Generating reproducible outputs in OpenAI models.
A common challenge encountered when using GPT models in applications is the lack of determinism in certain situations.
If you seek deterministic outputs from GPT models, here's a useful trick to consider:
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Exciting news! 🚨
The ChatGPT API is now available!
• ChatGPT has been out for a while, accessible through web playground, and a plus subscription for priority access.
• Now, developers can leverage the API to build powerful applications.
Here's how to use it in Python ↓
Neural Networks as Linear Combinations.
• NNs are multiple linear methods working in combination, but the power of non-linearity is what makes the difference.
• Let's see how many lines it'd take you to separate these two groups?
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Who are the people in MLOps?
Data Science and MLOps are constantly evolving and have multiple roles and people.
Not only Data Scientists but many other people are also there in the ML Model lifecycle.
But who are they and what do they do?
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1. Subject Matter Experts
Data scientists may not have a deep understanding of the businesses and domains they work for.
SMEs help them frame the problem better by working alongside data scientists.
They have defined goals, business questions and KPIs.
MLOps for Risk Mitigation
• In machine learning systems there are risks associated with an organization.
• Proper risk assessment and mitigation are important to be done.
Need for Machine Learning Pipelines
• Machine learning pipelines are crucial for sustainable machine learning systems.
• But what good do they bring? What are the benefits that we get from them?
Let's discuss ↓
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"Learn SQL"
Great advice no doubt.
• But what topics to cover?
• Which SQL database to use?
• What resources to learn from?
Here's is a track you can follow ↓
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