Plan, predict, and eliminate risk with ML algorithms
We create Machine Learning models that learn by analyzing data and their own performance to forecast trends, optimize processes, and personalize user experiences.
Let’s talkWe drive the success of leaders:
Machine Learning allows organizations to respond to events faster and more effectively.
Use algorithms to analyze data and make decisions based on insightful and personalized recommendations, without the need for advanced technical knowledge.
Efficient decision-making
Machine learning algorithms enable data-driven management based on reliable data and predictions. They provide a comprehensive view of the business and support agile responses to changes.
Personalization of customer experiences
Segmentation powered by machine learning helps companies tailor content across platforms like stores and games to meet users’ needs. This improves customer satisfaction, enhances the user experience (UX) and customer experience (CX), and boosts sales performance with targeted content.
Better marketing and sales
Machine Learning (ML) algorithms predict advertising outcomes and optimize messaging for targeted demographics, helping businesses improve engagement and maximize campaign success.
Maximizing revenue
Demand forecasting with machine learning leverages trends and seasonality to manage warehouse efficiency, meet customer needs, and prevent overstock or understock situations.
Predict failures and downtime
Using ML consulting services, production systems can monitor data from sensors to detect potential failures and identify optimal maintenance times, improving operations.
Forcast of key metrics
Machine learning consulting supports KPI prediction to evaluate business strategies and optimize resource allocation, driving efficiency and growth for clients.
Discover even more of the possibilities of ML
Create an ML model with a partner who understands business
At every stage of building Machine Learning solutions, we ensure that the solution supports better business management and removes guesswork from your decision-making processes.
We have designed the process of building ML models to ensure that it addresses real business problems.
We analyze the problem and propose a solution.
We identify business challenges and their context, and define what the future scenario of using the ML model will look like. We propose a solution that genuinely addresses the company’s challenges.
We prepare data for model training
We collect data, clean it, and explore it. We prepare the features for the machine learning model.
We create, train, and develop the ML model.
We select algorithms and “train” them on the training data. We evaluate them by measuring accuracy, precision, sensitivity, and other metrics, and then choose the best model for deployment.
We deploy and orchestrate the model.
We prepare the production environment and deploy the machine learning model. We conduct orchestration to ensure efficiency and consistency throughout the model’s lifecycle.
We monitor the effectiveness, update, and improve.
We establish monitoring metrics, collect data, and ensure consistent performance. We verify that the model is being used as designed and ensure that no changes have occurred that could decrease its performance. We implement updates and improvements.
We implement automated CI/CD management.
We implement a process that includes integration configuration (CI), automated testing, building and integrating the model, deployment configuration (CD), post-deployment monitoring, and continuous improvement.
See how to build a data-driven company.
Predict events before they happen
Machine Learning addresses the key challenges of your business.
You want to make better decisions
ML analyzes vast amounts of data and helps identify patterns, allowing businesses to predict future trends and outcomes.
You want to reduce the risk
Quantifying the probability of business risks materializing (e.g. non-payment of receivables, damage during transport) allows for optimizing activities.
You want to reduce customer churn
Detecting engagement drops using ML consulting services, enabling proactive actions to retain customers.
You want to increase the efficiency of models
Optimize machine learning algorithms to handle large datasets efficiently, ensuring faster processing and improved performance.
You want to allocate the budget more efficiently
ML consulting predicts campaign effectiveness and identifies profitable channels, which increases marketing strategies.
You aim to personalize offers
Analyze customer behaviors and preferences to deliver personalized recommendations, boosting satisfaction and driving sales.
Detecting fraud and abuse
Identifying and blocking malicious bots to protect platform resources and eliminate fraud.
You intend to test strategies
Simulators allow testing sales and marketing strategies to predict their potential effects before implementation.
Discover our clients’ success stories
How data-driven advertising management helped an AMS agency maintain its leading position.
For the AMS team, we created a reliable and user-friendly ecosystem by integrating key data from external providers, including traffic measurements from mobile devices.
Thanks to the solutions offered by Alterdata, AMS was able to provide clients with access to key metrics, giving them greater control over campaigns and optimization of advertising spend.
Implementation of Business Intelligence and integration of distributed databases in PŚO
For Polish Open FIber, we built an advanced Data Hub architecture based on an efficient and scalable Google Cloud ecosystem. We implemented Power BI as a Business Analytics tool and also trained its users.
This improved data availability and accelerated the creation of interactive reports and dashboards.
Tech stack: the foundation of our work
Discover the tools and technologies that power the solutions created by Alterdata.
Google Cloud Storage enables data storage in the cloud and provides high performance, offering flexible management of large datasets. It ensures easy data access and supports advanced analytics.
Azure Data Lake Storage is a service for storing and analyzing structured and unstructured data in the cloud, created by Microsoft. Data Lake Storage is scalable and supports various data formats.
Amazon S3 is a cloud service for securely storing data with virtually unlimited scalability. It is efficient, ensures consistency, and provides easy access to data.
Databricks is a cloud-based analytics platform that combines data engineering, data analysis, machine learning, and predictive models. It processes large datasets with high efficiency.
Microsoft Fabric is an integrated analytics environment that combines various tools such as Power BI, Data Factory, and Synapse. The platform supports the entire data lifecycle, including integration, processing, analysis, and visualization of results.
Google BigLake is a service that combines the features of both data warehouses and data lakes, making it easier to manage data in various formats and locations. It also allows processing large datasets without the need to move them between systems.
Google Cloud Dataflow is a data processing service based on Apache Beam. It supports distributed data processing in real-time and advanced analytics.
Azure Data Factory is a cloud-based data integration service that automates data flows and orchestrates processing tasks. It enables seamless integration of data from both cloud and on-premises sources for processing within a single environment.
Apache Kafka processes real-time data streams and supports the management of large volumes of data from various sources. It enables the analysis of events immediately after they occur.
Pub/Sub is used for messaging between applications, real-time data stream processing, analysis, and message queue creation. It integrates well with microservices and event-driven architectures (EDA).
Google Cloud Run supports containerized applications in a scalable and automated way, optimizing costs and resources. It allows flexible and efficient management of cloud applications, reducing the workload.
Azure Functions is another serverless solution that runs code in response to events, eliminating the need for server management. Its other advantages include the ability to automate processes and integrate various services.
AWS Lambda is an event-driven, serverless Function as a Service (FaaS) that enables automatic execution of code in response to events. It allows running applications without server infrastructure.
Azure App Service is a cloud platform used for running web and mobile applications. It offers automatic resource scaling and integration with DevOps tools (e.g., GitHub, Azure DevOps).
Snowflake is a platform that enables the storage, processing, and analysis of large datasets in the cloud. It is easily scalable, efficient, and ensures consistency as well as easy access to data.
Amazon Redshift is a cloud data warehouse that enables fast processing and analysis of large datasets. Redshift also offers the creation of complex analyses and real-time data reporting.
BigQuery is a scalable data analysis platform from Google Cloud. It enables fast processing of large datasets, analytics, and advanced reporting. It simplifies data access through integration with various data sources.
Azure Synapse Analytics is a platform that combines data warehousing, big data processing, and real-time analytics. It enables complex analyses on large volumes of data.
Data Build Tool simplifies data transformation and modeling directly in databases. It allows creating complex structures, automating processes, and managing data models in SQL.
Dataform is part of the Google Cloud Platform, automating data transformation in BigQuery using SQL query language. It supports serverless data stream orchestration and enables collaborative work with data.
Pandas is a data structure and analytical tool library in Python. It is useful for data manipulation and analysis. Pandas is used particularly in statistics and machine learning.
PySpark is an API for Apache Spark that allows processing large amounts of data in a distributed environment, in real-time. This tool is easy to use and versatile in its functionality.
Looker Studio is a tool used for exploring and advanced data visualization from various sources, in the form of clear reports, charts, and dashboards. It facilitates data sharing and supports simultaneous collaboration among multiple users, without the need for coding.
Tableau, an application from Salesforce, is a versatile tool for data analysis and visualization, ideal for those seeking intuitive solutions. It is valued for its visualizations of spatial and geographical data, quick trend identification, and data analysis accuracy.
Power BI, Microsoft’s Business Intelligence platform, efficiently transforms large volumes of data into clear, interactive visualizations and accessible reports. It easily integrates with various data sources and monitors KPIs in real-time.
Looker is a cloud-based Business Intelligence and data analytics platform that enables data exploration, sharing, and visualization while supporting decision-making processes. Looker also leverages machine learning to automate processes and generate predictions.
Terraform is an open-source tool that allows for infrastructure management as code, as well as the automatic creation and updating of cloud resources. It supports efficient infrastructure control, minimizes the risk of errors, and ensures transparency and repeatability of processes.
GCP Workflows automates workflows in the cloud and simplifies the management of processes connecting Google Cloud services. This tool saves time by avoiding the duplication of tasks, improves work quality by eliminating errors, and enables efficient resource management.
Apache Airflow manages workflows, enabling scheduling, monitoring, and automation of ETL processes and other analytical tasks. It also provides access to the status of completed and ongoing tasks, as well as insights into their execution logs.
Rundeck is an open-source automation tool that enables scheduling, managing, and executing tasks on servers. It allows for quick response to events and supports the optimization of administrative tasks.
Python is a programming language, also used for machine learning, with libraries dedicated to machine learning (e.g., TensorFlow and scikit-learn). It is used for creating and testing machine learning models.
BigQuery ML allows the creation of machine learning models directly within Google’s data warehouse using only SQL. It provides a fast time-to-market, is cost-effective, and enables rapid iterative work.
R is a programming language primarily used for statistical calculations, data analysis, and visualization, but it also has modules for training and testing machine learning models. It enables rapid prototyping and deployment of machine learning.
Vertex AI is used for deploying, testing, and managing machine learning models. It also includes pre-built models prepared and trained by Google, such as Gemini. Vertex AI also supports custom models from TensorFlow, PyTorch, and other popular frameworks.
Bartosz Szymański
Data Strategy and Customer Relations Director