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Tag: Hyperparameter Optimization

Demand forecasting at Getir built with Amazon Forecast | Amazon Web Services

This is a guest post co-authored by Nafi Ahmet Turgut, Mutlu Polatcan, Pınar Baki, Mehmet İkbal Özmen, Hasan Burak Yel, and Hamza Akyıldız from...

Deliver your first ML use case in 8–12 weeks

Do you need help to move your organization’s Machine Learning (ML) journey from pilot to production? You’re not alone. Most executives think ML can...

Financial text generation using a domain-adapted fine-tuned large language model in Amazon SageMaker JumpStart

Large language models (LLMs) with billions of parameters are currently at the forefront of natural language processing (NLP). These models are shaking up the...

Domain-adaptation Fine-tuning of Foundation Models in Amazon SageMaker JumpStart on Financial data

Large language models (LLMs) with billions of parameters are currently at the forefront of natural language processing (NLP). These models are shaking up the...

Automation in Data Science Workflows

Machine learning solutions have already automated a large part of how the world used to operate and are looking after their own inefficiencies now....

Tutorial on MNIST Digit Classification Using ClearML

Introduction If you are a Data Scientist or MLOps Engineer, at some point, you would have faced problems tracking code, data, and models for different...

AI/ML-driven actionable insights and themes for Amazon third-party sellers using AWS

The Amazon International Seller Growth (ISG) team runs the CSBA (Customer Service by Amazon) program that supports over 200,000 third-party Merchant Fulfilled Network (MFN)...

Accelerate hyperparameter grid search for sentiment analysis with BERT models using Weights & Biases, Amazon EKS, and TorchElastic

Financial market participants are faced with an overload of information that influences their decisions, and sentiment analysis stands out as a useful tool to...

Tune ML models for additional objectives like fairness with SageMaker Automatic Model Tuning

Model tuning is the experimental process of finding the optimal parameters and configurations for a machine learning (ML) model that result in the best...

Make Quantum Leaps in Your Data Science Journey

Image from Unsplash Key Takeways Data science is a field that is constantly evolving In the field of data science, learning is lifelong A data science...

7 Best Tools for Machine Learning Experiment Tracking

Image by Author  5 years ago, data scientists and machine learning engineers used to store Machine Learning (ML) experiment data on spreadsheets, paper, or...

Identifying defense coverage schemes in NFL’s Next Gen Stats

This post is co-written with Jonathan Jung, Mike Band, Michael Chi, and Thompson Bliss at the National Football League. A coverage scheme refers...

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