Machine Learning Consulting Services
Machine learning (ML) consulting services may include advising on and implementing ML-based software as well as supporting the existing ML initiatives.
With experience in data science and AI since 1989, ScienceSoft renders a full range of machine learning services to help companies solve business problems with accurate forecasts and predictions, root-cause analysis, (big) data mining and more.
- Data science and data analytics expertise since 1989.
- Big data services since 2013.
- Data warehouse services since 2005.
- Image analysis consulting and development services since 2013.
- Hands-on experience with all major languages, libraries and cloud services for data science.
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ISO 9001 and ISO 27001-certified to assure the quality of the machine learning consulting services and the security of the customers' data.
- Domain experience in 30 industries, including manufacturing, energy, retail and wholesale, professional services, healthcare, financial services, telecommunications.
1
Business analysis
- Defining business needs a firm wants to address with machine learning.
- Analyzing the existing machine learning environment (if any).
- Designing a machine learning strategy and roadmap.
- Selecting optimal machine learning technologies.
- Deciding on machine learning solution deliverables.
2
Data preparation
- Exploratory analysis of the existing data sources.
- Data collection, cleansing, and structuring.
- Defining the criteria for the machine learning model evaluation.
3
Development and implementation of machine learning models
- ML model exploration and refinement.
- ML model testing and evaluation.
- Fine-tuning the parameters of ML models until the generated results are acceptable.
- Deploying the ML models.
4
Reporting
- Delivering machine learning output in an agreed format.
- Integrating machine learning models into an application for users’ self-service, if required.
5
Support and maintenance of machine learning models
- Continuous monitoring and tuning of ML models for greater accuracy.
- Adding new data to the ML models for deeper insight.
- Building new ML models to address new business and data analytics questions.
Supply chain management
- Demand forecasting
- Inventory planning and management, preventive alerting for inventory control
- Identifying quality issues in line production
- Supplier relationship management based on smart supplier selection
- Identifying fraudulent transactions and preventing credential abuse
Production efficiency
- Automated recognition of manufacturing defects
- Power consumption forecasting and optimization
- Process quality prediction based on process parameters
- Production loss root cause analysis
- Production output predictive modeling with varying inputs
Predictive maintenance
- Predicting remaining useful lifetime
- Flagging anomalous behavior
- Predicting failure probability over time/in a certain number of steps
- Root cause failure analysis
- Providing recommended actions to take to avoid the potential failure
- Customer sentiment analysis
- Customer behavior prediction
- Sales forecasting
- Context-aware marketing
- AI-based product/service recommendation engines
- Digital assistants
Natural language processing
- Sentiment analysis
- Security authentication
- Chatbots
- Speech to text conversion
- Spam filtering
Computer vision
- Medical image analysis
- Biometric verification
- Tracking customers inside retail stores
- Object recognition and classification in traffic
- Autonomous vehicles
- Packaging and product quality monitoring in manufacturing
Two years ago, we commissioned ScienceSoft to audit and upgrade our partially developed AI-based software for clay pigeon shooting tracking.
ScienceSoft ramped up a development team consisting of two C++ developers, two data scientists, and a UI design expert to fulfill the project. The team identified core errors, which didn’t allow efficient solution operation, and implemented high-speed convolutional neural networks to fix them. As a result, the system could track a flying target in a real-life outdoor environment and faultlessly detect shooter’s performance.
Simen Løkka, CEO, Travision AS
KPIs-based service delivery
We can form the following KPI set:
- Output quality KPIs:
- Insights by value (high / average / low).
- Forecast accuracy.
- Missing alerts.
- KPIs related to business results (decrease in customer churn, operational costs reduction, etc.).
- User satisfaction score.
Guaranteed data security
To secure your data utilized for machine learning projects, we:
- Process data on highly secure cloud facilities (Azure, AWS, Google Cloud).
- Conduct 24/7 in-house data security monitoring.
- Use secure data transfer methods (FTP and VPN) controlled via regular health checks.
Technologies We Use
Programming languages
Machine learning platforms and services
Machine learning frameworks and libraries
Machine learning algorithms
Supervised learning
Unsupervised learning
Reinforcement learning
Neural networks, including deep learning
Neural networks
Convolutional and recurrent neural networks (LSTM, GRU, etc.)
Autoencoders (VAE,DAE, SAE, etc.)
Generative adversarial networks (GANs)
Deep Q-Network (DQN)
Feedforward Neural Network
Radial basis function network
Modular neural network
Statistics methods
Descriptive statistics
ARMA
ARIMA
Bayesian inference
Big data
Click on the technology to learn about our capabilities in it.
Data visualization
Our Featured Machine Learning and Data Science Projects
Machine Learning Consulting for Electric Energy Consumption Analysis and Forecasting
- High-level design of ML-based software architecture to facilitate electric energy consumption analysis and forecasting.
- Forecasting built on Seq2Seq models, LightGBM and XGBoost ML models.
Data Science Implementation for Sales Analysis and Forecasting
- Accurate sales forecast built on a linear regression ML algorithm, an autoregressive integrated moving average (ARIMA) model, median forecasting and zero forecasting.
Automated Visual Inspection Software Development for Defect Recognition in Polyurethane Film
- Detecting film defects in real time with the cv.findContours() and the OpenCV contourArea() methods.
- Informed decision-making within the production process and improved production quality.
Development of Defect Recognition Software for an Oil Drilling Equipment Manufacturer
- Analyzing the drill bit images with the Hough Circle Transform method, simple CNN and Mask R-CNN.
- Optimized drilling equipment condition monitoring, timely detection of emerging drill bit defects, streamlined inventory management, reduced inventory holding costs.
Machine learning consulting
For companies seeking strategic guidance throughout the whole cycle of their machine learning development project.
Machine learning implementation
For companies that need to design, develop and launch a smoothly functioning machine learning solution.
Machine learning support
For companies that need to fix inefficiencies within their current ML environment and get tailored recommendations on increasing the quality of ML insights in the future.
Related Services We Offer
Getting advanced data analytics insights derived with machine learning technologies or enhancing the existing machine learning initiatives without investing in in-house competencies.
Advising on, developing and supporting data science solutions to help companies run experiments on their data in search of business insights.
Retrieving valuable insights out of large, heterogeneous and constantly changing data sets without investing in an in-house data mining talents.
Big data consulting, implementation, support, and big data as a service to help companies store and process big data in real-time as well as retrieve advance analytics insights out of huge datasets.
Why Turn To Machine Learning Consulting Right Now
Implementing machine learning solutions brings considerable benefits, including:
- Increased employee productivity due to automating repetitive and routine tasks with computer vision and natural language processing.
- Enhanced customer service experience due to AI-powered chatbots and virtual assistants facilitating real-time communication.
- Accelerated sales process due to improved opportunity insights and better lead prioritization.
- Reduced equipment maintenance costs due to predictive monitoring and preventive maintenance.
- Increased production efficiency due to demand and throughput forecasting, production process optimization and predictive modeling of product quality.