🚛✨ CloudTrucks leverages Qwak's advanced machine learning platform to power their Personalized Load Recommendation System, P-REX. 🦖 By centralizing data, streamlining feature engineering, and accelerating model deployment, they achieved a 12% increase in booking rates and impressive user engagement. Read Cloudtrucks' full overview here: https://lnkd.in/dgSPi7NS
Qwak (Acquired by JFrog)’s Post
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Smart Traffic Optimization using Machine Learning
Smart Traffic Optimization using Machine Learning
https://techearth.co.in
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Smart Traffic Optimization using Machine Learning
Smart Traffic Optimization using Machine Learning
https://techearth.co.in
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🚀 Exciting News! 🚀 I'm thrilled to announce the completion of my latest project: #End_to_End #AutoML journey powered by #PyCaret and #TPOT! With #PyCaret user-friendly interface simplifying the ML pipeline and #TPOT's advanced #hyperparameter optimization, I achieved remarkable efficiency and accuracy in #model development. From #data #preprocessing to #model selection, automation played a pivotal role, allowing me to focus on extracting valuable insights. Excited to share more about the transformative power of automation in data science! Let's connect and discuss how #AutoML can revolutionize your #projects. For complete Project visit #Github: https://lnkd.in/dcAMScsa Special Thanks to Hussain Shahbaz Khawaja atomcamp #DataScience #AutoML #PyCaret #TPOT #MachineLearning #AI #Innovation #DataScience #MachineLearning #AutoML #PyCaret #TPOT #ArtificialIntelligence #Innovation #DataDriven #LinkedInPost
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While data scientists are often taught about training a machine learning model, building a reliable MLOps strategy to deploy and maintain that model can be daunting. It doesn’t have to be this way! Join us with Julia Silge -- Data Scientist & Engineering Manager at Posit - to learn how Posit Team provides fluent tooling for the whole ML lifecycle. How to develop and deploy a machine learning model with Posit. 1. Develop an ML model using Posit Workbench and a recent Tidy Tuesday dataset! 2. Version, deploy, and monitor that model with Posit Connect 3. Maintain reproducible software dependencies throughout the ML lifecycle with Posit Package Manager https://lnkd.in/gQPUyhK7
How to develop and deploy a machine learning model with Posit
https://www.youtube.com/
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In this post, Samuel Flender takes a deep dive into one modeling approach that has enabled the industry to build superior recommender systems: the two-tower model, where one tower learns relevance and another (shallow) tower learns biases.
The Rise of Two-Tower Models in Recommender Systems
towardsdatascience.com
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Data Science | Machine Learning | Deep Learning | MLOPS | Natural Language Processing |Reinforcement Learning | React Js | Mongo | SQL | Java | Java Script | Python | C
📱💡 Excited to share my latest project: Mobile Price Prediction using Machine Learning! 🚀 With 80 columns of data, I dove deep into feature engineering to streamline it down to just 20 key features. The result? A fantastic R2 score that reflects the accuracy of our predictions! 🛠 Check out the code and contribute on GitHub: https://lnkd.in/g7iVWC9H 📊 Want to see it in action? Explore our Streamlit application for a hands-on experience: https://lnkd.in/gjDPW7Nw 👏 Special thanks to my amazing contributors and friends who helped make this project possible Animesh Kumar Singh ANIKET KUMAR Rajiv Lochan Dash Anjali Singh! Your insights and support were invaluable. Let's keep pushing the boundaries of what's possible with ML! #MachineLearning #MobilePricePrediction #DataScience 🤖🔍
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AI and ML have a huge role in improving strategy and its execution IFF you have good, prepared data. This example looks at how we’ve used it to identify cross-org dependencies and essential collaboration on achieving org and team OKRs. #ai #ml #okrs #strategytoexecution
As machine learning models become more central to our operations, vectorization allows them to handle high-dimensional data more efficiently and unlocks additional benefits at lower costs. Here's a technical overview of how we implemented it at WorkBoard to find semantically similar Key Results. Whether you're new to machine learning or an experienced practitioner, I think you'll find this vectorization post worth a read. It aims to explain the key ideas in an accessible way. https://lnkd.in/gKVBfD_z
Surfacing Impactful Results Insights with GenAI and Semantic Search
workboard.com
