Weidmüller Industrial AutoML - Profitieren Sie von Machine Learning ohne Data Science Kenntnisse

Weidmüller

13 Jan 202102:31

Summary

TLDRThe video script introduces Weidmüller's machine learning solutions designed to simplify the adoption of ML in industrial settings. It highlights the benefits for machine builders and manufacturing companies, such as enabling new data-based services and higher productivity levels. The tool is user-friendly, requiring no data science knowledge, and allows teams to import machine or process data to identify normal and abnormal behavior. By leveraging domain knowledge, users can select models based on performance and plausibility, deploy them on-premises or in the cloud, and continuously improve model performance. This intuitive solution facilitates the integration of ML into existing infrastructure, making it easier than ever to enhance machine productivity and monitor product quality.

Takeaways

🤖 Machine learning offers numerous advantages for machine builders and manufacturing companies, including new data-based services and higher productivity levels.
📈 Machine learning requires data tests to ensure its effectiveness, which is a crucial step in the process.
🛠 Weidmüller introduces a machine learning solution that does not require any data science knowledge from the team.
🔧 The tool provides machine learning competencies to the company by allowing them to import their machine or process data.
📊 The tool identifies normal and abnormal behavior based on the company's domain knowledge and creates a selection of models.
🔍 Users can select the appropriate model based on criteria such as model performance and plausibility.
🌐 The selected model can be directly implemented, offering flexibility and platform independence, whether in the cloud or on-premises.
🚀 The model provides information to detect anomalies and avoid process or machine failures, enhancing operational efficiency.
🔧 Users can leverage their domain knowledge to continuously improve the performance of the model.
💡 Weidmüller's solution enables machine builders to offer new data-based services without the need for data science support.
📈 Manufacturing companies can increase the productivity of their machines and efficiently monitor product quality with this solution.
👍 The tool is intuitive and easy to operate without additional training, allowing for seamless integration into existing infrastructure.
🚀 The tool accelerates and simplifies the deployment of machine learning in the industry, making it more accessible than ever.

Q & A

What are the benefits of machine learning for machine builders and manufacturing companies mentioned in the script?
-The script mentions that machine learning offers numerous advantages such as new data-based services and the ability to achieve a higher level of productivity.
What is the prerequisite for machine learning as stated in the script?
-According to the script, data tests are always required for machine learning.
What is the solution presented by Weidmüller for machine learning?
-Weidmüller presents a machine learning solution that equips a company with the necessary machine learning competencies without requiring any prior knowledge in data science.
How does the Weidmüller tool help in identifying normal and abnormal behavior of machines?
-The Weidmüller tool allows users to import their machine or process data and then, based on domain knowledge, it identifies normal and abnormal behavior.
What does the tool do with the imported data?
-The tool creates a selection of models based on the user's inputs, from which they can choose the appropriate model based on criteria such as model performance and plausibility.
How flexible is the application of the model after selection?
-The application of the model is highly flexible and platform-independent, allowing it to be deployed either in the cloud or on-premises.
What kind of information does the model provide after deployment?
-The deployed model provides information that enables the detection of anomalies and helps in avoiding process or machine failures.
How can machine builders use the Weidmüller solution to offer new data-based services?
-Machine builders can use the intuitive Weidmüller tool to offer new data-based services without the need for support through data science tests.
What is the impact of using the Weidmüller solution on manufacturing companies?
-Manufacturing companies can increase the productivity of their machines and efficiently monitor product quality using the Weidmüller solution.
How user-friendly is the Weidmüller tool according to the script?
-The script describes the Weidmüller tool as intuitive and easy to operate without additional training, allowing for easy integration into existing infrastructure.
What is the final call to action presented in the script regarding the Weidmüller tool?
-The script encourages trying out the Weidmüller tool for oneself to experience the simplification and acceleration of machine learning deployment in the industry.

Outlines

00:00

🤖 Machine Learning Solutions for Industry

The script introduces the benefits of machine learning for machine builders and manufacturing companies, highlighting how it can offer new data-based services and increase productivity. It emphasizes that machine learning requires data and tests to be effective. The focus then shifts to presenting Weidmüller's machine learning solutions, which are designed to be user-friendly and do not require data science knowledge. The tool allows users to import machine or process data, identify normal and abnormal behavior, and create models based on domain knowledge. Users can select the most suitable model based on performance and plausibility, and then apply it directly, either in the cloud or on-premises. The tool also helps in avoiding anomalies and machine failures by leveraging domain knowledge to continuously improve model performance. The script concludes by stating that Weidmüller's solution makes it easy for machine builders to offer new data-based services and for manufacturing companies to enhance machine productivity and monitor product quality efficiently. The tool is intuitive and can be easily integrated into existing infrastructure, making machine learning more accessible than ever before.

Mindmap

Keywords

💡Machine Learning

Machine Learning is a subset of artificial intelligence that provides systems the ability to learn and improve from experience without being explicitly programmed. In the context of the video, Machine Learning is presented as a transformative technology for machine builders and manufacturing companies, offering new data-based services and higher productivity levels. The script emphasizes that machine learning solutions from Weidmüller enable companies to leverage machine learning capabilities without requiring in-depth data science knowledge.

💡Data Science

Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data. The video mentions that the Weidmüller tool provides machine learning competencies to the enterprise, eliminating the need for the team to have any knowledge in data science. This implies that the tool simplifies the process of leveraging data for insights, making it accessible to non-experts.

💡Productivity Level

Productivity Level refers to the efficiency of production in terms of output per unit of input. The script suggests that machine learning can significantly enhance productivity levels within manufacturing environments, which is a key benefit for producing companies looking to optimize their operations.

💡Weidmüller

Weidmüller is a company that is mentioned in the script as providing machine learning solutions. The video highlights that their tool allows for the easy implementation of machine learning models without the need for extensive data science knowledge, which is a significant advantage for companies looking to adopt advanced technologies.

💡Machine or Process Data

Machine or Process Data refers to the information collected from machines or processes within an industrial setting. The script indicates that users can import their machine or process data into the Weidmüller tool, which then helps in identifying normal and abnormal behavior patterns, a crucial step in applying machine learning for predictive maintenance and process optimization.

💡Domain Knowledge

Domain Knowledge is the specific information and expertise that is unique to a particular area or industry. The video script mentions that the tool uses the company's domain knowledge to create a selection of models, emphasizing the importance of industry-specific insights in tailoring machine learning solutions to meet specific needs.

💡Model Performance

Model Performance refers to how well a machine learning model operates in terms of accuracy, speed, and reliability. The script explains that users can select the appropriate model based on criteria such as model performance, indicating that the tool allows for the evaluation and comparison of different models to find the best fit for their application.

💡Anomalies

Anomalies are unusual patterns or deviations from the norm that can indicate potential issues or faults. In the context of the video, the ability to recognize anomalies is a key feature of the machine learning tool, which helps in avoiding process or machine failures by identifying problems before they become critical.

💡Cloud

Cloud computing refers to the delivery of various computing services, including storage, processing, and software, over the internet. The script mentions the flexibility of deploying the machine learning model either in the cloud or on-premises, highlighting the tool's adaptability to different operational environments and preferences.

💡Intuitive Tool

An Intuitive Tool is one that is easy to understand and use without requiring extensive training or prior knowledge. The video emphasizes the intuitive nature of the Weidmüller tool, suggesting that it can be easily integrated into existing infrastructure and used without additional training, making the adoption of machine learning more accessible.

💡Infrastructure Integration

Infrastructure Integration refers to the process of incorporating new technologies or systems into an existing infrastructure. The script points out that the Weidmüller tool can be easily integrated into a company's infrastructure, which is important for seamless adoption and operation of machine learning solutions within a manufacturing context.

Highlights

Machine learning offers numerous advantages for machine builders and manufacturing companies.

It provides new data-based services and enables a higher level of productivity.

Machine learning always requires data and tests.

We would like to introduce the Weidmüller machine learning solutions.

This solution does not require any knowledge of data science from your team.

The tool provides machine learning competencies needed for your business.

You simply import your machine or process data.

The tool then identifies normal and abnormal behavior based on domain knowledge.

It creates a selection of models based on your inputs.

You can then select the appropriate model based on criteria such as model performance and plausibility.

The model can be directly implemented very flexibly and independently of the platform.

You can use your model in the cloud or on-premises.

The model now provides information that allows you to detect anomalies and avoid process or machine failures.

You can use your domain knowledge to continuously improve the performance of the model.

This Weidmüller solution enables machine builders to offer new data-based services without the need for data science tests.

Manufacturing companies can thus increase the productivity of their machines and efficiently monitor product quality.

The best thing is that the tool is intuitive and easy to operate without additional training.

It allows for easy integration into your infrastructure.

Machine learning has never been so easy in the industry.

The tool accelerates and facilitates the use of ML in the industry.

Try it out for yourself today.