DATA

Pure Storage AI Infrastructure Solutions for Machine Learning Workloads

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20 May 2023 20h01

Smarter Infrastructure for #AI from @PureStorage => AI workloads process massive amounts of data from structured & unstructured sources. #PureStorage offers high-performance, architecturally optimized solutions: https://
purefla.sh/3AUwlQT
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#MachineLearning #BigData #StorageMatters

AI Data Pipeline: 5 Essential Processing Steps and Storage Importance

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20 May 2023 19h13

What is an #AI Data Pipeline? Why #StorageMatters: https://
purefla.sh/3oUEnGm by @PureStorage — 5 processing steps in AI Data Pipeline Lifecycle:
1-Ingestion
2-Cleaning
3-Exploration
4-Training
5-Deployment
+The importance of the right Data Platform for AI Pipelines!
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#PureStorage

40+ Exclusive Free Resources for Data Science and AI Learning

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20 May 2023 18h48

I just posted 40+ resources exclusively for my Twitter Subscribers, including a variety of FREE downloads that cover #DataScience #AI #ML #DeepLearning #MachineLearning #GenerativeAI #ChatGPT #Statistics #Python #SQL #DataViz #DataPrep #DataStorytelling — Subscribe to my Twitter

7 Essential Features for Enterprise Knowledge Management Solutions

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20 May 2023 18h00

The 7 features needed in an #EnterpriseKnowledge Management solution: https://
roboticsandautomationnews.com/2023/03/12/7-t
ools-that-one-should-search-for-in-knowledge-management-software/65201/
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The @Mindbreeze InSpire Insight Engine has it covered: https://
inspire.mindbreeze.com/knowledge-mana
gement?ls=22
…
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#AI #BigData #LinkedData #KnowledgeGraph #Knowledgebase #Automation #Semantic #NLProc #DeepLearning

Data Science ML AI Analytics Resources Follow Guide

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20 May 2023 16h42

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

MLOps Maturity Model: Five Levels of Production Lifecycle

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20 May 2023 16h30

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…)

Full Lifecycle ML Model Automation and Real-Time Redeployment

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20 May 2023 16h30

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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ML Model Monitoring Performance Degradation Retraining

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20 May 2023 16h30

– 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

ML Pipeline Orchestration for Automated MLOps Deployment

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20 May 2023 16h30

– 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