotheses obtained from the business are also verified, which helps check whether specialists’ assumptions are reflected in the data and support problem solving.
Such analysis helps understand which data elements are important for the problem and which should be included in the model. Data exploration combines raw information with business context, ultimately allowing transforming it into practical conclusions.
Feature Engineering
Feature engineering is an important stage that directly determines model effectiveness. It uses insights from data exploration, translating business observations and significant patterns into the language of mathematics, which is easily processed by the model. This process involves creating new features, transforming existing ones, and selecting the most important variables that best represent data in the problem context. Well-designed features allow the model to detect dependencies more effectively, improving its performance and decision-making ability. By providing relevant and informative features, the model can adjust its internal parameters more effectively during training, leading to better prediction accuracy and overall performance.
Data Splitting and Validation
Splitting data into training, validation, and test sets is the basis to avoid overfitting and ensure model reliability. Proper splitting into training, validation, and test data allows effective training and evaluation of ML models – training data constitute input data and expected outcomes used for algorithm learning, enabling it to recognize patterns and predict results based on previous examples. This process ensures that the algorithm trains models on representative data and can be properly evaluated for performance. The recommended split is 70%-15%-15%, though it may vary depending on data set size.
Building the ML Model
Methodology Selection
Before choosing an algorithm, a critical step in a data science project is selecting the appropriate methodology. It includes decisions about when the model will run, how to build the target variable, and where in the entire project the ML model will be incorporated. This stage determines project effectiveness much more than the later choice of the algorithm itself.
Selecting the methodology allows designing a comprehensive solution that addresses real business needs. The chosen methodology should enable the model to predict outcomes that are relevant to business objectives, supporting better planning and decision-making. Machine learning focuses on programming, process automation, and pattern analysis in data, enabling optimization of business solutions. It requires considering key factors such as data availability timing, when the business should make decisions, and the type of actions the model should support. Often, solutions include not only the ML model but also additional analytical tools.
Choosing Appropriate Machine Learning Algorithms
The choice of ML algorithm should be thoughtful and tailored to the project’s specifics. It is key to consider the amount of available data – some algorithms, like neural networks, require large data sets, while others, like logistic regression or decision trees, perform better with smaller data sets. The choice of the right algorithm, such as linear regression, neural networks, or classification algorithms, depends on the problem type and available data. Supervised and unsupervised learning are the main approaches in machine learning – supervised learning uses labeled data for classification and regression, while unsupervised learning is used to detect hidden patterns and structures in data without labels.
The selection of machine learning libraries—such as NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch—and the available computational resources can significantly impact the feasibility and performance of different algorithms, especially for complex models like deep learning and neural networks.
Equally important is a sensible approach: choosing an overly complicated model when a simpler solution can achieve sufficient effectiveness leads to unnecessary preparation and maintenance costs. Using a cannon to shoot a sparrow may be impressive, but rarely effective.
Additionally, machine learning operations (MLOps) practices are essential for deploying, monitoring, and maintaining models in production environments.
Explainable AI (XAI)
Explainable AI plays a key role in connecting machine learning models with business by enabling their interpretation. The significance of variables in models alone helps to better understand the issue being analyzed, such as user needs and behaviors, which translates into more accurate decisions and more effective actions. Analyzing the significance of variables often reveals key factors influencing customer decisions, supporting the optimization of products and services.
Linear models, thanks to their simplicity, offer additional value—their weights can be directly used in a production environment. An example is attribution models, where variable weights help determine which marketing channels have the greatest impact on achieving conversions.
Explainable AI not only increases transparency and trust in models, but also enables the acquisition of knowledge that is valuable in itself, regardless of the algorithm's operation. Lack of interpretability can distort results and make it difficult to understand how ML algorithms work, leading to erroneous conclusions. This makes XAI an indispensable tool for achieving business goals.
Production Model Deployment
ML Pipeline and MLOps
Effective integration of models with existing systems requires automated ML pipelines and implementing MLOps practices, such as data and code versioning. Key elements include:
- Automating data collection and processing.
- Scaling models in response to increased load.
- Versioning models to easily revert to previous versions in case of problems.
Model Monitoring and Retraining
Monitoring and retraining models in production are key activities ensuring their effectiveness in changing conditions. Models operating in production environments must be regularly checked and updated to maintain high prediction quality and achieve business goals. ML model effectiveness may change over time, so they require regular optimization.
Production Model Monitoring
- Technical Metrics
Tracking metrics such as accuracy, precision, or F1-score allows detecting performance degradation (model drift). Monitoring also enables assessing ML models' prediction effectiveness in real production conditions. This may be caused by changes in input data (data drift) or relationships between features and responses (concept drift). Automated monitoring systems allow quick response to issues. - Business Metrics
Models must be evaluated regarding impact on:
- Reducing customer churn – correctness in identifying customers at risk of leaving.
- Increasing conversion – effectiveness in boosting sales or clicks.
- Optimizing operational costs – e.g., in logistics or inventory management.
Model Retraining
Regular retraining ensures models are adapted to changing environments and fully benefit from the latest data.
- Retraining Schedule
Systematic model updates, e.g., monthly, help maintain performance, especially in fast-changing industries. - Triggered Retraining
Retraining can be triggered by events, e.g., quality drop, data distribution change, or new data relevant to the problem. - Automation Pipeline
Automated pipelines covering the process – from data collection through training and validation to deploying the new model – ensure consistency and retraining efficiency. This enables teams to quickly respond to changes and maintain high ML model quality.
Production Data Monitoring
After ML model deployment, monitoring production data is crucial because data sources and ETL processes may change. Monitoring often involves huge data volumes, requiring advanced tools and automation. Data may be presented in different structures, change format, contain new values, and availability times may differ from training assumptions. Such changes can cause discrepancies between training and production data, significantly reducing model effectiveness.
Monitoring allows detecting changes in data structure and quality, such as missing values, new categories, or feature distribution shifts. It is also key to track ETL processes, which may be sources of unexpected problems. Early detection of such anomalies enables quick actions – from adjusting processing to retraining the model.
Without proper monitoring, data may cease to meet model assumptions, leading to incorrect predictions and reduced deployment value. Therefore, continuous data control is essential to maintain quality and ML solution effectiveness.
Tools Supporting Monitoring and Retraining
Tools like MLflow (experiment tracking), Evidently AI (data and model monitoring), or Kubeflow Pipelines (process automation) support the entire model lifecycle.
Monitoring and retraining are investments in maintaining systems that provide accurate predictions and support achieving business goals.
Scalability and Performance
Using cloud or serverless solutions allows scaling models as needed. A key aspect is optimizing model response time, especially in real-time systems.
To shorten model response time, the following techniques can be applied:
- Model size reduction: Compression through pruning (removing less important parameters) or quantization (reducing weight precision).
- Caching results: Storing results for the most frequent queries to avoid repeated computations.
- Infrastructure optimization: Using hardware supporting matrix computations, such as GPU or TPU, and employing serverless services with minimal cold start latency (e.g., AWS Lambda with prewarmed instances).
- Model distillation: Creating a smaller, faster model that mimics the original's behavior.
Data Security in Machine Learning
Data security is one of the most important aspects of every machine learning project. ML models learn based on data that often contain sensitive information – from customers' personal data, through transactional data, to company operational details. Therefore, protecting this data from unauthorized access and use is absolutely crucial.
In practice, data security in machine learning is ensured through methods such as encrypting data both during transmission and storage, anonymizing sensitive data, and strict access control to data sets and models. It is also important to implement incident response procedures and conduct regular audits of systems processing data.
Thanks to these measures, organizations can harness the potential of machine learning without risking data leaks or violating user privacy. Data security is the foundation of trust in machine learning-based solutions and a condition for their effective business deployment.
Ethics in Machine Learning
Ethics in machine learning projects is an issue gaining importance along with the growing impact of artificial intelligence on daily life and business. ML models may unintentionally perpetuate biases, lead to discrimination, or violate user privacy if not properly designed and tested.
To implement machine learning ethically, it is necessary to test models on diverse data sets, monitor their operation for unwanted effects, and ensure transparency of decisions made. Respecting the right to privacy and protecting personal data at every project stage is also crucial.
Organizations should implement clear ethical procedures, regularly analyze potential risks, and engage stakeholders in assessing ML models' impact on users. This allows building machine learning-based solutions that not only bring business value but are also socially responsible and comply with applicable standards.
Common Pitfalls and Challenges
Data Quality Issues
Data quality is fundamental. Both initial data quality analysis and continuous monitoring of production data and its consistency with training data are important.
Overengineering
Avoiding overly complex models is essential. Simple solutions are often sufficient and easier to implement. Focusing on MVP (Minimum Viable Product) is important to quickly deliver business value.
Communication of Results
Model results must be presented in a way understandable to the business. Data visualization tools and techniques are crucial for effectively communicating insights and model predictions to business stakeholders. Educating stakeholders on interpreting results is necessary. Reports should be tailored to the audience and include practical recommendations. Additionally, reports should clearly present ML model predictions and their impact on business decisions.
Lack of Business Training and Education
Insufficient business engagement in model deployment causes lack of understanding of their operation and results. This may lead to poor utilization of model potential and communication problems between technical and business teams. Basic data analysis knowledge is necessary for effective collaboration with ML teams.
Model Abandonment Instead of Iteration
Models are sometimes discarded when their results become less accurate instead of analyzing and adapting them to changing data. ML model effectiveness may change over time, so regular adjustment is important. Lack of iterative approach may result in loss of business trust in ML technology and underutilization of investment.
Incorrect Use Case
Even if the model works technically correctly, its results may have no business value if they are not “actionable” – i.e., do not provide information on which decisions can be made. This usually results from improper goal definition at the planning stage. Clearly defining what predictions the model should make – for example, failure forecasts, market trends, or price changes – is key to its business value.
Focus on Technical Metrics Instead of Business Value
Excessive focus on metrics such as accuracy or F1-score may distract attention from the actual model impact on key business indicators, e.g., conversion, revenue, or cost reduction. The ultimate goal of machine learning models is accurate predictions that translate into real business benefits.
Data Leakage
Omitting analysis of data availability over time may lead to situations where future or unavailable-at-prediction-time data influence model training. This results in artificially inflated effectiveness and incorrect production predictions.
Non-representative Validation
Improper selection of validation data sample may lead to inadequate results. A sample that does not reflect the real long-term distribution of production data inflates model effectiveness evaluation. Only properly selected training and validation data allow reliable assessment of ML model effectiveness. As a result, a model that performs well during tests in a narrow data range may not work correctly in real conditions, leading to incorrect predictions and business decisions.
Case Studies
ML Implementation for an E-learning Industry Client
For our client, the Alterdata team implemented an ML model supporting user engagement and motivation. Key activities included analyzing factors influencing student activity. Thanks to data analysis and classification, effective prediction of user engagement was possible. Additionally, user segmentation and implementation of the XGBoost model in BigQueryML were conducted. Model integration with data allowed precise matching of educational recommendations and analytics automation.
Similar machine learning models, based on neural networks and deep learning inspired by the human brain, are used in image recognition, speech recognition, or face recognition, confirming the wide range of artificial intelligence applications.
Effects: 80% accuracy in predicting engagement, increased user retention, and simplified data management. The example shows how ML can support experience personalization and platform development.
Machine learning finds application in many fields, confirming the broad spectrum of practical uses of this technology.
Summary of Key Steps
Successful ML model deployment requires full understanding of the business problem, ensuring data quality, choosing the right methodology, and integration with organizational processes. Every stage, from data exploration to production model monitoring, is fundamental to the final outcome. Effective ML model deployment enables accurate predictions and better business decisions.
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