Machine learning consulting

Machine learning consulting

Machine learning consulting helps companies deploy ML algorithms trained with corporate data to automate and optimize various business processes. With solid expertise in the ML domain, Itransition helps enterprises personalize recommendations for customers, detect fraud, predict churn, and more.

Why choose Itransition

20+ years in IT consulting and software development services

5+ years of experience in machine learning consulting and development

Standing partnerships with Microsoft, AWS, and Google Cloud

Delivering AI solutions in full adherence to HIPPA, GDPR, FDA, and other standards and regulations

Client spotlight

An AI-powered BI platform


buyer conversion rate increase

An AI-powered shoppable video platform


increase in client satisfaction

Looking for a reliable tech partner for your ML project?

Contact us

The scope of our machine learning consulting services

The scope of our machine learning consulting services


Itransition’s machine learning consultants provide strategic guidance throughout end-to-end ML development projects:

  • Analysis of your business processes and data sources as well as setting project goals
  • Business process optimization roadmapping
  • Tech stack selection
  • Project scope planning


Depending on the available data and outlined business goals, our machine learning engineers

  • Offer optimal ML modeling algorithms
  • Handle all data-related processes (data collection, processing, and structuring).
  • Build and train ML models until the results are acceptable
  • Define mechanisms for assessing the quality of the delivered solution


To ensure the smooth operation of machine learning models without jeopardizing their integrity and results, our machine learning consultants:

  • Shape ML models into fully functioning AI solutions
  • Integrate ML models into enterprise software to augment its capabilities


If your existing solution fails to fulfill set objectives or you need to upgrade it to align with new business objectives, our consultants will:

  • Assess ML technology maturity and analyze ML solution gaps
  • Outline possible optimization scenarios and their implementation
  • Perform troubleshooting and upgrades
  • Provide ML solution administration support

Our machine learning expertise

Data mining

We develop custom algorithms that look for hidden patterns and trends in huge data sets, helping you solve business problems.

  • Discovery of purchase trends
  • Identification of at-risk customers
  • Market risks estimation
  • Upselling and cross-selling product recommendations
  • Treatment recommendations
  • Fraud identification
  • Wear and tear of production assets prediction

Machine learning solutions across industries

Itransition renders ML consulting services to help its clients incorporate insights derived out of voluminous data into healthcare practice to achieve better medical outcomes, improve patient satisfaction, reduce operating costs, and minimize fraud.

  • Automated diagnosis
  • Early disease detection
  • At-risk patients and patient deterioration detection
  • Personalized treatment recommendations
  • Fraud, waste, and abuse detection
  • AI-based medical assistants

    Want to entrust your machine learning project to Itransition?

    Let’s discuss the details

    Book a free consultation

    Our approach to ML projects


    Business analysis

    Our ML consultants start with a careful analysis of your business needs as well as existing tech infrastructure and data sources to define the scope of the project (including objectives, deliverables and timeframes).


    Solution design

    Based on the findings from the previous step, we design the architecture of the solution, outline the optimal ML methodology and recommend a technology stack. To ensure the feasibility and economic viability of the solution as well as outline possible limitations, we deliver a PoC at this stage.


    Data preparation

    After the exploratory data source analysis, our machine-learning experts cleanse, annotate and transform data for ML training and testing and define the ML model evaluation criteria.



    To meet set business objectives, our machine learning engineers provide ML algorithms with the prepared training data to learn from using both supervised and unsupervised machine learning techniques.


    Integration and deployment

    Our team wraps the ML model source code into a separate container, integrates it into the solution, and publishes it to the production infrastructure, so you can start getting benefits from freshly generated data.



    We continue monitoring and enhancing the solution by releasing new features, adding new data sets, or implementing new features.

    Machine learning technologies we use

    Our machine learning engineers have hands-on experience with major programming languages, ML libraries and frameworks, and ready-to-use third-party services to build tailored ML solutions as well as improve the performance, prediction accuracy, and resource optimization of the existing ones.






    Libraries and frameworks

    TensorFlow Keras PyTorch Skikit-Learn

    Theano MXNet NumPy NLTK

    Pandas SparkML Sonnet DarkNet

    Catboost XGBoostAnnoy Faiss NvidiaDigits

    Network architectures

    Residual neural network (ResNet) Recurrent neural network (RNN) Convolutional neural network (CNN) Regression models

    Categorization models Generative adversarial network (GAN) Neural radiance field (NeRF) Clustering algorithms

    YoloNet AlphaPose Skeleton detection Pose2Seg

    RetinaFace U-Net DBSCAN


    Amazon SageMaker Amazon Rekognition Amazon Lex Amazon Polly


    Azure Machine Learning Azure Cognitive Services Language Understanding Intelligent Service Azure Bot Services

    Google Cloud

    Cloud Machine Learning Engine Cloud Vision API Cloud Natural Language AI Cloud Speech API DialogFlow

    Machine learning consulting: FAQ from clients

    What engagement models do you offer?

    How do you integrate the ML solution with our legacy systems?

    How do you guarantee the safety of our information?

    Do we need the infrastructure to train models?

    What if we don’t have enough data for training models?