TDK QEEXO: AUTOMATED MACHINE LEARNING PLATFORM FOR EDGE DEVICES

TDK Qeexo's Auto Machine Learning (AutoML) platform offers a comprehensive solution that automates and simplifies the process of building and scaling ML models without the need for expensive experts.

• Intuitive User Experience: Easy-to-use user interface (UI) with no coding required.
• Edge Deployment: Translates models into C to compile and deploy directly on edge devices such as sensors and controllers to leverage the power of AI where data is generated to reduce latency, enhance data security, and enhance real-time decision-making.
• Edge Optimized: Supports Arm^® Cortex^®-M0-to-M4 class MCUs and other constrained environments.
• Sensor Data: Ingests data from multiple streams (sensor fusion) and is sensor agnostic.
• ML Methods: AutoML supports a wide range of ML models, allowing users to compare results from many algorithms: regressors, decision trees, and neural networks.
• Feature Extraction: Automated feature extraction to generate and weigh features from data for the best performance.
• Data: Visualize, label, and segment collected or uploaded data to understand patterns and problems.
• Reporting: Provides model performance summaries, visualizations, and recommendations.

Machine Learning Consulting
Are you looking to unlock the power of ML and maximize the value of your data? TDK Qeexo offers full-service expert ML consulting to guide you on your ML journey. Our ML Consulting team comprises experienced data scientists, engineers, and ML experts who bring a deep understanding of diverse industries and ML techniques. We partner with you to develop a tailored ML strategy that aligns with your business objectives and leverages the full potential of your data assets.

TDK Qeexo ML Consulting Services Overview:

• Holistic Approach: We take a comprehensive approach to ML consulting, considering not only the technical aspects but also the broader business context. Our ML engineers work closely with your team to understand your unique challenges, goals, and data ecosystem.
• Customized Solutions: We believe in the power of tailored solutions. We analyze your data, identify opportunities for ML applications, and create custom solutions that deliver actionable insights, optimize processes, and drive efficiency.
• End-to-End Expertise + AutoML: From data collection and preprocessing to model development, deployment, and ongoing maintenance, our ML Consulting team provides comprehensive support throughout the ML lifecycle. We bring expertise and the power of Qeexo AutoML to ensure that your ML models are accurate, reliable, scalable and deliver tangible results.
• Infrastructure Optimization: ML success often relies on a robust and scalable infrastructure. Our consultants assess your existing infrastructure and provide recommendations for optimizing your ML environment. Whether it is deploying ML models on edge devices, leveraging cloud platforms, or optimizing computing resources, we help you build a scalable and efficient infrastructure.
• Qeexo AutoML Training and Knowledge Transfer: We believe in empowering your team with ML knowledge and skills. Our ML Consulting engagements include training sessions and knowledge transfer activities, equipping your team with the expertise to maintain and enhance ML models independently with Qeexo AutoML.
• Continuous Innovation: As pioneers in AutoML on-edge devices, we stay at the forefront of ML advancements. Our ML Consulting team is immersed in the latest research, methodologies, and best practices. We bring this expertise to your organization, ensuring that your ML initiatives leverage the most innovative techniques and remain innovative and competitive.

AutoML Applications

Industrial & Manufacturing:

• Condition-based Monitoring (CbM): Detect early signs of degradation to optimize asset utilization, predict maintenance needs, minimize downtime, and reduce maintenance costs.
• Anomaly Detection: Identify abnormal patterns or fault signatures, trigger alerts and initiate corrective action.
• Operational Efficiency & Quality Control: Optimize production workflows, reduce defects, optimize resource allocation, and make data-driven decisions to enhance overall product quality and yield.

• Intelligent Transportation Systems: Leverage data from sensors, vehicles, and infrastructure to optimize traffic management, improve safety, and enhance vehicle performance.
• Fleet Management: Enable efficient resource allocation, predictive maintenance scheduling, fuel efficiency optimization, and effective route planning.
• Driver Assistance and Safety Systems: Rapidly prototype and deploy models that can detect and respond to various driving scenarios.

• Intelligent Building Automation: Optimize building automation systems by leveraging data from sensors, mechanical systems, weather forecasts, and occupancy patterns to enable predictive control of lighting, HVAC, and energy management systems.
• Predictive Maintenance and Fault Detection: Detect anomalies and potential faults in building systems by automatically analyzing sensor data and historical patterns. Identify deviations from normal operating conditions and generate alerts for proactive maintenance.
• Occupant-Centric Optimization: Analyze data from occupancy and environmental sensors to optimize lighting, temperature, and air quality to create personalized and comfortable spaces.

• Energy Generation and Distribution: Optimize energy generation schedules, predict demand fluctuations, and enable efficient grid management.
• Predictive Maintenance for Power Equipment: Detect early signs of equipment degradation, identify potential failures, and generate alerts for timely maintenance to minimize unplanned downtime.
• Renewable Energy Forecasting and Integration: Generate accurate forecasts of renewable energy generation to facilitate efficient grid management, optimal utilization of renewable resources, and improved integration of intermittent energy sources into the power system.

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