Machine learning consulting streamlines the deployment of ML solutions powered by self-learning algorithms and designed to enhance decision-making and operational efficiency. Itransition offers its ML expertise to help companies get the most out of this technology and overcome adoption challenges.
25+ years in IT consulting and software development services
5+ years of experience in machine learning consulting and development
Standing partnerships with Microsoft and AWS
Delivering AI solutions in full adherence to HIPPA, GDPR, FDA, and other standards and regulations
buyer conversion rate
Itransition delivered an advanced BI platform with an ML-powered recommender system and computer vision capabilities to help the customer predict user behavior and personalize customer interactions.
customer satisfaction
Our team enhanced a video ecommerce platform with ML-based functionalities, allowing the customer to create shoppable images and videos for online channels and provide a frictionless ecommerce experience.
Over the course of our collaboration with Itransition, we were consistently impressed with both skill and dedication their team employed to fulfill our business needs. Itransition’s involvement extended beyond the technical realization of the project, they acted as consultants, continuously helping us hone the project vision and suggesting approaches that would be best suited for the intricacies of our business.
Dr Sarah Melville
Media Director, YouGov Sport
Itransition’s ML skills include computer vision-powered solutions that can capture, process, and make sense of visual inputs from multimedia content or the physical world to extract valuable information and autonomously trigger certain actions.
Itransition provides ML consulting services to help our clients incorporate insights derived from big data into their healthcare practices and personalize treatments, achieve better medical outcomes, improve patient satisfaction, and reduce operating costs.
With expertise in a full spectrum of cognitive technologies, we can craft artificial intelligence solutions tailored to your requirements, streamline their deployment to your corporate scenario, and help you address emerging technical or business challenges.
Itransition develops ML systems powered by supervised, unsupervised, or reinforcement learning algorithms that can autonomously learn how to complete analytical or operational tasks and progressively improve their performance through experience.
Itransition’s data scientists can help your organization gather, prepare, store, and analyze structured or unstructured data to identify patterns, anomalies, and correlations, shed light on current trends, and forecast future scenarios to enhance decision-making.
Itransition’s team provides data mapping, transformation, warehousing, analysis, and visualization services to help enterprises extract knowledge from their big data assets and selected external sources.
We create robotic process automation solutions, including software bots and smart assistants, and expand their operational scope with AI-fuelled capabilities to support your workforce in performing tedious and time-consuming clerical tasks.
Our machine learning engineers have hands-on experience with major programming languages, libraries, frameworks, and third-party tools and services to build tailored ML solutions or improve the performance and resource usage of the existing ones.
Python
R
Objective-C
C++
Java/Kotlin
Our machine learning consulting company can help your organization select an optimal tech stack and identify use cases requiring ML instead of conventional solutions.
With the support of a machine learning consulting firm, you can set up a suitable ML development and implementation roadmap and accurately define timelines, budgets, tasks, teams, and iterations.
Our ML consultants can complement your in-house experts to complete your ML project within a shorter time frame without recruiting and training additional talent.
A team of ML consultants will help you address potential business or technical challenges (lack of training data, ML model bias, non-compliance, etc.) and mitigate related risks.