We are far from having dangerous #AGI, and while progress is being made towards the goal, we are nowhere close to #LLM networks developing consciousness or agency to do bad things or eradicate humanity (yet), but..
AI
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Governing Generative AI: Understanding Algorithm Governance Challenges
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Governance of algorithms is not a new idea, but given the rapid availability of these easy to use Generative AI tools like #ChatGPT . but how to govern these is largely misunderstood & misconstrued.
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Friday Musings on AI Governance Trends and Stakeholders
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There has been a lot of recently activity on #governing #ai from academics, to industrialists to governments. Some Friday musings on this hot topic. A thread :
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AI Paradigm Shift: Building Open, Collaborative and Values-Driven Tech
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The paradigm shift to AI is the opportunity for new generations to take the lead and reinvent the rules. Let’s not compromise on our ideals and make tech more open, collaborative, inclusive and values-driven!
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Data Science ML AI Analytics Resources Follow Guide
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End of this thread! If you are looking to learn more about
Data Science
ML/DL/AI
Analytics
Math & Statistics
Resources
MLOps Then, Don't forget to follow me at @avikumart_ for upcoming posts -
Full Lifecycle ML Model Automation and Real-Time Redeployment
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4) Automated deployment
5) Full lifecycle automation (Including retraining and redeployment of ML models in real-time) Learn more here https://
learn.microsoft.com/en-us/azure/ar
chitecture/example-scenario/mlops/mlops-maturity-model
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MLOps Maturity Model: Five Levels of Production Lifecycle
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2) MLOps maturity model There are 5 levels of the MLOps lifecycle in production 1) No MLOps (Static model deployment, used for POC)
2) DevOps but NO MLOps
3) Automated training (Cont…) -
ML Model Monitoring Performance Degradation Retraining
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– Monitoring Monitoring ML models in production is super important to track performance in real time. If the performance of models degrades it requires retraining and diagnosis to update the quality of ML models in production
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ML Pipeline Orchestration for Automated MLOps Deployment
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– Orchestration and ML pipelines This includes converting ML code in modular components to create a connected pipeline for smother deployment and maintenance Pipelines also enable automated training and deployment for the MLOps lifecycle
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Deploying AI Model Pipelines on Cloud Platforms
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– Deployment Deploying the model pipeline on cloud-based platforms like AWS, GCP, etc.