Training AI Models: Practice of Creation and Deployment

Training process

Training needs analysis

If you have specific requirements regarding the training programme, we will carry out a training needs analysis for you. This will guide us on which aspects of the programme should receive greater emphasis, so that the training programme meets your specific needs.

What will you gain?

Better data preparation - You will learn how to organize data from multiple sources, fix missing values and errors, and apply transformations that measurably improve training quality and model stability.

Effective data analysis - You will master EDA with charts, heatmaps, and dependency analysis, so you can spot trends, correlations, anomalies, and hidden issues before they damage model performance.

Right algorithm selection - You will learn how to match the model to the problem type and data characteristics, so you avoid random trial and error and build solutions that make technical and business sense.

Hands-on model training - You will practice implementing and training models in popular tools, including data splitting, validation, and learning settings that directly affect the final performance.

Reliable quality assessment - You will learn to interpret accuracy, precision, recall, F1, confusion matrix, and ROC AUC, so you can evaluate models properly and make decisions based on the right metrics.

Optimization with purpose - You will explore hyperparameter tuning, feature selection, and dimensionality reduction, helping you improve model performance while reducing overfitting and underfitting risk.

Deployment readiness - You will see how to expose a model through APIs and containers, integrate it with business systems, and prepare a solution for scalable, real-world production use.

Post-deployment control - You will learn how to monitor model performance, detect data drift and concept drift, and plan retraining cycles so your model keeps delivering quality after deployment.

Training programme

1. Introduction to Training AI Models

the role of data in the machine learning process model lifecycle: from data preparation to deployment the most common challenges in practice.

2. Data collection and gathering

acquiring data from various sources (internal, external, API) organization and storage of data (SQL/NoSQL databases, data lakes) security and legal aspects of data.

3. Data preparation and transformation

cleaning and filling in missing data,
transformations: normalization, standardization, encoding categorical variables feature engineering as the key to better models.

4. Exploratory Data Analysis (EDA)

data visualization: charts, diagrams, heatmaps identification of trends, relationships and correlations detection of anomalies and outlying values (outliers).

5. Building and selecting an AI model

overview of the most commonly used algorithms (regression, trees, neural networks) selection of the algorithm for the type of problem and the nature of the data implementation of the model in practice (Python, R, ML.NET).

6. Training process

data split: training, validation, test set validation techniques: cross-validation and its variants management of the learning process (epochs, batch size, learning rate).

7. Validation and quality assessment of models

quality metrics: accuracy, precision, recall, F1-score confusion matrix and interpretation of results analysis of ROC curves and AUC in model evaluation.

8. Model optimization

hyperparameter tuning: grid search, random search, bayesian optimization dimensionality reduction and feature selection (PCA, feature selection) prevention of overfitting and underfitting.

9. Deploying the model in practice

integration with business and production systems API and containerization (e.g. Docker) as a way of making models available issues of scalability and performance in the production environment.

10. Monitoring and maintenance of models

tracking model performance in real time identification of data drift (data drift, concept drift) updating, retraining and cyclic improvement of the model.

What are the prerequisites for participating in the training?

Data analysis basics - You should be comfortable reading data tables, understanding variable types, and spotting basic relationships in datasets to work smoothly with analysis and data preparation.

Basic machine learning knowledge - You should know core terms such as model, feature, label, training, and validation so you can quickly follow topics related to algorithm choice and result evaluation.

Programming basics - You should be able to read and modify simple code in Python, R, or a similar language, because the training includes hands-on implementation and tool-based work.

Data handling awareness - You should have basic experience working with data files or databases so you can more easily understand data collection, storage, and preparation for AI models.