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What are the best ways to monitor AI models on the cloud?

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If you are developing or deploying AI models on the cloud, you need to monitor their performance, reliability, and security. Monitoring AI models on the cloud can help you detect and fix issues, optimize resources, and ensure compliance. But how do you monitor AI models on the cloud effectively? Here are some of the best ways to monitor AI models on the cloud.

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1 Choose the right metrics

The first step to monitor AI models on the cloud is to choose the right metrics that reflect your goals and expectations. Depending on your use case and domain, you may want to measure metrics such as accuracy, precision, recall, latency, throughput, availability, scalability, cost, or fairness. You should also define thresholds and alerts for each metric to notify you when something goes wrong or needs attention.

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Guillermo Vazquez

Artificial Intelligence @ Meta
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Some useful metrics you want to include in a robust AI monitoring system: - Model Accuracy: The percentage of correct predictions made by the model. - Precision: The ratio of true positives to the sum of true and false positives. - Recall: The ability of a model to find all relevant cases within a dataset. - Latency: The time it takes for the model to make a prediction once it receives data. - Throughput: The number of requests processed per unit of time. - Availability: The percentage of time the AI service is operational and accessible. - Cost: The amount of cloud resources consumed by the model, translated into monetary value.

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2 Use cloud-native tools

The second step to monitor AI models on the cloud is to use cloud-native tools that are designed for the specific features and challenges of cloud computing. Cloud-native tools can help you collect, store, analyze, and visualize data from your AI models and their environments. For example, you can use tools such as AWS CloudWatch, Azure Monitor, or Google Cloud Monitoring to monitor your AI models on the cloud.

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?????? Advitya Gemawat

"25 under 25: Top Data Science Contributors and Thought-Leaders" ?? Influencer through Data ?? Operationalizing enterprise-grade Responsible AI tools ??
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Azure Machine Learning (AML) is an enterprise-grade ML service for the end to end ML lifecycle involving experimentation, versioning, training, parameter tuning, model monitoring, deployment, and even Responsible AI / AI safety features baked as first class citizens into the same service.

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3 Implement observability

The third step to monitor AI models on the cloud is to implement observability, which is the ability to understand the internal state and behavior of your AI models from the external outputs. Observability can help you identify and troubleshoot root causes of problems, as well as improve the quality and reliability of your AI models. To implement observability, you need to collect and correlate data from different sources, such as logs, metrics, traces, events, or feedback.

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Ivan Ralic

Co-Founder at Collabwriting - Building startups, teams and everything in between
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When it comes to LLMs, although I pretty much dislike LangChain because most of the tools are written very poorly, so in the end you end up writing your own tools from ground zero. LangSmith is pretty awesome for tracking analytics and costs. And it's pretty simple to implement. When it comes to AI Cloud Platforms, most of them already have metrics built-in so use it. And if you are building your custom solution, you really need to account for implementing observability from day 1 because GPUs are expensive ??

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4 Apply explainability

The fourth step to monitor AI models on the cloud is to apply explainability, which is the ability to understand and communicate the logic and reasoning behind your AI models' decisions and actions. Explainability can help you verify and validate your AI models, as well as enhance trust and transparency with your stakeholders and customers. To apply explainability, you need to use techniques such as feature importance, counterfactuals, or SHAP values.

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?????? Advitya Gemawat

"25 under 25: Top Data Science Contributors and Thought-Leaders" ?? Influencer through Data ?? Operationalizing enterprise-grade Responsible AI tools ??
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A lot of such tools are efforts are rather fragmented. Microsoft’s Responsible AI Toolbox (open source repo in GitHub) is like a Swiss-army knife for consolidating several RAI tools, other unique RAI capabilities, and operationalizing them under a unified view to better understand the model’s behavior and identify potential gaps / harms in the model’s performance.

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5 Incorporate security

The fifth step to monitor AI models on the cloud is to incorporate security, which is the ability to protect your AI models and their data from unauthorized access, modification, or damage. Security can help you prevent and mitigate risks, such as data breaches, malicious attacks, or model theft. To incorporate security, you need to use methods such as encryption, authentication, authorization, or anomaly detection.

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6 Update and retrain

The sixth step to monitor AI models on the cloud is to update and retrain your AI models regularly, based on the feedback and insights from your monitoring process. Updating and retraining your AI models can help you improve their performance, accuracy, and relevance, as well as adapt to changing conditions and requirements. To update and retrain your AI models, you need to use strategies such as data augmentation, model tuning, or transfer learning.

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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 are the best ways to monitor AI models on the cloud?

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2

3

4

5

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7

1 Choose the right metrics

2 Use cloud-native tools

3 Implement observability

4 Apply explainability

5 Incorporate security

6 Update and retrain

7 Here’s what else to consider

Artificial Intelligence

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What are the best ways to monitor AI models on the cloud?

1

2

3

4

5

6

7

1 Choose the right metrics

2 Use cloud-native tools

3 Implement observability

4 Apply explainability

5 Incorporate security

6 Update and retrain

7 Here’s what else to consider

Artificial Intelligence

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