Machine learning development & consulting services

Our machine learning development services run the gamut from ML consulting and development to ML migration and integration solutions. Furthermore, ITRex delivers a full spectrum of related services, including generative AI consulting and development, Big Data, IPA, NLP, Deep Learning, Computer Vision, and Anomaly Detection services — all under one roof. Deep-rooted AI development and consulting expertise, technology-agnostic approach, extensive cross-industry experience, complete transparency, and flexibility — with us, you get all of this and so much more.

Our track record includes multiple machine learning development projects for SMBs and giants alike across a number of industries. Among the most landmark projects are an ML-based clinical decision support system for cancer patients for a healthcare analytics company, an ML-powered live stream content moderation tool for a social media giant, and an end-to-end ML solution for marketing campaigns for a provider of innovative marketing services to the financial sector.

A machine learning development project typically involves the following phases: problem definition and analysis, ML solution design, data collection and preparation, model selection and training, model performance evaluation, ML solution integration and deployment, and ML solution performance monitoring and optimization.

The data used in machine learning is typically numerical (consists of numbers) or categorical (consists of categories or labels, such as names, types, or categories). You can use any training data that can be transformed into numbers, including text, graphs, images, videos, tabular data, and more.
If the volume or quality of the data is not up to par, we will help you collect more data or use different data synthesis and data augmentation techniques to assist you in generating new data sets or transforming the already available data samples to create new ones. Which technique to select will mostly depend on the nature of your problem. You don’t need to worry about how much training data you have—our top machine learning development experts will shoulder the burden.

There isn't a one-size-fits-all machine learning development strategy that works for every project. The type of data you have and the results you want to achieve determine which machine learning approach is the most appropriate one. Each approach comes with its own set of advantages and limitations. Pre-trained on vast volumes of unlabeled data, foundation models emerge as the most cost-effective option in that you don't have to invest in a solid training data set. However, in some use cases, training a model from the ground up may turn out to be the preferred alternative. Our machine learning consulting company will help you specify which approach might be better suited for your specific scenario: train a model from scratch or fine-tune a pre-trained foundation model to your specific requirements.

The adoption rate, scalability of solutions, and alignment with business objectives are just a few examples of the variables that might affect how long it takes to realize the business benefit and return on investment (ROI) from an ML project. While some projects may require longer-term adoption and investment to show a substantial impact, others may produce immediate results and measurable return on investment.

We can't tell you the exact cost of the solution until we assess your requirements, objectives, and problems you are grappling with. Several factors determine the final cost of a machine learning development project. When estimating the cost of an ML solution, we consider the complexity of the solution, characteristics of the available data (data quality, type, and volume), ML approach, and the infrastructure and resources required. For a ballpark figure, contact our machine learning consultants.