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 2017, Swift Web Design 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.

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What Makes Swift Web Design a Reliable Machine Learning Vendor

Data science and data analytics expertise since 2017.
Big data services since 2017.
Data warehouse services since 2017.
Image analysis consulting and development services since 2017.
Hands-on experience with all major languages, libraries and cloud services for data science.
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.

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 metric

Customer analytics

Customer sentiment analysis
Customer behavior prediction
Sales forecasting
Context-aware marketing
AI-based product/service recommendation engines
Digital assistants

Financial management

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

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

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 Approach to Rendering Machine Learning Consulting

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.

Choose Your Service Option

Machine learning consulting

For companies seeking strategic guidance throughout the whole cycle of their machine learning development project.

Go for consulting

Machine learning implementation

For companies that need to design, develop and launch a smoothly functioning machine learning solution.

Go for implementation

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.

Go for support

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.

Our Advanced Big Data Competencies

Swift Web Design combines big data expertise with decades-long experience in other advanced technologies to deliver end-to-end big data applications that bring maximum value to their users.

Artificial intelligence

Developing personalization engines, natural language processing systems, computer vision, and other AI-powered solutions that maintain stable performance under any data load.

Machine learning

Building highly accurate ML models that identify hidden patterns in big data, provide reliable forecasts, power complex neural networks, and automate complex business algorithms.

Data science

Providing strategic and technological guidance in wrangling, exploring, and applying data, we employ reliable statistical methods, establish robust data quality management processes, and help avoid issues related to inaccurate data and false predictions.

Business intelligence

Integrating large volumes of high-velocity data into scalable, fault-tolerant analytics solutions that provide trustworthy insights to any number of users.

Data visualization

Creating easy-to-navigate, customizable reports and dashboards that are tailored to the needs of specific business users and provide a clear and concentrated view of data insights that matter most.

Cloud services

Proficient in Azure, AWS, and GCP, we build cloud big data solutions from scratch and migrate legacy workloads to the cloud to achieve better scalability, cost-efficiency, and availability of our customers’ data.