Remove project-use-case deep-learning-for-time-series-forecasting
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How to get datasets for Machine Learning?

Knowledge Hut

Datasets play a crucial role and are at the heart of all Machine Learning models. Machine Learning without data sets will not exist because ML depends on data sets to bring out relevant insights and solve real-world problems. Quality data is therefore important to ensure the efficacy of a machine learning model.

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Druid Deprecation and ClickHouse Adoption at Lyft

Lyft Engineering

Introduction At Lyft, we have used systems like Apache ClickHouse and Apache Druid for near real-time and sub-second analytics. Sub-second query systems allow for near real-time data explorations and low latency, high throughput queries, which are particularly well-suited for handling time-series data.

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Data Engineering Weekly #158

Data Engineering Weekly

Visit rudderstack.com to learn more. Survey Link: [link] Tristan Handy: Is the "Modern Data Stack" Still a Useful Idea? Is Modern Data Stack still a useful idea? It leads to hiring more developers and more lead time to produce value. Editors Note: A Survey on the current state of Data Products.

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How DoorDash Built an Ensemble Learning Model for Time Series Forecasting

DoorDash Engineering

In real-world forecasting applications , it is a challenge to balance accuracy and speed. We explore a number of models and configuration combinations at DoorDash to forecast demand on our platform. However, the challenge is to build a forecast model that is highly accurate while also running fast and cheap.

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Data News — Week 23.35

Christophe Blefari

I spent a few hours working on the fancy data stack project and articles are in the works, but it was idealistic to produce quality code and content while enjoying the summer. Like wine, it takes time to get it right. This time Instacart bills have been overlooked. He covers everything. Answering " Why did the KPI change? "

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Top 6 Data Science Projects in Python

Knowledge Hut

Whether you are a student or a working professional, the journey to be an industry-ready Data Scientist would never be fulfilled unless there is a good portfolio of projects considerably demonstrated in the resume. Choosing the right project is crucial as it could set you apart from the other Data Scientists. to detect anomalies.

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Time Series Forecasting: What, Why, and, How?

ProjectPro

This blog introduces the concept of time series forecasting models in the most detailed form. Next, you will find a section that presents the definition of a time series forecasting article. After that, you will explore popular time-series-forecasting models.