Introducing Streamlit Sharing. Deploy, manage, and share your Streamlit apps for free
Machine learning and data science code is easy to share but hard to use. GitHub overflows with models, algorithms, and datasets. But code is static. Can you play with the models? See the algorithms? Interact with the data? Doing so requires following complex instructions, installing packages, or reading dense code snippets. Frustrated by this, we decided that we need a simple, sharable “play” button for machine learning code.
There are two challenges here. The first is creating apps that make data science and machine learning code interactive. The second is sharing these apps so that the world can experience your work.
A year ago, we addressed the first challenge — creating — *by releasing Streamlit, an open-source library that lets you transform Python scripts into interactive apps. Streamlit lets you easily demonstrate algorithms, play with models, manipulate data, and combine all of these superpowers into beautiful apps. The response has been tremendous. We just crossed our millionth download. Hundreds of thousands of Streamlit apps have been created all over the world. *But creating great apps only solves half the problem.
Deploy a Machine Learning Model | Data Science | Machine Learning . I will train and Deploy a Machine Learning Model using Flask step by step. I will first train a model, then I will work to serve our model, and at the end I will deploy our machine learning model.
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.