What We
Do
01
Machine Learning
Business Evaluation
- Defining the business needs a company intends to use machine learning to solve.
- Examining the current machine learning setting (if any).
- Creating a roadmap and strategy for machine learning.
- Choosing the best machine learning tools.
- Selecting the deliverables for a machine learning solution.
Data Preparation
- Examination of the available data sources.
- Gathering, scrubbing, and organizing data
- Collecting, cleansing, and structuring the data.
- Defining the criteria for evaluating a machine learning model.
Creating and Implementing Machine Learning Models
- Exploration and refinement of machine learning models.
- Testing and evaluating machine learning models.
- The parameters of ML models are fine-tuned until the generated results are acceptable.
- The deployment of machine learning models.
Providing Reporting
- Supplying results from machine learning in a predetermined format.
- Adding machine learning models for users' self-service, if necessary.
02
Image Analysis
Design of Image Processing Software
- Defining the IA technology solutions to specific business problems. high-level business requirements for picture quality and recognition accuracy are extracted and converted into software features.
Software Architecture Design
- Enhance and optimize the existing software architecture while taking into account all the nuances that might affect its performance.
Evaluation and Selection of Implementation Options
- API integration and customisation for computer vision products from third parties.
- Creating from scratch a proprietary ML-driven technology.
- Using cloud-based services
- Portfolio Architecture
03
Data Science
Building an Infrastructure
- Data Aggregation
- Analysis
- Reporting
