In this post, we will cover how to train and deploy a machine learning model leveraging a scalable stream processing architecture for an automated text prediction use-case.
In this post, we will cover how to train and deploy a machine learning model leveraging a scalable stream processing architecture for an automated text prediction use-case. We will be using Sklearn and SpaCy to train an ML model from the Reddit Content Moderation dataset, and we will deploy that model using Seldon Core for real time processing of text data from Kafka real-time streams. This is the content for the talk presented at the NLP Summit 2020.
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For this use-case we will be using the Reddit /r/science Content Moderation Dataset. This dataset consists of over 200,000 reddit comments — primarily labelled based on whether the comments have been removed by moderators. We’ll be tasked to train an ML model that is able to predict the comments that would have been removed by reddit moderators.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
Learning is a new fun in the field of Machine Learning and Data Science. In this article, we’ll be discussing 15 machine learning and data science projects.
This post will help you in finding different websites where you can easily get free Datasets to practice and develop projects in Data Science and Machine Learning.
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
What is Standardization and why is it soo darn important? It’s possible that you will come across datasets with lots of numerical noise built-in, such as variance or differently-scaled data, so a good preprocessing is a must before even thinking about machine learning.