— AI-Driven Digitalization Platform for Industrial Automation

Business Needs

The client – a high-tech leading global provider of automation technology and digital solutions, serving industries such as automotive, manufacturing, logistics, and building materials, headquartered in Germany – aimed to develop a robust Digitalization Platform to accelerate their digital transformation initiatives. The platform was designed to:

  • Leverage machine learning to optimize processes and provide actionable insights.
  • Integrate various systems for seamless interoperability.
  • Automate complex workflows to enhance operational efficiency.
Solution

A dedicated development team was assembled to build and deploy a scalable, AI-powered Digitalization Platform. The solution featured:

  • Machine Learning Insights: Enabling predictive analytics and intelligent decision-making.
  • System Integration Capabilities: Unifying disparate systems to ensure streamlined data flow.
  • Automation of Workflows: Reducing manual processes to increase productivity.
Results

The project was successfully delivered within the agreed timeline and budget, empowering the customer with a cutting-edge digital transformation solution. The implemented platform significantly improved operational efficiency, facilitated seamless system integration, and enabled data-driven decision-making through advanced machine learning capabilities.

Cooperation lasted for 4 years with a team of up to 25 engineers.

Technologies

DevOps:

  • Version Control: Git, Bitbucket
  • Continuous Integration and Deployment: Jenkins, GitLab CI/CD, Docker, Kubernetes
  • Configuration Management: Ansible
  • Monitoring and Logging: ELK Stack (Elasticsearch, Logstash, Kibana), Prometheus, Grafana
  • Cloud Platform: AWS, Microsoft Azure

Backend Development:

  • Programming Languages: C#, Java, Python
  • Frameworks: .NET Core, Spring Boot, Django
  • Databases: MySQL, PostgreSQL
  • Web Services: RESTful APIs
  • Message Brokers: RabbitMQ, Apache Kafka
  • Caching: Redis
  • Authentication and Authorization: OAuth 2.0, JWT

Frontend development:

  • Programming Languages: HTML5, CSS3, JavaScript (ES6+)
  • Frameworks: React, Angular, Vue.js
  • Libraries: Redux, RxJS, Sass, Bootstrap
  • Build Tools: Webpack, Babel
  • Testing Frameworks: Jest, Enzyme

Machine Learning:

  • Frameworks: TensorFlow, PyTorch
  • Libraries: scikit-learn, NumPy, pandas
  • Data Processing: Apache Spark
  • Deployment: Docker, Kubernetes
  • Model Monitoring: TensorFlow Serving, KFServing
  • Experiment Tracking: MLflow

Quality Assurance (QA):

  • Testing Frameworks: Selenium, Cypress, JUnit
  • Test Automation Tools: Jenkins, Travis CI
  • Code Quality Tools: SonarQube, ESLint, Prettier
  • Performance Testing: JMeter
  • Test Management: TestRail
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