- 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.
- ScienceSoft is a 3-Year Champion in The Americas’ Fastest-Growing Companies Rating by the Financial Times.
- 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 healthcare, banking, lending, investment, insurance, retail, ecommerce, professional services, manufacturing, transportation and logistics, energy, telecommunications, and more.
Theme Partner
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. In data science and AI since 1989, Edylinn renders full-cycle machine learning services to introduce powerful (big) data analytics. We help companies solve business problems with accurate forecasting, root-cause analysis, data mining, and more.
What Makes Edylinn a Reliable Machine Learning Vendor
Our Industry Expertise
Retail
Web Design
Manufacturing
Financial services
Transportation & Logistics
Professional services
Telecoms
Educations
Scope of Our Machine Learning Services
1.Business analysis
- Defining business needs a firm wants to address with machine learning.
- Analyzing the existing machine learning environment (if any).
- Determining regulatory compliance requirements for an ML solution.
- Designing a machine learning implementation strategy and roadmap.
- Deciding on machine learning solution deliverables.
2.Technical design
- Designing an optimal feature set for an ML solution.
- Architecting an ML system according to scalability, security, and compliance requirements.
- Selecting optimal machine learning technologies (ML programming languages, ML development frameworks, data processing techs, etc.).
- Designing role-specific UX and UI to interact with an ML solution.
3.Data preparation
- Exploratory analysis of the existing data sources.
- Data collection, cleansing, and structuring.
- Defining the criteria for the machine learning model evaluation.
4.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.
5.Reporting
- Delivering machine learning output in an agreed format.
- Integrating machine learning models into an application for users’ self-service, if required.
6.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.
Code review practices in our company
E.g., ad hoc review, pass-around, walkthrough, pull request, inspection.
Control of code quality metrics
Maintainability Index (MI), Cyclomatic Complexity (CC), Depth of Inheritance, Class Coupling, Lines of Code.
Machine Learning Use Cases We Cover
Supply chain management
- Demand forecasting
- Inventory planning, management, and optimization, 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
Transportation and logistics
- Predicting vehicle demand
- Predicting optimal amounts of fuel needed based on the analysis of driving patterns
- Vehicle failure prediction and recommendation of maintenance actions
Operational intelligence
- Operations anomaly and bottleneck recognition
- Deviation root-cause analysis
- Operational decision-making
- Forecasting of operational performance metrics
Customer analytics
- Customer sentiment analysis
- Customer behavior prediction
- Sales forecasting
- Context-aware marketing
- AI-based product/service recommendation engines
- Digital assistants
Machine Learning Methods We Rely On
Non-neural-network machine learning
Supervised learning algorithms, such as decision trees, linear regression, logistic regression, support vector machines.
Unsupervised learning algorithms: K-means clustering, hierarchical clustering, etc.
Reinforcement learning methods, including Q-learning, SARSA, temporal differences method.
Neural networks, including deep learning
Convolutional and recurrent neural networks (including LSTM and GRU)
Autoencoders (VAE, DAE, SAE, etc.).
Generative adversarial networks (GANs)
Deep Q-Networks (DQNs)
Feed-forward neural networks, including Bayesian deep learning
Modular neural networks
What they've said about us
lorem ipsum dolor set amet et it lorem ipsum dolor set amet et it