guildai
nodejs-bigquery
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guildai | nodejs-bigquery | |
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16 | 43 | |
856 | 455 | |
0.6% | 1.3% | |
8.8 | 7.9 | |
8 months ago | 7 days ago | |
Python | TypeScript | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
guildai
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guildai VS cascade - a user suggested alternative
2 projects | 5 Dec 2023
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[D] Who here are convinced that they have a really good setup that keeps track of their ML experiments?
Experiment tracking in DvC is implemented using git to store snapshots of a project and related artifacts. You might take a look at Guild AI's support for DvC, which is tightly integrated with DvC stages. You can run any of the stages defined for a project and you get a properly isolated run (each run is a project copy to ensure that you're not corrupting the run if you modify files while it's running - as well as properly supporting concurrent runs). Once you have runs in Guild, you can use any number of tools to study, compare, export, etc.
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[D] Deploying SOTA models into my own projects
I built an experiment tracking tool (Guild AI) that focuses on code/model reuse and so this question is dear to my heart :) Best of luck!
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[P] I reviewed 50+ open-source MLOps tools. Hereβs the result
I'm not aware of experiment tracking in Jupyter notebooks themselves. Guild AI is able to run notebooks as experiments however.
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[D] What MLOps platform do you use, and how helpful are they?
Disclosure - I'm the author of Guild AI so take this for the biased opinion that it is.
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[N] Experiment tracking with DvC and Guild AI
I'm the author of Guild AI (open source experiment tracking). For some time now Guild users have asked for DvC support. This is now available as a pre-release.
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[D] Why doesnβt your team use an experiment tracking tool?
Guild AI now has support for running DvC stages as experiments. DvC uses git under the covers to manage project state for each experiment, along with the experiment results. Guild doesn't touch your git repo and instead copies your project source to a new run directory. This ensures that you have a correct record of your experiment without churning your project state.
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Data Science toolset summary from 2021
Guild.ai - https://guild.ai/
- [D] How do you ensure reproducibility?
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[D] I'm new and scrappy. What tips do you have for better logging and documentation when training or hyperparameter training?
Use guild and pytorch-lightning. Make it easy for new contributors to get your data by using dvc as a data access tool.
nodejs-bigquery
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Wrangling BigQuery at Reddit
If you've ever wondered what it's like to manage a BigQuery instance at Reddit scale, know that it's exactly like smaller systems just with much, much bigger numbers in the logs. Database management fundamentals are eerily similar regardless of scale or platform; BigQuery handles just about anything we throw at it, and we do indeed throw it the whole book. Our BigQuery platform is more than 100 petabytes of data that supports data science, machine learning, and analytics workloads that drive experiments, analytics, advertising, revenue, safety, and more. As Reddit grew, so did the workload velocity and complexity within BigQuery and thus the need for more elegant and fine-tuned workload management.
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Building a dev.to analytics dashboard using OpenSearch
Now I know I've got some data I could use, I now need to find a platform that I can use to analyse the data coming from the Forem API. I did consider some other pieces of software, such as Google BigQuery (with looker studio) and ElasticSearch (with Kibana), I ultimately went with OpenSearch which is essentially a forked version of ElasticSearch maintained by AWS. The main reasons are that I could host it locally for free (unlike BigQuery). I do have some prior experience with both elastic (back when it was called ELK) and OpenSearch, but my work with OpenSearch was far more recent, so I decided to go with that.
- Learning Excel. Is there a resource for fake data sets like retail and wholesale inventories and sales histories etc for testing and practice?
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Data Analytics at Potloc I: Making data integrity your priority with Elementary & Meltano
Bigquery as our data warehouse
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Designing a Video Streaming Platform πΉ
Google BigQuery
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What is data integration?
You build a data integration between all the ad service providers (e.g. Google Ads, Facebook Ads, etc.), ingesting data from those APIs and storing it in your BigQuery data warehouse.
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What are Firebase Extensions? How can they speed up your app development?
It also includes some extensions that integrate Firebase with Google Cloud Platform services such as BigQuery.
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Evolutionary Data Infrastructure
In addition, batch tasks require knowledge of the data schema of each service in order to get the data correctly and save it to the corresponding warehouse table. Assuming our data warehouse is GCP BigQuery, the schema in the warehouse table also needs to be created and modified manually.
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Moving to Google Cloud managed services, from a FinOps point of view
BigQuery has a pricing model close to Pub/Sub : you pay for what you insert on the database (in streaming) and the storage of these data. The main difference is on what you can do with these data. BigQuery is not a message queuing service, this is a data warehouse service. It proposes a query service to exploit these data and you pay for these queries. Actually, not on the query itself but on the quantity of data manipulated for producing the results of the query. This means that you do not directly pay for a power capacity on query but on data transfer to produce the result which is very different from a none managed database perspective such SQL databases where the main model pricing is the node size to store and query data.
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Apache Kafka Use Cases: When To Use It & When Not To
A Kafka-based data integration platform will be a good fit here. The services can add events to different topics in a broker whenever there is a data update. Kafka consumers corresponding to each of the services can monitor these topics and make updates to the data in real-time. It is also possible to create a unified data store through the same integration platform. Developers can implement a unified store either using an open source data warehouse like Apache Kylin or use a cloud-based one like Redshift or Snowflake. In this instance, the organization uses BigQuery. Data to this warehouse can be loaded through a separate Kafka topic. The below diagram summarizes the complete architecture.
What are some alternatives?
MLflow - Open source platform for the machine learning lifecycle
aim - Aim π« β An easy-to-use & supercharged open-source experiment tracker.
dvc - π¦ ML Experiments and Data Management with Git
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
airbyte - The leading data integration platform for ETL / ELT data pipelines from APIs, databases & files to data warehouses, data lakes & data lakehouses. Both self-hosted and Cloud-hosted.
dbt-core - dbt enables data analysts and engineers to transform their data using the same practices that software engineers use to build applications.
labml - π Monitor deep learning model training and hardware usage from your mobile phone π±
dagster - An orchestration platform for the development, production, and observation of data assets.
Sacred - Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
wandb - π₯ A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
rudderstack-docs - Documentation repository for RudderStack - the Customer Data Platform for Developers.
cube.js - π Cube β The Semantic Layer for Building Data Applications