Google Cloud TPU
Scikit-learn
machine-learning in Python
Amazon Forecast
AWS Personalize
Qubole
python-recsys
BigML
Google BigQuery
Databricks
Looker
Jupyter
Presto DB
Google Cloud Dataflow
Amazon EMR
Rakam
Google Cloud TPU Google BigQueryBased on our record, Google BigQuery should be more popular than Google Cloud TPU. It has been mentiond 35 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.
According to https://cloud.google.com/tpu, each individual TPUv3 has 420 Teraflops, and TPUv4 is supposed to double that performance, so if that guess is correct, it should take a few seconds to do inference. Quite impressive really. - Source: Hacker News / about 2 years ago
You can also rent a cloud TPU-v4 pod (https://cloud.google.com/tpu) which 4096 TPUv-4 chips with fast interconnect, amounting to around 1.1 exaflops of compute. It won't be cheap though (excess of 20M$/year I believe). - Source: Hacker News / over 2 years ago
Actually, that's done with TPUs which are more efficient: https://cloud.google.com/tpu. Source: almost 3 years ago
TPU training uses Google silicon and is thus a true deep learning alternative to Nvidia. Source: almost 3 years ago
The server choice really depends on how much CPU and RAM the requests take, how many users will be hitting the server, etc. You can start with a $5/month Digital Ocean server (or AWS or Google) and see if that works for you. Or you can outsource the server administration to Amazon or Google if you don't want to deal with it or need specialized tpu hardware. Source: about 3 years ago
Using the Galaxy UI, knowledge workers can systematically review the best results from all configured services including Apache Solr, ChatGPT, Elastic, OpenSearch, PostgreSQL, Google BigQuery, plus generic HTTP/GET/POST with configurations for premium services like Google's Programmable Search Engine, Miro and Northern Light Research. - Source: dev.to / 8 months ago
Data Transformations: This phase involves modifying and integrating tables to generate new tables optimized for analytical use. Consider this example: you want to understand the purchasing behavior of customers aged between 20-30 in your online shop. This means you'll need to join product, customer, and transaction data to create a unified table for analytics. These data preparation tasks (e.g., joining... - Source: dev.to / 8 months ago
Introduction In today's data-driven world, transforming raw data into valuable insights is crucial. This process, however, often involves complex tasks that demand efficiency, scalability, and reliability. Enter dbt Cloud—a powerful tool that simplifies data transformations on Google BigQuery. In this article, we'll take you through a step-by-step guide on how to run BigQuery transformations using dbt Cloud.... - Source: dev.to / 9 months ago
You'll want to evaluate what BigQuery has to offer and see if it makes sense for you to move over. Source: 10 months ago
Watch the introductory videos on BigQuery on the Google Cloud Platform website (https://cloud.google.com/bigquery). Source: 10 months ago
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Databricks - Databricks provides a Unified Analytics Platform that accelerates innovation by unifying data science, engineering and business.What is Apache Spark?
machine-learning in Python - Do you want to do machine learning using Python, but you’re having trouble getting started? In this post, you will complete your first machine learning project using Python.
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.
Amazon Forecast - Accurate time-series forecasting service, based on the same technology used at Amazon.com. No machine learning experience required.
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.
