Iterative.ai
Algorithmia
Managed MLflow
neptune.ai
Spell
MCenter
Datatron
5Analytics
Databricks
Google BigQuery
Jupyter
Looker
Presto DB
Informatica
Rakam
Concurrent
Iterative.ai DatabricksThe 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, Databricks should be more popular than Iterative.ai. It has been mentiond 17 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.
PyDrive2 is am open-source python package maintained by the awesome people at Iterative. And it is very easy to install:. - Source: dev.to / about 1 year 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 1 year 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 2 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 2 years ago
Hey all, we (at iterative.ai) are launching TPI - Terraform Provider Iterative https://github.com/iterative/terraform-provider-iterative. Source: about 2 years ago
Dolly-v2-12bis a 12 billion parameter causal language model created by Databricks that is derived from EleutherAI’s Pythia-12b and fine-tuned on a ~15K record instruction corpus generated by Databricks employees and released under a permissive license (CC-BY-SA). Source: about 1 year ago
Global organizations need a way to process the massive amounts of data they produce for real-time decision making. They often utilize event-streaming tools like Redpanda with stream-processing tools like Databricks for this purpose. - Source: dev.to / almost 2 years ago
Databricks, a data lakehouse company founded by the creators of Apache Spark, published a blog post claiming that it set a new data warehousing performance record in 100 TB TPC-DS benchmark. It was also mentioned that Databricks was 2.7x faster and 12x better in terms of price performance compared to Snowflake. - Source: dev.to / almost 2 years ago
Go to Databricks and click the Try Databricks button. Fill in the form and Select AWS as your desired platform afterward. - Source: dev.to / about 2 years ago
I am considering Hex, Deepnote, and possibly Databricks. Does anyone have any experience using the first 2 (i have worked with Databricks in the past) and have thoughts they can share? The company isn't doing any fancy data science so far so I mostly want it for deep product analytics which I can turn into reports that are easily shareable across the org. That being said, I do want to get into statistical... Source: about 2 years ago
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.
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
Managed MLflow - Managed MLflow is built on top of MLflow, an open source platform developed by Databricks to help manage the complete Machine Learning lifecycle with enterprise reliability, security, and scale.
Jupyter - Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.
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.
Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.