#mlworkflow resultados da pesquisa
ML ≠ just algorithms!❌ 80% of ML work is in Data Understanding & Data Preprocessing. Algorithms (Training) are only 20%. Master the full 8-step workflow from Problem Definition to Model Deployment to create a perfect project! #MachineLearning #DataScience #MLWorkflow #AI
Data cleaning isn’t glamorous, but it's 80% of the job. Fix these rookie errors and your models will thank you later. #DataCleaning #datascience #mlworkflow #datapreprocessing #zell #MachineLearning #dataquality
7/ If you care about performance, FastSet isn’t optional—it’s essential. Built by @Pi_Squared_Pi2. Used by people who want results. #FastSet #MLworkflow #AItools #FeatureEngineerin
The development workflow is the same as 20 years ago. Your expensive GPUs stay idle, and complex workflows for more compute. Learn why this happens and how to fix that: velda.io/blog/why-stuck… #cloudcomputing #mlworkflow #gpu #cloudcost #mlplatform #ai #mlinfra #costsaving #ai
velda.io
Why AI/ML Researchers Are Stuck with Inefficient GPU Setups (And How to Fix It)
AI/ML researchers are stuck with inefficient GPU setups that limit productivity and increase costs. Learn how Velda provides instant access to scalable compute without the complexity.
In practice, data preparation and cleaning often consume up to 80% of a machine learning project’s timeline. Model development is only a fraction of the work — data is the foundation. #DataScience #MLWorkflow
Manual + KFold + StratifiedKFold on Iris & Digits Generalization power = Cross-Validation > Train-Test Used cross_val_score for clean validation Generalization power = Cross-Validation > Train-Test #MachineLearning #MLWorkflow #BiasVariance #CrossValidation
Export the winner Deploy like a boss No manual tweaking. No wasted cycles. Just clean, optimized features—ready to roll. Props to @Pi_Squared_Pi2 for building a beast. 🧠💥 #FastSet #MLworkflow #AItools
🤖 Master the Machine Learning Workflow! From data acquisition to continuous monitoring—automate your ML process for better accuracy and efficiency. 👉 Explore the full workflow now! #MachineLearning #MLWorkflow #DataScience #AI #Automation #TechSolutions #ML
Focus on results, not grunt work Athina AI streamlines the AI development process, letting your team focus on what matters—creating impact. #AItools #MLworkflow 🔗 buff.ly/41Plo1t
Jaiganesh Prabhakaran shares his great agenda for today's talk! #MLOpsSalon #MLWorkflow #Kubeflow #ModelDB
🎥 Have you seen MLT's latest Data and Analytics video with Wilco van Ginkel? Click the link to view 🔗youtube.com/watch?v=Lua8_2… #MachineLearning #MLworkflow
🔍 How does an ML model go from raw data to real-world impact? Let me take you on a 10-step journey — from idea to deployment! 🚀 This is the real workflow Data Scientists & ML Engineers follow. 🧵👇 #MLWorkflow #DataScience
🧠 From Data to Deployment – Here’s how AI Magpie builds smart ML solutions step-by-step: 📊 Data Collection → Preprocessing → Model → Evaluation → Training → Deployment 🔗 aimagpie.com #MachineLearning #AI #MLWorkflow #SmartSolutions #AIMagpie #AIinBusiness
Take this FREE on-demand course – learn the basic concepts of #AI and #ML and how to build ML projects that maximize business results. #MLworkflow hpe.to/6014H9vTn
When reducing data dimensionality, what’s your go-to technique? 🔘 PCA 🔘 Feature Selection (e.g. RFE, SelectKBest) 🔘 Autoencoders 🔘 Depends on the dataset Vote & comment why 👇 #DataPreprocessing #MLWorkflow #PCA
6. Tools & Languages Wetin people dey use build ML: – Python (King for ML work) – Libraries: scikit-learn, TensorFlow, PyTorch – Data fit come from: CSV, Excel, database, web scraping #Python4AI #TechTools #MLWorkflow
Tired of complex Databricks deployments? Learn how Databricks Asset Bundles (DABs) simplify workflows with version control & CI/CD. Practical tips inside 🔗 okt.to/ztX7mO. #Databricks #DataEngineering #MLWorkflow #Xebia
ML ≠ just algorithms!❌ 80% of ML work is in Data Understanding & Data Preprocessing. Algorithms (Training) are only 20%. Master the full 8-step workflow from Problem Definition to Model Deployment to create a perfect project! #MachineLearning #DataScience #MLWorkflow #AI
In practice, data preparation and cleaning often consume up to 80% of a machine learning project’s timeline. Model development is only a fraction of the work — data is the foundation. #DataScience #MLWorkflow
7/ If you care about performance, FastSet isn’t optional—it’s essential. Built by @Pi_Squared_Pi2. Used by people who want results. #FastSet #MLworkflow #AItools #FeatureEngineerin
Export the winner Deploy like a boss No manual tweaking. No wasted cycles. Just clean, optimized features—ready to roll. Props to @Pi_Squared_Pi2 for building a beast. 🧠💥 #FastSet #MLworkflow #AItools
Data cleaning isn’t glamorous, but it's 80% of the job. Fix these rookie errors and your models will thank you later. #DataCleaning #datascience #mlworkflow #datapreprocessing #zell #MachineLearning #dataquality
⚡️ Why is FastSet a game-changer for data scientists? Let’s break down the key benefits in this thread 👇 @Pi_Squared_Pi2 #FastSet #MLworkflow #DataScience
I rely on Perplexity for deep research and Comet for managing my ML projects. Both save time and keep my workflow organized. Highly recommended! #AIDaily #MLWorkflow
Manual + KFold + StratifiedKFold on Iris & Digits Generalization power = Cross-Validation > Train-Test Used cross_val_score for clean validation Generalization power = Cross-Validation > Train-Test #MachineLearning #MLWorkflow #BiasVariance #CrossValidation
🧠 From Data to Deployment – Here’s how AI Magpie builds smart ML solutions step-by-step: 📊 Data Collection → Preprocessing → Model → Evaluation → Training → Deployment 🔗 aimagpie.com #MachineLearning #AI #MLWorkflow #SmartSolutions #AIMagpie #AIinBusiness
Data scientists, unlock true agility with @FractionAI_xyz. This platform simplifies complex feature engineering and model training workflows, empowering faster experimentation and superior results. #DataScience #MLWorkflow
Data scientists, unlock true agility with @FractionAI_xyz. This platform simplifies complex feature engineering and model training workflows, empowering faster experimentation and superior results. #DataScience #MLWorkflow
Training an AI model involves data, algorithms, and iteration. This guide explains the full process clearly. #TrainAI #MLworkflow #SmartModels andrewroche.ai/ai-model-train…
💡 Plus: a full Titanic dataset case study, clean Python code, and tips on avoiding common pitfalls. 🚀 Dive in: buff.ly/0yatT3e #PythonDataScience #MLworkflow #EDA
When reducing data dimensionality, what’s your go-to technique? 🔘 PCA 🔘 Feature Selection (e.g. RFE, SelectKBest) 🔘 Autoencoders 🔘 Depends on the dataset Vote & comment why 👇 #DataPreprocessing #MLWorkflow #PCA
Tired of complex Databricks deployments? Learn how Databricks Asset Bundles (DABs) simplify workflows with version control & CI/CD. Practical tips inside 🔗 okt.to/ztX7mO. #Databricks #DataEngineering #MLWorkflow #Xebia
PHP works great with APIs. Train your model in Python, call it from PHP. Or use services like TensorFlow.js or OpenAI APIs to make your PHP apps AI-powered without full ML stacks. #PHP #MLworkflow
ML ≠ just algorithms!❌ 80% of ML work is in Data Understanding & Data Preprocessing. Algorithms (Training) are only 20%. Master the full 8-step workflow from Problem Definition to Model Deployment to create a perfect project! #MachineLearning #DataScience #MLWorkflow #AI
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