The people at iterative.ai are special.
Its hard to describe quickly, but if you're a potential client or employee--you could easily go your entire career unaware that groups like this exist.
Their tools (like DVC) are exceptional, but I write this review because one need only interact with the people there to understand why they're execptional.
The culture there is one that can only exist when the founding talent is top-tier. The experience you'll have, though, is so much more than that.
Recommend whole-heatedly.
Based on our record, Scikit-learn should be more popular than Iterative.ai. It has been mentiond 31 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python,... - Source: dev.to / 4 months ago
Scikit-learn (optional): Useful for additional training or evaluation tasks. - Source: dev.to / 6 months ago
How to Accomplish: Utilize data splitting tools in libraries like Scikit-learn to partition your dataset. Make sure the split mirrors the real-world distribution of your data to avoid biased evaluations. - Source: dev.to / 12 months ago
Online Courses: Coursera: "Machine Learning" by Andrew Ng EdX: "Introduction to Machine Learning" by MIT Tutorials: Scikit-learn documentation: https://scikit-learn.org/ Kaggle Learn: https://www.kaggle.com/learn Books: "Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman By... - Source: dev.to / about 1 year ago
Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy. - Source: dev.to / almost 2 years ago
PyDrive2 is am open-source python package maintained by the awesome people at Iterative. And it is very easy to install:. - Source: dev.to / over 2 years ago
These three are made by Iterative.ai, and seem like very clean implementations of MLOps tooling - especially if you aren't dealing with massive data. https://iterative.ai/. Source: over 2 years ago
For what it's worth. (Full disclosure: I'm the community manager at Iterative (DVC,et.al.) Just wanted to make you aware of our online course (free) that we created specifically for Data Scientists (https://learn.iterative.ai). We know that bridging the gap between prototype to production/ jupyter notebook to reproducible/software engineering compatible, is a challenge. That's why we created the course. To also... Source: almost 3 years ago
What do you think of iterative.ai tools like dvc or cml? I have no direct experience, but I am looking at setting up something similar to what you need for a personal project. Source: almost 3 years ago
Hey all, we (at iterative.ai) are launching TPI - Terraform Provider Iterative https://github.com/iterative/terraform-provider-iterative. Source: about 3 years ago
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Algorithmia - Algorithmia makes applications smarter, by building a community around algorithm development, where state of the art algorithms are always live and accessible to anyone.
OpenCV - OpenCV is the world's biggest computer vision library
MCenter - Machine Learning Operationalization
NumPy - NumPy is the fundamental package for scientific computing with Python
neptune.ai - Neptune brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed and shared with others. Works with all common technologies and integrates with other tools.