Panel: A high-level app and dashboarding solution for the PyData ecosystem.
New framework for handling python dashboards.
Link: https://medium.com/@philipp.jfr/panel-announcement-2107c2b15f52
#vizualization #panel #metrics #dashboard #python
New framework for handling python dashboards.
Link: https://medium.com/@philipp.jfr/panel-announcement-2107c2b15f52
#vizualization #panel #metrics #dashboard #python
Medium
Panel
A high-level app and dashboarding solution for the PyData ecosystem.
the latest news from :hugging_face_mask:
[0] Helsinki-NLP
With v2.9.1 released 1,008 machine translation models, covering of 140 different languages trained with marian-nmt
link to models: https://huggingface.co/models?search=Helsinki-NLP%2Fopus-mt
[1] updated colab notebook with the new Trainer
colab: https://t.co/nGQxwqwwZu?amp=1
[2] NLP – library to easily share & load data/metrics already providing access to 99+ datasets!
features
– get them all: built-in interoperability with pytorch, tensorflow, pandas, numpy
– simple transparent pythonic API
– strive on large datasets: nlp frees you from RAM memory limits
– smart cache: process once reuse forever
– add your dataset
colab: https://t.co/37pfogRWIZ?amp=1
github: https://github.com/huggingface/nlp
#nlp #huggingface #helsinki #marian #trainer # #data #metrics
[0] Helsinki-NLP
With v2.9.1 released 1,008 machine translation models, covering of 140 different languages trained with marian-nmt
link to models: https://huggingface.co/models?search=Helsinki-NLP%2Fopus-mt
[1] updated colab notebook with the new Trainer
colab: https://t.co/nGQxwqwwZu?amp=1
[2] NLP – library to easily share & load data/metrics already providing access to 99+ datasets!
features
– get them all: built-in interoperability with pytorch, tensorflow, pandas, numpy
– simple transparent pythonic API
– strive on large datasets: nlp frees you from RAM memory limits
– smart cache: process once reuse forever
– add your dataset
colab: https://t.co/37pfogRWIZ?amp=1
github: https://github.com/huggingface/nlp
#nlp #huggingface #helsinki #marian #trainer # #data #metrics
Reliable ML track at Data Fest Online 2023
Call for Papers
Friends, we are glad to inform you that the largest Russian-language conference on Data Science - Data Fest - from the Open Data Science community will take place in 2023 (at the end of May).
And it will again have a section from Reliable ML community. We are waiting for your applications for reports: write directly to me or Dmitry.
Track Info
The concept of Reliable ML is about what to do so that the result of the work of data teams would be, firstly, applicable in the business processes of the customer company and, secondly, brought benefits to this company.
For this you need to be able to:
- correctly build a portfolio of projects (#business)
- think over the system design of each project (#ml_system_design)
- overcome various difficulties when developing a prototype (#tech #causal_inference #metrics)
- explain to the business that your MVP deserves a pilot (#interpretable_ml)
- conduct a pilot (#causal_inference #ab_testing)
- implement your solution in business processes (#tech #mlops #business)
- set up solution monitoring in the productive environment (#tech #mlops)
If you have something to say on the topics above, write to us! If in doubt, write anyway. Many of the coolest reports of previous Reliable ML tracks have come about as a result of discussion and collaboration on the topic.
If you are not ready to make a report but want to listen to something interesting, you can still help! Repost to a relevant community / forward to a friend = participate in the creation of good content.
Registration and full information about Data Fest 2023 is here.
@Reliable ML
Call for Papers
Friends, we are glad to inform you that the largest Russian-language conference on Data Science - Data Fest - from the Open Data Science community will take place in 2023 (at the end of May).
And it will again have a section from Reliable ML community. We are waiting for your applications for reports: write directly to me or Dmitry.
Track Info
The concept of Reliable ML is about what to do so that the result of the work of data teams would be, firstly, applicable in the business processes of the customer company and, secondly, brought benefits to this company.
For this you need to be able to:
- correctly build a portfolio of projects (#business)
- think over the system design of each project (#ml_system_design)
- overcome various difficulties when developing a prototype (#tech #causal_inference #metrics)
- explain to the business that your MVP deserves a pilot (#interpretable_ml)
- conduct a pilot (#causal_inference #ab_testing)
- implement your solution in business processes (#tech #mlops #business)
- set up solution monitoring in the productive environment (#tech #mlops)
If you have something to say on the topics above, write to us! If in doubt, write anyway. Many of the coolest reports of previous Reliable ML tracks have come about as a result of discussion and collaboration on the topic.
If you are not ready to make a report but want to listen to something interesting, you can still help! Repost to a relevant community / forward to a friend = participate in the creation of good content.
Registration and full information about Data Fest 2023 is here.
@Reliable ML