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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, ScienceSoft 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 ScienceSoft a Reliable Machine Learning Vendor
- 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.
What makes ScienceSoft different
We achieve project success no matter what
ScienceSoft does not pass mere project administration off as project management, which, unfortunately, often happens on the market. We practice real project management, achieving project success for our clients no matter what.
Machine Learning Use Cases We Cover
- 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
- 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
- Predicting vehicle demand
- Predicting optimal amounts of fuel needed based on the analysis of driving patterns
- Vehicle failure prediction and recommendation of maintenance actions
- Operations anomaly and bottleneck recognition
- Deviation root-cause analysis
- Operational decision-making
- Forecasting of operational performance metrics
- Customer sentiment analysis
- Customer behavior prediction
- Sales forecasting
- Context-aware marketing
- AI-based product/service recommendation engines
- Digital assistants
- Financial planning and analysis
- Financial modeling
- Algorithmic trading and hedging
- Financial advisory and wealth management
- Intelligent processing of financial documents
- Dynamic pricing
- Financial fraud detection
Natural language processing
- Sentiment analysis
- Security authentication
- Chatbots
- Speech to text conversion
- Spam filtering
- 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
Want to discuss your ML solution?
Having decades-long practice in machine learning projects, we are eager to share our expert knowledge to help you seamlessly avail ML for the listed cases or your specific area of ML use.
Scope of Our Machine Learning Services
Depending on your needs and current ML environment (if any), our machine learning consulting services may include:
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.
Our Featured Machine Learning and Data Science Projects
What Our Clients Say
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.
Derek Carter
CEO
Right from the start, your team showed professionalism and expertise that immediately put us at ease.
We appreciate that you examined our needs with great diligence and went above and beyond to develop a solution that fully meets our high demands for image quality. Special credit to the data scientist: the performance of the new image stitching algorithm is amazing. Overall, you have exceeded our expectations in every way.
ScienceSoft’s team undertook the development of our AI-based software product from scratch. They delivered software in time and with the required quality.
The team regularly shared detailed project updates and recommendations on how certain functions could be implemented in a more efficient way.
Our Approach to Rendering Machine Learning Consulting
We follow project management practices that we've polished for 35 years to drive projects to their goals regardless of time and budget constraints.
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.
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
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
Choose Your Service Option
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.
How Much Does an ML-Powered Solution Cost?
The cost of machine learning services can vary from $30,000 to $600,000+, depending on the service option and the project scope.
Developing a separate ML-powered component
Cost: $30,000–$200,000
End-to-end development of an ML-based solution
Cost: $200,000–$600,000
The yearly cost of support services may be estimated as 15–25% of the initial development costs, and it may amount to 70% of the TCO during the entire solution lifespan.
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.