Home Business Insights & Advice Six best practices for training AI models in manufacturing

Six best practices for training AI models in manufacturing

by John Saunders
28th Mar 22 12:22 pm

The manufacturing industry has one common goal: to produce more in a short time and at the minimal cost possible. Achieving this goal makes the industry grow as it helps them cut production costs, increase sales, and generate more revenue. However, relying on traditional manufacturing processes will make the said goal challenging to achieve. Also, depending on human intelligence and labor to increase production also increases the chances of errors.

Thankfully, artificial intelligence (AI) and machine learning (ML) models have been deployed in manufacturing. One of the main reasons industries implements AI is to have better visibility of operations and identify areas that need improvement. So, AI has become core in the manufacturing industry, which is delivering more productivity.

Even with such benefits, training AI models in manufacturing can be challenging and, if done wrong, won’t yield any meaningful result. To avoid this problem, here are some of the best practices you should follow:

1. Data annotation

While AI learns to perform operations and improves over time, it needs to be told what to do. Your computer won’t process visual information like you, so data annotation should be one of your first steps when training AI models.

Data Annotation is the categorising and labeling of data, such as text or audio in ML, to create a connection. This process uses an annotation tool to label content, allowing ML models to recognise them and make predictions. (1)

Data annotation is important in manufacturing as large data sets are collected each day. The data could be about production, sales, raw materials, or inspection reports. Each data set needs to be correctly labeled so your AI model can learn from and perform properly. The annotation tools can help discard non-existent data and ensure that the data is accurate. The AI model you created in your manufacturing plant will then understand the patterns and make decisions that can increase productivity in the industry, reduce errors, and save productions costs. (1)

2. Data assessment

The AI models you develop for your manufacturing industry depend so much on the type of data you have. So, you should first assess if you have the correct data or not, if the information is enough, and if you can get the data fast enough. If you have enough data, you’ll have easy access to a variety of data required to set up and test different models. (3)

Also, if you can collect the data fast enough, it makes it easier for your model to make accurate predictions. Even when you’ve collected enough data, the data scientists should experiment to ensure that the data collected contains the information needed to bring the desired manufacturing or business change. If the data is correct and big enough, it should be cleaned, structured, processed, and cataloged for future reference. (3)

3. Start with small data sets

Another great way to train your AI is by overfitting. This is a process of training an AI model using small data sets then evaluating the model based on this performance. The main advantage of this practice is that it’s easier to train models based on a small data set and improve over time instead of starting with a large set. Also, it’ll serve as a test that ensures the training pipeline is working correctly. (2)

During this test, you can identify any problem from the model and correct it before feeding more data into it. It also helps you test different models to identify the one that suits your manufacturing needs the most before moving forward. In the end, you’re more likely to get a high-performance AI model that’ll help increase efficiency in your industry. (2)

4. Assess your IT infrastructure

Artificial intelligence and machine learning are relatively new concepts for many organizations. So, there’s a high chance that your industry will use outdated systems, making it hard to implement AI. Other than the traditional methods, your company could also lack the right skill set to develop, improve, and deploy the models to suit your industry’s needs. When operating under this environment, it’s easier for the AI systems to stall in the training pipeline. (4)

The best way to ensure this doesn’t happen is by assessing your IT infrastructure. The assessment can be done by conducting tests on the technology’s validity. The tests should include the IT infrastructure you already have and others you want to acquire for your manufacturing business. After the tests are completed, you can then choose the right technology to be used. (3)

The technology you’ll choose should be tested against scalability and cloud readiness, stability, business use case, and any future scenario that may require its usage. Generally, the technology should make ML easier. (3)

5. Determine use cases

Another great way to ensure you train your AI models in manufacturing is determining the use case. A use case is a methodology used to identify, organize, and clarify a system requirement. In manufacturing, the priorities of one company may not be the same as the other company. However, there are some simple use cases, such as developing smarter products and services as well as making productions more intelligent. (4)

You can use your competitors’ methods or consult with stakeholders on what use cases to have for your AI models. This will help you reduce the risk of failure during the deployment process and ensure that it links to your company’s goals. Moreover, consulting with stakeholders will help drive a quick adoption of the AI models in the company. (4)

6. Monitor the model

After deploying a successful training model in your manufacturing process, you should remember to track and monitor it. The monitoring process includes reporting results, such as the success or failure of the model in real-time. The test results should then be sent back to the development teams to decide which variant requires underweighting or overweighting. When the training model is adequately monitored, the production will stabilize. (3)

Conclusion

The manufacturing industry is one of the top beneficiaries of AI and ML. AI can help cut costs, improve production and efficiency, and test products before being launched to the market. However, every model should be trained and tested before being used in production. The practices discussed in this article can help achieve that.

References

  1. “What is data annotation and why does it matter?”, Source: https://www.telusinternational.com/articles/what-is-data-annotation
  2. “Best Practices for Training ML & AI Models for Manufacturing”, Source: https://www.vanti-analytics.com/best-practices-for-training-ml-ai-models-for-manufacturing/
  3. “5 Best Practices for Putting Machine Learning Models Into Production”, Source: https://www.kdnuggets.com/2020/10/5-best-practices-machine-learning-models-production.html
  4. “7 Best Practices for Implementing AI”, Source: https://www.processmaker.com/blog/7-best-practices-for-implementing-ai/

 

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