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How Creating Training-ready Datasets Faster Can Unleash ML Teams’ Productivity

DagsHub

ML teams have a very important core purpose in their organizations - delivering high-quality, reliable models, fast. With users’ productivity in mind, at DagHub we aimed for a solution that will provide ML teams with the whole process out of the box and with no extra effort. Let’s explain how the Data Engine helps teams do just that.

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Getir end-to-end workforce management: Amazon Forecast and AWS Step Functions

AWS Machine Learning Blog

Amazon Forecast is a fully managed service that uses machine learning (ML) algorithms to deliver highly accurate time series forecasts. In this post, we describe how we reduced the modelling time by 70% by doing the feature engineering and modelling using Amazon Forecast.

AWS 129
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MLOps and the evolution of data science

IBM Journey to AI blog

Both computer scientists and business leaders have taken note of the potential of the data. Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. MLOps is the next evolution of data analysis and deep learning. What is MLOps?

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Active Learning with Domain Experts - A Case Study on Working with Dentists on Machine Learning

DagsHub

Historically, implementing an active learning pipeline has been something only within reach for corporations with large budgets for ML and MLOps teams. As reference, you can see a complete active learning pipeline in a Jupyter Notebook , created using Data Engine in the Tooth Fairy project. What is Active Learning?

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Definite Guide to Building a Machine Learning Platform

The MLOps Blog

From gathering and processing data to building models through experiments, deploying the best ones, and managing them at scale for continuous value in production—it’s a lot. As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale.

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Data science vs. machine learning: What’s the difference?

IBM Journey to AI blog

Data science solves a business problem by understanding the problem, knowing the data that’s required, and analyzing the data to help solve the real-world problem. What is machine learning? It requires data science tools to first clean, prepare and analyze unstructured big data.

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10 Can’t-Miss Sessions on Language Models Coming to ODSC West 2023

ODSC - Open Data Science

During this tutorial, you’ll learn about the practical tools and best practices for evaluating and choosing LLMs. To attend these and many more expert-led sessions on LLMs, Generative AI, Machine Learning, NLP, Deep Learning, Data Engineering, and more, join us at ODSC West in just a few weeks. Sign me up!