InfuseAI

Data quality (@getpiperider)
Data versioning MLOps community:

[Recce - Making Data Productive. @DataRecce]

Data projects often have two extremes

- no pre-merge checks, fast merge times, but constant breakages
- adopt best practices causing merge times to increase from hours to days

Keep best practices and speed up PR review with Recce
https://t.co/MEFNXp95gp

#dbt #dataengineering

[InfuseAI @InfuseAI]

Nice! This is perfect for some static analysis and a taster of what PipeRider's Impact Reports can offer:

[Canner @cannerdata]

LLM/AI sharing x LangChain Watch Party

Awesome sharings from @langchain, @InfuseAI CEO @clkao, and developers of the open-source project Accio(https://t.co/Os3xVtZwnL)!

[Modern Data Stack @moderndatastack]

🔓 Unlocking the Power of DBT: Visualizing Data Lineage, Diff Analysis, and Impact Analysis for Efficient Data Pipeline Management by @DaveFlynn
https://t.co/XV31sHvQ7x

[InfuseAI @InfuseAI]

Please help get a PipeRider a home on the dbt Slack to help users 🙏

https://t.co/uxXzqcZxuU

[InfuseAI @InfuseAI]

Check out Alexey's thread below - he breaks down the MLOps process in this easy to understand thread

And if you need an open-source MLOps platform in 30 minutes, check out the PrimeHub 1-click AWS install:

https://t.co/5ry6xdqNBW

#MLOps #MachineLearning #AI #opensource

[Alexey Grigorev @Al_Grigor]

Are you curious about MLOps?

How can you apply it in a real-world project?

Here is a summary of my article about it 🧵

[InfuseAI @InfuseAI]

Get the latest version of PipeRider now, with improved branch comparison.

Also, follow @getpiperider for tweets about PipeRider releases, features, and data quality related content!

#dataops #dataquality #DataEngineering #Analyticsengineering #dbt #datareliability

[InfuseAI @InfuseAI]

PrimeHub 4.0 released last month 📢
The #1 open-source MLOps platform just got even better:

- K8s 1.24 support
- Group resource dashboard
- Increased SDK and CLI support

GitHub
https://t.co/MXGiRiEAfT

Read more here
https://t.co/oiTpR7w23c

#MLOps #MachineLearning #OpenSource

[InfuseAI @InfuseAI]

@geraldooizx Hi Gerald, that's awesome.
If you need any help getting set up or using PipeRider just let us know :)

[InfuseAI @InfuseAI]

If you plan to build and deploy your dbt project using GitHub Actions, you'll need to connect to your data warehouse

Here's a quick guide on how to run dbt with BigQuery in GitHub actions:

https://t.co/7wiYW83hHy

#BigQuery #GitHubActions #dbt #DataEngineering #DataOps

[InfuseAI @InfuseAI]

Thanks @jfcohh_ glad we could help!

If you have any questions or need helping using PipeRider, just join our Discord server like Juan did:

https://t.co/HEM8xOgkrq

#dataquality #datareliability @opensource #dataprofiling #DataViz

[Juan Francisco HH @jfcohh_]

@InfuseAI @DataTalksClub I've been working these days with #PipeRider and it just amazed me the quickness of handling 36M records. This kind of cool tool would save me a lot of code, time and effort. Thanks to @DaveFlynn for supporting me on my doubts

[InfuseAI @InfuseAI]

Details about our @DataTalksClub Workshop can also be found in this blog post:

https://t.co/8UkyryREbG

The workshop is useful to all #dbt users (with repo provided).

If you're following the #DataEngineering Zoomcamp there is homework at the end 👩‍🎓

#opensource #dataquality

[DataTalksClub @DataTalksClub]

Last week, we had special guests on Data Engineering Zoomcamp!

You'll want to check this out if you missed it!

Dave Flynn from @InfuseAI presented a free hands-on workshop on data profiling with dbt and PipeRider.

1/2

[InfuseAI @InfuseAI]

Oh yeah, and the workshop has its own self-contained repo.

If you were naughty and skipped the Zoomcamp module, you can fork-and-go our repo and get stuck in.

All the steps you need are in the readme:

https://t.co/Iona2AT2Ye

#dataengineering #dbt #analyticsengineering

[InfuseAI @InfuseAI]

The PipeRider workshop that accompanies week 4 of the @datatalksclub Data Engineering Zoomcamp is now online to watch:

https://t.co/WfRrPtM0jl

You’ll learn how to use PipeRider's data comparison to understand the impact of your #dbt data model changes

[InfuseAI @InfuseAI]

The data profile comparison summary highlights things like schema change the percentage change of values within tables

The markdown-formatted summary is specially designed for pull request comments:

[InfuseAI @InfuseAI]

Don't forget that the PipeRider workshop with Data Talks Club is coming up on Wednesday Feb 22.

If you're following along with the Data Engineering Zoomcamp then the workshop will be linked with Week 4. (but really all you need is a dbt project to join!)

https://t.co/bvjCZRkebB

[InfuseAI @InfuseAI]

We'll be joining @DataTalksClub to show how to maximize your confidence making data model changes in dbt using PipeRider

You'll learn how to use PipeRider's data profile comparison to compare production and dev data models and more

https://t.co/5KJTcbWf8u

[InfuseAI @InfuseAI]

Part 2 of Simon's look at GPT is out now, this time introducing ChatGPT:

Mandarin content 中文:
https://t.co/LuAf851mGR

#gpt #chatgpt #NLP #語言模型

[InfuseAI @InfuseAI]

In this first in a series of articles about GPT, InfuseAI Customer Success Engineer Simon Liu looks at the history of GPT models.

Mandarin content 中文:
https://t.co/aFRfpwLOuJ

#gpt #chatgpt #NLP #語言模型

[InfuseAI @InfuseAI]

We'll be joining @DataTalksClub to show how to maximize your confidence making data model changes in dbt using PipeRider

You'll learn how to use PipeRider's data profile comparison to compare production and dev data models and more

https://t.co/5KJTcbWf8u

[Modern Data Stack @moderndatastack]

➡️ Confidence in Data Model Changes: Staging + Data Profile Comparison. By @DaveFlynn https://t.co/xy32xZSvnn