At radheapps, our approach to assessing data quality is meticulous. We begin by identifying all data sources and thoroughly analysing their structures. We check whether the data is accurately labelled and if it’s formatted appropriately for machine learning algorithms. If we detect data quality issues, we collaborate with you to develop a comprehensive plan to address and rectify these issues. Rest assured, we provide detailed documentation of our data quality assessment process for transparency.

The timeframe for completing a machine learning project can vary based on factors such as project complexity, data availability and quality, available resources, and the expertise of the team. Smaller projects with simpler datasets and models may be completed in a few weeks. However, larger, more complex projects that involve extensive data processing, advanced feature engineering, and large-scale model training may take several months to a year or more to reach completion.

The cost of developing an ML-based solution can vary significantly and is contingent on several factors. It encompasses consulting fees, data acquisition and preparation expenses, infrastructure costs, model training and development outlays, as well as regulatory and compliance expenses. The total cost is calculated based on the specific pricing models you choose, tailored to your project requirements.

At radheapps, we offer four different hiring models, including fixed, hourly, and dedicated models. While you can share your project requirements with us during our consultation, we recommend the most suitable hiring model based on your unique business needs. Our goal is to provide you with the ideal team of ML developers to bring your project to life.