The Machine Learning Workflow: From Data to Model Deployment

The global machine learning (ML) market is experiencing rapid growth. According to Statista, it will reach USD 113.11 billion in 2025 and is expected to reach USD 503.41 billion by 2030, with an annual growth rate (CAGR) of 34.80% from 2025 to 2030. This projection reflects the accelerating adoption of ML technologies, driven by advancements in artificial intelligence, big data analytics, and the increasing demand for intelligent automation and data-driven decision-making. As businesses and industries increasingly utilize ML functionality in markets ranging from banking to healthcare, understanding the subtleties of ML processes and how they work becomes more crucial.

In this blog post, we will explore the principal steps of machine learning development, its specifics, and its sequence — from issue formulation to the deployment of the final model.

What is Machine Learning?

ML is a branch of artificial intelligence (AI) that allows computers to automatically learn from data, improve their performance with the elapse of time, and make predictions or decisions without needing to be explicitly programmed. However, for an ML model to succeed, it must go through a formal development process. Every step — from problem formulation to the deployment of the ultimate model — has its role in guaranteeing the system's accuracy, efficiency, and usability in practice.

Machine Learning workflow steps

A successful and streamlined ML process starts with clearly understanding the steps involved. A standardized workflow helps ensure clarity, focus, well-defined roles, responsibilities, and project goals. A disciplined approach maximizes a model's value and increases the chances of success. Stages can slightly vary, but to emphasize the most important ones, they encompass:

1. Data Collection & Preprocessing

Data collection and processing are essential parts of the ML process. They will determine how precise and effective the model will be. The data collection and processing stage consists of several interconnected components that ensure the data is correct for proper analysis and modeling. All components are crucial to strengthening the quality and relevance of the ML model in general: 

a. Data collection 

The first step in the process is data collection, which starts with determining clear goals for the ML project and having straightforward questions to be answered and issues to be addressed to help identify the most relevant data. A defined goal gives a sense of direction to the data collection process. 

Identification of data sources 

After setting objectives, the next important step is identifying suitable data sources. The choice of data sources may vary depending on the nature of the project, but the most widely used data sources are:

• Public datasets: Websites such as Kaggle and UCI Machine Learning Repository offer many datasets that can be used to work on various ML projects. 

• APIs: Many organizations provide APIs that facilitate programmatic access to their data, which can be invaluable for gathering real-time information. 

• Web scraping: Tools such as Beautiful Soup or Scrapy can extract data from websites using techniques such as web scraping. 

• Internal databases: Organizations can utilize data in internal databases by taking advantage of available resources and historical data. 

• Surveys and interviews: Collecting information directly from the users or the experts through surveys or interviews may provide unique insights relevant to the project requirements. 

Assessment of data quality 

Before collecting the data, it is necessary to assess the quality. Some key factors to consider are:

• Accuracy: Ensure the data is free from errors or consistent. 

• Completeness: Ensure the dataset contains all the required variables and records. 

• Consistency: Verifying whether data values agree within or across sources or periods.

• Relevance: Ensuring the data fits the project goal and possesses the information necessary to answer the questions. 

• Timeliness: Ensuring the data is recent and valid for the sought-after analysis. 

b. Data processing

Data processing follows data collection and involves preparing the data for training the model. It consists of numerous crucial processes: 

Data cleaning

This step processes the missing data, identifies the features that contain the missing values, and selects appropriate imputation methods by using the average (mean), the middle value (median), the most common value (mode), or by predicting the missing values using predictive imputation.

This stage is also crucial because it identifies and manages outliers. Here, statistical methods such as box plots, Z-scores, or IQRs (interquartile range) are used to identify outliers. Depending on the study, they can be excluded, limited, or transformed to minimize their impact on analysis and model performance. 

Data transformation 

Data transformation ensures that the data obtained is in an easily handled format to analyze. It may include:

• Normalization includes scaling numerical features using methods such as Z-score normalization (standardization), min-max scaling (rescaling features between 0-1), or robust scaling (which uses median and interquartile range to reduce the sensitivity to outliers).

• Encoding categorical features is done by converting such features into a numerical form that machine learning algorithms can process. This can be achieved using methods such as one-hot encoding (creating binary columns for each category), ordinal encoding (assigning ordered numerical values to categories), label encoding (assigning a unique integer to each category), etc. 

Data augmentation

Data augmentation techniques can be used to increase the size and diversity of a data set. This is especially useful when data is limited, such as in image or text classification tasks. Generating additional data points from existing data can make the model more reliable and generalizable.  

In summary, the data collection and processing phase is crucial for laying the groundwork for successful machine learning projects. By focusing on clear objectives, identifying quality data sources, and implementing robust processing techniques, you can significantly enhance the performance and reliability of your ML models. 

2. Model Selection, Training & Validation

Model training, testing, and evaluation are the next steps in the machine learning process, which focuses on developing an accurate model to solve a specific problem. This stage involves selecting a suitable algorithm, training the model on the prepared data, and thoroughly evaluating its performance.

Choosing a model 

The first step is to understand the nature of your problem. Is it about sorting things into categories (classification), predicting numbers (regression), or grouping similar items (clustering)? Determining the type of issue helps in choosing the right candidate models. Using your domain knowledge, you can identify proven effective models for similar tasks. The most common options include:

• Linear models for more straightforward relationships.

• Tree models display non-linear relationships when data doesn't follow a clear pattern.

• Neural networks for complex patterns in large data sets such as images, speech, and unstructured text data.

It is essential to consider the trade-off between model complexity and interpretability. More complex models (like deep learning) may yield higher accuracy but are less interpretable, while simpler models (like tree models) are easier to understand.

Thus, a "good enough" model can refer to many things and is specific to your project, such as:

• A model that meets the requirements of the project stakeholders. 

• A model that is reasonably effective given the time and resources available. 

• A model that is effective compared to naive models. 

• A model that is effective compared to other tested models. 

• A model that is effective compared to state-of-the-art models. 

Training the model 

Once a model is selected, it is time to start training it. The first step involves preparing the appropriate algorithms for the task at hand. Popular machine learning libraries such as scikit-learn, TensorFlow, and PyTorch provide built-in functions for various algorithms. While these libraries simplify the process, it is still essential to understand the processes behind them.

Next, it is necessary to set up the model with training data that is ready to use so that the model can learn from the examples provided. Training the machine language model provides the ML algorithm with data that will allow it to identify and discover good values for all the attributes. There are several machine learning models, most commonly supervised and unsupervised learning. 

Supervised learning is possible when training data contains input and output values. Each data set with input and target output is known as a training signal. Training is based on the discrepancy between the processed and captured results when input data is presented to the model. 

Unsupervised learning is applied to discover patterns in the data. Additional data is used to find out matches for the patterns or clusters. It is also an iterative process that gets more refined with correlation against expected patterns or clusters. There is no initial reference dataset in this method. 

In our previous blog post, we explained the types of machine learning in detail. You can review it here. 

Testing and evaluating model performance 

After training, the model is tested on an independent test data set to evaluate its performance objectively. To properly test a model, you must provide it with data as close to real-world circumstances as possible. This will require a different data set from the one the model was trained on, preferably using completely new data for each test.  At this stage, it is necessary to input test data into the model that has actual target values so you can compare the model's predictions with the exact values. Standard evaluation metrics include:

• For classification tasks, use metrics such as precision, accuracy, recall, F1 score, ROC-AUC, etc., based on class imbalance and business objectives. 

• For regression tasks, use metrics such as mean squared error (MSE), mean absolute error (MAE), R-squared, etc. to evaluate model performance. Use appropriate validation techniques such as cross validation, training test partitioning, or time series validation (for time series data) to thoroughly investigate the model performance. 

To thoroughly assess model performance, use appropriate validation techniques such as cross-validation, training/test partitioning, or time series validation (for time-sensitive data). While testing typically refers to checking the model's performance using specific data (such as a separate test set), validation techniques help to assess how well a machine learning model generalizes to unseen data, ensuring that the model is not just memorizing the training data (which could lead to poor performance on new, real-world data).

The process of experimentation and gradual fine-tuning is essential. It's important to allocate time for model optimization to achieve the best results. Establishing success criteria at the start of this iterative cycle helps clarify when further refinement is necessary.

Depending on the model type, you can create separate test datasets to support different variables, such as location or demographics. During testing, you can change model parameters and hyperparameters based on the results, allowing you to improve the model. Hyperparameter tunings will enable you to adjust operations or parameters to control the learning process, such as the number of learning steps, to maximize model performance. 

Training, experimenting, and testing a model are essential to developing a reliable machine-learning model. By choosing the right model, training it on well-prepared data, and thoroughly validating its performance through systematic testing, you can build a solution that will accurately solve your formulated problem. This back-and-forth process enables continuous refinement and improvement, leading to more accurate and reliable machine-learning programs.

3. Model Deployment

Once a machine learning model is trained, the next step is deploying it within a business application, such as a mobile or desktop app. This involves integrating the model into existing software, ensuring it can process real-world data and make accurate predictions. Deployment isn't just about having a well-trained model—it must seamlessly integrate into the environment, scale effectively, and remain reliable over time. The ultimate goal is transitioning from an experimental model to a fully operational system that delivers consistent, real-world predictive power.

Model preparation for deployment

Before the model can be deployed, it must be exported in a form that allows compatibility across different environments. Most machine learning frameworks, such as Scikit-learn, TensorFlow, and PyTorch, offer multiple methods of serializing models.

For example, Scikit-learn models are typically stored in Python Pickle (.pkl) format, while deep learning models are stored in SavedModel for TensorFlow or Script Module for PyTorch. If the model needs to be deployed in other frameworks, ONNX (Open Neural Network Exchange) is a handy format because it enables systems to interoperate.

Serialization ensures that the model's learned parameters can be stored and deployed to a production system without retraining.

Here are some popular tools for deployment:

• Kubernetes is a container orchestration platform that automates containerized applications' deployment, scaling, and management, offering reliability and scalability.

• TensorFlow Serving is a flexible and scalable service system that serves TensorFlow models in production. It simplifies the deployment of TensorFlow models as microservices, such as serving multiple models at once and dynamically scaling with demand.

• Kubeflow is an open-source machine learning platform on top of Kubernetes that provides tools for deploying, monitoring, and managing machine learning models in production. It simplifies running and managing machine learning models on Kubernetes.

• MLflow is an open-source end-to-end machine learning lifecycle management framework. It provides tools for experiment tracking, code packaging, and model management to enable reproducibility and collaboration on ML projects.

Choosing the right deployment environment

Once the model has been trained, approved, and prepared for deployment, the next step is determining where and how to deploy it. The deployment environment depends on whether scalability, latency, or security needs are critical. Standard deployment methods include:

• Cloud deployment: most companies use cloud-based setups such as AWS, Google Cloud, or Microsoft Azure, which provide good infrastructure and built-in tools to host machine learning models. Cloud deployments are highly scalable, so the model can handle many queries without affecting performance.

• On-premises deployment: some companies prefer hosting models on their servers for sensitive data or regulatory issues. It provides complete control but requires dedicated resources for the hardware and software.

• Edge deployment: for real-time predictions with low latency — for instance, in autonomous cars, IoT devices, or mobile applications — models can be deployed directly to edge devices. In this case, the model runs on the device itself instead of relying on cloud servers to deliver output.

Managing scalability and performance

As usage grows, the model must be able to handle lots of queries. Performance optimization involves a variety of techniques:

• Load distribution: incoming queries are split across several model instances to prevent bottlenecks.

• Real-time vs. batch output: some applications, like fraud detection, require real-time predictions, while others, such as customer segmentation, can settle for batched data.

• Model acceleration: techniques like quantization (reducing the accuracy of model weights) and pruning (removing redundant relationships) can improve inference speed, especially on edge devices.

• Scalable infrastructure: cloud services can dynamically allocate resources based on demand.

• Hardware acceleration: accelerators like GPUs or TPUs, caching, and pre-computation techniques can reduce response time (latency).

Monitoring and maintenance

Implementing a model doesn't mean the task is completed; it is just a part of an ongoing process. Patterns in data change over time, and a previously correct model can start making incorrect predictions. To prevent this, ongoing monitoring is needed.

Tracking key metrics such as accuracy, latency, and error rate helps ensure the model performs as expected.

If the data the model encounters during deployment drastically differs from the training data, its predictions will no longer be reliable. Automatic alerts can notify engineers that the model needs to be retrained.

Machine learning models will need to be updated periodically to stay current. There are several approaches to updating models:

• Periodic retraining, wherein some models are periodically retrained with the freshest data.

• On-demand retraining, wherein one retraining cycle can be triggered when a reduction in accuracy is detected.

• Cyclical updates — instead of replacing the model entirely, the new model is rolled out piece by piece so that teams can benchmark against performance before the full transition.

A/B testing is widely used to compare model versions in production before full deployment.  It works in the following way: instead of releasing a new version of the model all at once, companies can release it to a small group of users first to compare performance and fix bugs.

Security and Compliance

With the world experiencing the development and progress of various fraud schemes, protection against malicious attacks is crucial. Machine learning models are vulnerable to attacks when hackers alter the input data to produce misleading predictions. 

Deploying a machine learning model is not only coding but also ensuring the model is reliable in production. A deployed model must be scalable, efficient, monitorable, and secure. Security is critical, especially if the model is accessible via an API.  An API allows external systems or users to interact with it over the internet, which means it needs to be protected against misuse.

Authentication and encryption are critically essential here. Authentication methods like OAuth ensure that only authorized users or systems can interact with the model, while encryption through HTTPS protects data as it is transmitted over the internet.

Depending on the industry, the deployed models must comply with legal regulations such as GDPR (for handling personal data) or HIPAA (for healthcare purposes). 

Version control is also essential — every version of the model should be logged appropriately so that teams can roll back to previous versions if necessary.

With best practices in serialization, API integration, scalability, monitoring, security, and versioning, organizations can ensure the maximum value of their machine-learning solutions.

Conclusion

A well-structured machine learning workflow is essential to building effective models. It starts with defining the problem, preparing the data, developing useful features, selecting the right model, optimizing its performance through training and evaluation, and finally deploying it in production systems. Each step is crucial to ensure the model is accurate, reliable, and can deliver real value. Since machine learning is an iterative process, continuous improvement based on evaluation results helps to improve model accuracy over time. As machine learning continues to evolve, implementing and following structured workflows enables organizations to build scalable, high-performance machine learning models that provide meaningful results.