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.
- For the second straight year, ScienceSoft USA Corporation is listed among The Americas’ Fastest-Growing Companies 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 manufacturing, energy, retail and wholesale, professional services, healthcare, financial services, telecommunications.
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
- 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
- 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
- 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
- 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.
- 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.
- Exploratory analysis of the existing data sources.
- Data collection, cleansing, and structuring.
- Defining the criteria for the machine learning model evaluation.
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.
- Delivering machine learning output in an agreed format.
- Integrating machine learning models into an application for users’ self-service, if required.
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.
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
ScienceSoft's Python developers and data scientists excel at building general-purpose Python apps, big data and IoT platforms, AI and ML-based apps, and BI solutions.
ScienceSoft's Java developers build secure, resilient and efficient cloud-native and cloud-only software of any complexity and successfully modernize legacy software solutions.
ScienceSoft's C++ developers created the desktop version of Viber and an award-winning imaging application for a global leader in image processing.
By request of a leading market research company, we have built a Hadoop-based big data solution for monitoring and analyzing advertising channels in 10+ countries.
A large US-based jewelry manufacturer and retailer relies on ETL pipelines built by ScienceSoft’s Spark developers.
Our Apache Cassandra consultants helped a leading Internet of Vehicles company enhance their big data solution that analyzes IoT data from 600,000 vehicles.
We use Kafka for handling big data streams. In our IoT pet tracking solution, Kafka processes 30,000+ events per second from 1 million devices.
ScienceSoft has helped one of the top market research companies migrate its big data solution for advertising channel analysis to Apache Hive. Together with other improvements, this led tо 100x faster data processing.
We leverage Apache ZooKeeper to coordinate services in large-scale distributed systems and avoid server crashes, performance and partitioning issues.
We use HBase if your database should scale to billions of rows and millions of columns while maintaining constant write and read performance.
We leverage Azure Cosmos DB to implement a multi-model, globally distributed, elastic NoSQL database on the cloud. Our team used Cosmos DB in a connected car solution for one of the world’s technology leaders.
We use Amazon Redshift to build cost-effective data warehouses that easily handle complex queries and large amounts of data.
We use Amazon DynamoDB as a NoSQL database service for solutions that require low latency, high scalability and always available data.
ScienceSoft sets up Power BI to process data from any source and report on data findings in a user-friendly format.
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
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.
Designing and developing custom image analysis software.
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.
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