Machine learning development
services & solutions

Pricing can vary greatly depending on the type of ML solution you intend to build for the chosen real-world use case. When estimating ML TCO, we factor in data-related attributes (number of data sources, data type, data volume, and data quality used for ML development), machine learning algorithm accuracy requirements, ML approach and methodology, and infrastructure costs.

Basic PoC implementation starts from $10,000. If you’d like a ballpark estimation of the cost and resources needed to implement your ML project, you can contact our consultants.

Machine learning models are trained on labeled data (in supervised machine learning) and unlabeled data (in unsupervised machine learning). We can use any data that can be converted into numbers, including tabular data, text, images, video, and graphs. That said, training data should be as close as possible to production data.

As for data volume, the more, the better. At the same time, data diversity is as important as data volume. At Itransition, we help with training data collection, preparation, and augmentation to ensure its quality and diversity.

Data science is a broad, multidisciplinary field that aims to find correlations and trends in data to derive insights and enable informed decision-making. Data scientists combine expertise in data mining, statistics, data analytics, data modeling, and machine learning modeling and programming to define and implement processes and tools that transform data into actionable insights. ML engineers, on the other hand, build algorithms and train models that can learn patterns from data and make predictions or decisions, applying techniques such as supervised, unsupervised, and reinforcement learning.

ML development outsourcing helps businesses reduce the costs associated with hiring and maintaining an in-house ML team, enabling them to focus on their core activities, while entrusting complex development tasks to specialists with dedicated ML experience. ML development teams that have established project delivery processes and DevOps expertise also accelerate software implementation, ensuring its security, high performance, and reliability.

The main challenges companies implementing ML solutions encounter include high initial investment, ethical and regulatory challenges, skill gaps within the company, the lack of high-quality training data, and solution integration complexity. At Itransition, we help you overcome these problems, preparing data for model training, implementing robust data security measures and ethical AI practices, integrating the ML solution with the broader technical ecosystem, and providing change management support.