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What do you do if machine learning algorithms in process automation systems are not accurate or reliable?

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When integrating machine learning (ML) algorithms into process automation systems, it's crucial to ensure their accuracy and reliability. If you're facing issues with ML performance, it can lead to inefficiencies, errors, and potentially costly consequences. The challenge is to identify the root cause and take corrective actions to enhance the system's effectiveness. This article will guide you through the steps you need to take to address and rectify the problem, ensuring that your process automation leverages ML algorithms to their fullest potential.

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Jacob Sam

Intelligent Automation|AI|Solution Architect

1 Assess Data

The first step is to scrutinize the data feeding into your machine learning algorithms. Poor data quality is often the culprit behind inaccurate ML predictions. Make sure your data is complete, relevant, and properly preprocessed. Look for issues like missing values, outliers, or biased data that could skew your algorithm's learning process. Cleaning and normalizing your data can significantly improve the performance of your ML models in process automation.

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Jacob Sam

Intelligent Automation|AI|Solution Architect
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An efficient way to do data clean up is to use techniques like local outlier factor, fuzzy logic for detection of duplicate documents.I would suggest to try a skewed set for small volume to check if the bases are working as expected and then scale up

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2 Review Models

Review your machine learning models to ensure they are well-suited for the task at hand. Sometimes, the chosen model may not be complex enough to capture the nuances of your data, or it might be too complex, leading to overfitting. Consider experimenting with different models or adjusting hyperparameters. Model validation techniques, such as cross-validation, can help you determine if your model generalizes well to new data.

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Jacob Sam

Intelligent Automation|AI|Solution Architect
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An example i have seen is invoice and purchase order models it is a very thin line between PO and invoice we should be careful and do lot of analysis on a wide variety of samples before conclusion.

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3 Upgrade Training

Enhancing the training process can lead to more accurate machine learning outcomes. This might involve increasing the size or diversity of the training dataset to better represent the complexities of the real-world processes you're automating. You could also explore more sophisticated training techniques or algorithms that are designed to handle the specific challenges presented by your data.

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4 Monitor Performance

Ongoing monitoring of machine learning algorithm performance is crucial. Set up a system to track accuracy, precision, recall, and other relevant metrics over time. If these metrics start to decline or do not meet your standards, it's a signal that your ML algorithms may need retraining or that the process they are automating has changed and the models need to adapt.

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Jacob Sam

Intelligent Automation|AI|Solution Architect
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Periodic revaluation of your benchmarks like precision score, recall score etc .is important to understand both model performance as well as the quality of data coming in.Regularization is. good method to prevent overfitting. These methods introduce penalties for model complexity, encouraging the model to learn generalizable patterns.

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5 Iterate Quickly

In the world of machine learning and process automation, iteration is key. If an algorithm is underperforming, don't hesitate to go back to the drawing board. Quick iterations allow you to test changes and integrate feedback effectively. This agile approach can lead to rapid improvements in algorithm performance and process efficiency.

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6 Seek Expertise

If you've tried the above steps and still struggle with ML algorithm accuracy, it might be time to consult with experts in the field. Data scientists and ML engineers can provide deeper insights into complex issues and help you devise strategies that are beyond basic troubleshooting. Their expertise can be invaluable in optimizing your process automation systems.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What do you do if machine learning algorithms in process automation systems are not accurate or reliable?

1

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3

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1 Assess Data

2 Review Models

3 Upgrade Training

4 Monitor Performance

5 Iterate Quickly

6 Seek Expertise

7 Here’s what else to consider

Process Automation

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What do you do if machine learning algorithms in process automation systems are not accurate or reliable?

1

2

3

4

5

6

7

1 Assess Data

2 Review Models

3 Upgrade Training

4 Monitor Performance

5 Iterate Quickly

6 Seek Expertise

7 Here’s what else to consider

Process Automation

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