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Industrial AI Revolutionizing Manufacturing and Operations - OneDDL - 07-03-2024
![[Image: 05514f13fabfdb170d081f1ebab8de2a.jpeg]](https://i123.fastpic.org/big/2024/0703/2a/05514f13fabfdb170d081f1ebab8de2a.jpeg)
Free Download Industrial AI Revolutionizing Manufacturing and Operations
Published 6/2024
Created by Uplatz Training
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 16 Lectures ( 9h 43m ) | Size: 4.82 GB
Master Industrial AI: Predictive Maintenance, Smart Manufacturing, Supply Chain Optimization, and Success Strategies.
What you'll learn:
Introduction to Industrial AI
Industrial AI in Practice
Strategies for Success in AI
Data Collection and Management
Data Analysis and Visualization
Machine Learning and AI Algorithms
Predictive Maintenance
Quality Control
Process Automation and Optimization
Supply Chain Optimization
Energy Management
Robotics and Human-Robot Collaboration
Digital Twins
Anomaly Detection and Risk Management
Ethics and Privacy in Industrial AI
Industrial AI Tools and Platforms
Requirements:
Enthusiasm and determination to make your mark on the world!
Description:
A warm welcome to the Industrial Artificial Intelligence (AI) course by Uplatz.Industrial AI refers to the application of artificial intelligence technologies to improve processes, efficiency, and decision-making in industrial settings such as manufacturing, energy, logistics, and other related sectors. It leverages data analysis, machine learning, and other AI techniques to optimize operations, predict maintenance needs, enhance quality control, and more. By integrating AI into industrial operations, companies can achieve greater efficiency, reduced costs, improved quality, and enhanced decision-making capabilities.How Industrial AI works
ata CollectionSensors and IoT Devices: Collect data from machines, equipment, and industrial processes. These devices monitor parameters like temperature, pressure, vibration, and more.Historical Data: Utilize existing datasets from past operations to identify patterns and trends.Data ProcessingData Cleaning: Ensure the collected data is accurate, consistent, and free from errors.Data Integration: Combine data from multiple sources to create a comprehensive dataset for analysis.Data Analysis and ModelingDescriptive Analytics: Analyze historical data to understand what has happened in the past.Predictive Analytics: Use machine learning models to predict future events, such as equipment failures or production bottlenecks.Prescriptive Analytics: Provide actionable recommendations based on predictive insights to optimize decision-making.Machine Learning and AI AlgorithmsSupervised Learning: Train models using labeled data to predict outcomes based on input features (e.g., predicting equipment failure).Unsupervised Learning: Identify patterns and anomalies in data without predefined labels (e.g., detecting unusual behavior in machinery).Reinforcement Learning: Optimize processes by learning from the outcomes of actions taken in a dynamic environment (e.g., optimizing robotic movements in real-time).ImplementationAutomation: Implement AI-driven automation to perform repetitive or complex tasks, reducing human intervention and error.Optimization: Continuously improve processes by integrating AI models that adapt to new data and changing conditions.Monitoring and MaintenanceReal-Time Monitoring: Use AI to monitor operations in real-time, providing instant feedback and alerts for any deviations from expected performance.Predictive Maintenance: Schedule maintenance activities based on predictive analytics, minimizing downtime and preventing unexpected failures.Applications of Industrial AI
redictive Maintenance: Predict when equipment is likely to fail and schedule maintenance before the failure occurs.Quality Control: Use AI-driven vision systems and data analysis to detect defects and ensure product quality.Supply Chain Optimization: Enhance supply chain efficiency through demand forecasting, inventory management, and logistics planning.Process Automation: Automate routine and complex tasks in manufacturing and other industrial processes.Energy Management: Optimize energy usage and reduce waste in industrial facilities.Anomaly Detection: Identify unusual patterns that indicate potential problems or opportunities for improvement.Human-Robot Collaboration: Facilitate advanced interactions between humans and robots to perform tasks requiring both human intuition and machine precision.Industrial AI - Course CurriculumIndustrial AI in Practice - IIndustrial AI in Practice - IIIndustrial AI in Practice - IIIIndustrial AI in Practice - IVIndustrial AI in Practice - VIndustrial AI in Practice - VIIndustrial AI in Practice - VIIIndustrial AI in Practice - VIIIIndustrial AI in Practice - IXIndustrial AI in Practice - XIndustrial AI in Practice - XIStrategies for Success in AI - IStrategies for Success in AI - IIStrategies for Success in AI - IIIStrategies for Success in AI - IVStrategies for Success in AI - VWho this course is for:
Anyone aspiring for a career in any Industry domain and/or Artificial Intelligence related technologies
Data Scientists
Machine Learning Engineers
Industrial Engineers
Automation Engineers
Robotics Engineers
IoT Specialists
Manufacturing Professionals
Operations Managers
Quality Control Analysts
Supply Chain Analysts
Energy Management Analysts
Safety and Risk Analysts
AI Researchers and Academics
Technology Consultants
IT Professionals in Industrial Sectors
Engineering Students
Business Analysts in Industrial Companies
Maintenance Engineers
Production Managers
R&D Professionals in Industrial Companies
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