Machine learning development services

Machine learning is a discipline within artificial intelligence that enables software to learn from data using algorithms instead of following hard-coded instructions. 


Of course, you need an adequate volume of it; otherwise, your model won’t be able to learn properly. It also must be well-organized, or it won’t be accessible or usable by the system. If your data is unstructured, our experts can help you transform it into clean, structured, and model-ready datasets.


Once your data meets these criteria, you can build an ML model that delivers tangible value to your organization.


For businesses, ML can help analyze patterns, forecast trends, automate decisions, and streamline operations. It helps organizations make data-driven, efficient, and objective choices for better performance and uncovers valuable insights.

Machine learning has a wide range of use cases. It can power recommendation systems that improve user experiences. It enables predictive maintenance in manufacturing to protect critical equipment. It even enhances healthcare diagnostics through image recognition and analysis.


Businesses also use ML for fraud detection by monitoring for anomalous activities in real time. They use it to personalize marketing efforts by tailoring recommendations based on user preferences. Logistics use it to optimize deliveries and forecast demand. 


In short, there are limitless use cases through which ML helps teams work more efficiently and innovate faster.

Custom ML solutions benefit any organization that already collects data and wants to use it more effectively. Industries such as finance, healthcare, retail, manufacturing, logistics, and telecommunications are already using it to get a deeper understanding of their operations and customers.


When built with your data and around your workflows, an ML model can significantly increase the accuracy of your predictions. It can streamline processes and drive measurable efficiency improvements.

Generic tools are just that—generic. They are designed for general use. Custom ML development, on the other hand, is built around your business goals, data quality, and domain requirements.
 A bespoke model reflects the realities of your organization, providing results that are more relevant, accurate, and adaptable—ensuring unparalleled performance and better return on investment over time.

At Infinum, we follow a comprehensive, end-to-end process. We start by understanding your goals and data realities, then guide you through deployment and scaling. 


It starts with a data audit, where we determine whether the solution is feasible based on the amount and quality of data your business possesses. The audit also reveals how complex the solution is, based on the data you have to use to train it.


This is followed by the roadmap design, which flows into the proof of concept development, so you can test out your solution without investing in full-scale development.


Our specialists work closely with your internal teams to ensure seamless collaboration, while we design and deploy your innovative and efficient solutions.

The cost of ML development depends on several factors, including data availability and quality, project complexity, and the desired level of integration. As such, it is difficult to provide a ballpark figure without discussing your needs first.


That is why we typically suggest starting with the agentic sprint, which is made up of two stages: First is a discovery phase to define your objectives and scope. From there, we provide a clear, transparent estimate based on your organization’s goals, timeline, and resources.

We use a wide range of ML and data engineering technologies—including AWS SageMaker, MLflow, PyTorch, Scikit-learn, Hugging Face, and Apache Spark—depending on the requirements of your project.


Tool selection is a critical factor in the success of any ML initiative, so we choose frameworks and platforms that best align with your data, infrastructure, and long-term objectives.

As part of our MLOps and model monitoring practices, we cover continuous performance tracking, retraining, and metric analysis. As soon as we notice a model or data drift, we will intervene to help your model maintain consistently accurate results and business relevance.

We absolutely do. If you are an organization just starting your ML journey, do get in touch.


Our process includes consulting and data readiness assessments to help you understand where ML can make the greatest impact.


We guide you from strategy to execution to ensure your solution is both feasible and impactful.

Yes. Integration is a core part of our ML development service offering. We design ML solutions that work within your existing products, platforms, and IT environment.

We ensure smooth interoperability with everything, including CRM tools, analytics platforms, or custom enterprise software. Part of our responsibility is to make sure the technology enhances your workflows rather than disrupting them.

Interested in how we can help your business? Get in touch with us.