D How successful are ML projects?

Our team just deployed our first ML solution. We have several people with DS/ML certificates, and have done ML in side projects and hackathons, but no one has a degree in stats or math or DS and no one's ever done ML professionally. We regularly consulted multiple DS / ML experts, but never had a dedicated DS on our team. It cost us \~$400K to implement, is expected to save $50K a year, and have operational costs of $20K a year.

​

It seems like pursuing this wasn't worth it. Was this a miscalculation on our part? What's the success rate of the projects you work on? How much do you cost the company vs how much money do you generate (or save)?

​

/r/MachineLearning
https://redd.it/1cxtro6

P Fish Speech TTS: clone OpenAI TTS in 30 minutes

While we are still figuring out ways to improve the agent's emotional response to OpenAI GPT-4o, we have already made significant progress in aligning OpenAI's TTS performance. To begin this experiment, we collected 10 hours of OpenAI TTS data to perform supervised fine-tuning (SFT) on both the LLM (medium) and VITS models, which took approximately 30 minutes. After that, we used 15 seconds of audio as a prompt during inference.

Demos Available: here.

As you can see, the model's emotion, rhythm, accent, and timbre match the OpenAI speakers, though there is some degradation in audio quality, which we are working on. To avoid any legal issues, we are unable to release the fine-tuned model, but I believe everyone can tune fish-speech to this level within hours and for around $20.

Our experiment shows that with only 25 seconds of prompts (few-shot learning), without any fine-tuning, the model can mimic most behaviors except details like timbre and how it reads numbers. To the best of our knowledge, you can clone how someone speaks in English, Chinese, and Japanese with 30 minutes of data using this framework.


Repo: https://github.com/fishaudio/fish-speech

/r/MachineLearning
https://redd.it/1cxwqb7

django-import-export v4 released

You may be interested in looking at django-import-export v4.

It offers straightforward, reliable and comprehensive file import / export for your Django application.

django-import-export is an application and library which lets you manage import / export from / to a variety of sources (csv, xlsx, json etc).

It can be run programmatically, or with optional integration with the Django Admin site.

v4 includes lots of fixes and new features.

Please see the README for a full list of features and use-cases.

https://github.com/django-import-export/django-import-export

v4 has been completed by a small group of volunteers. We welcome any comments or feedback.


/r/django
https://redd.it/1cxvlvk

GeoEntropy: A Python Package for Computing Spatial/Geometric Entropy

I look forward to your critical thoughts and ideas about my project GeoEntropy! :-) Meaningful contributions are very welcome.



You can find the source code on Github and the package on PyPi.



Target Audience: Scientists



What My Project Does: GeoEntropy is a Python package designed to compute different entropy measures for spatial data represented in matrices (numpy arrays).



Comparison: GeoEntropy is inspired by the R package SpatEntropy by L. Altieri, D. Cocchi, and G. Roli and provides tools for analyzing the entropy of spatial data.

/r/Python
https://redd.it/1cxyvn2

[D] AI Agents: too early, too expensive, too unreliable

[**Reference: Full blog post**](https://www.kadoa.com/blog/ai-agents-hype-vs-reality)

There has been a lot of hype about the promise of autonomous agent-based LLM workflows. By now, all major LLMs are capable of interacting with external tools and functions, letting the LLM perform sequences of tasks automatically.

But reality is proving more challenging than anticipated.

The [WebArena leaderboard](https://docs.google.com/spreadsheets/d/1M801lEpBbKSNwP-vDBkC_pF7LdyGU1f_ufZb_NWNBZQ/edit#gid=0), which benchmarks LLMs agents against real-world tasks, shows that even the best-performing models have a success rate of only 35.8%.

# Challenges in Practice

After seeing many attempts to AI agents, I believe it's too early, too expensive, too slow, too unreliable.
It feels like many AI agent startups are waiting for a model breakthrough that will start the race to productize agents.

* Reliability: As we all know, LLMs are prone to hallucinations and inconsistencies. Chaining multiple AI steps compounds these issues, especially for tasks requiring exact outputs.
* Performance and costs: GPT-4o, Gemini-1.5, and Claude Opus are working quite well with tool usage/function calling, but they are still slow and expensive, particularly if you need to do loops and automatic retries.
* Legal concerns: Companies may be held liable for the mistakes of their agents. A [recent example](https://www.theguardian.com/world/2024/feb/16/air-canada-chatbot-lawsuit) is Air Canada being ordered to pay a customer who was misled by the airline's

/r/MachineLearning
https://redd.it/1cy1kn9

Django 5.1 alpha 1 released
https://www.djangoproject.com/weblog/2024/may/22/django-51-alpha-1-released/

/r/django
https://redd.it/1cy52tv

Thursday Daily Thread: Python Careers, Courses, and Furthering Education!

# Weekly Thread: Professional Use, Jobs, and Education 🏢

Welcome to this week's discussion on Python in the professional world! This is your spot to talk about job hunting, career growth, and educational resources in Python. Please note, this thread is not for recruitment.

---

## How it Works:

1. Career Talk: Discuss using Python in your job, or the job market for Python roles.
2. Education Q&A: Ask or answer questions about Python courses, certifications, and educational resources.
3. Workplace Chat: Share your experiences, challenges, or success stories about using Python professionally.

---

## Guidelines:

- This thread is not for recruitment. For job postings, please see r/PythonJobs or the recruitment thread in the sidebar.
- Keep discussions relevant to Python in the professional and educational context.

---

## Example Topics:

1. Career Paths: What kinds of roles are out there for Python developers?
2. Certifications: Are Python certifications worth it?
3. Course Recommendations: Any good advanced Python courses to recommend?
4. Workplace Tools: What Python libraries are indispensable in your professional work?
5. Interview Tips: What types of Python questions are commonly asked in interviews?

---

Let's help each other grow in our careers and education. Happy discussing! 🌟

/r/Python
https://redd.it/1cyf892

Flask Stripe Tutorial
https://testdriven.io/blog/flask-stripe-tutorial/

/r/flask
https://redd.it/1cxzphc

Take your Django Serializer game to the next level
https://differ.blog/p/take-your-django-serializer-game-to-the-next-level-b4659a

/r/django
https://redd.it/1cyg4ea

A django rest api key package

Hey everyone,

I've been working on some projects using Django for about five years now. But when I discovered DRF, I've decided to focus on building backend API applications without dealing much with the frontend. But about a year or two ago, I started to build APIs for some SaaS projects, and I realized I needed a robust API key management system.

I initially used https://github.com/florimondmanca/djangorestframework-api-key which is fantastic and has everything you need for API key systems, including great authorization and identification based on Django's password authentication system.

I will say this library shines if you only need API keys for permissions and nothing more.

However, when I wanted to push the package further, I hit some limitations. I needed features like key rotation, monitoring, and usage analytics to help with billing per request and permissions and better performances as the package use passwords hashing algorithms to create api keys.

So, I decided to create my own package. I've been working on it for about nine months to a year now, and it's come a long way. Here are some of the key features:

Quick Authentication and Permission System: You can easily implement authentication and permissions, for example, for organizations or businesses.
Monitoring

/r/django
https://redd.it/1cykl2c

Best practice projects Django + HTMX (+ AlpineJS)

I wan't to dive into Django + HTMX + (AlpineJS) and it would be great to see how others use these tools in projects. Do you know of any projects I could look into? Best practices?

Thanks!

/r/django
https://redd.it/1cyilp8

Do you write many views and paths or have single view for htmx requests?

Do you create view and path for each htmx component or you have single view htmx_request() and then inside that view resolve which template and data to return?

/r/django
https://redd.it/1cy86y3

Is there a way to pass data to redirect() and render the data on template?

Is there way to pass data to redirect() and render it out on templates. I have a static POST form below for sharing post URL. The form is connected to share\_post\_view in the views.py. What I would like to do is pass message='Your message has been sent' to redirect() so that I could render it out on template. Any help will be greatly appreciated. Thank you very much.

return redirect(post.get_absolute_url(), message='Your message has been sent')

views.py

def post_share_view(request, post_id):
post = Post.objects.get(id=post_id)
form = EmailPostForm(request.POST)
if form.is_valid():
name = form.cleaned_data.get('name')
email = form.cleaned_data.get('email')
to = form.cleaned_data.get('to')
comments = form.cleaned_data.get('comments')

message = EmailMultiAlternatives(


/r/django
https://redd.it/1cya1q9

JupyterLab 4.2 and Notebook 7.2 are available!
https://blog.jupyter.org/jupyterlab-4-2-and-notebook-7-2-are-available-b5e6b3c753de

/r/IPython
https://redd.it/1cykkwr

Speed improvements in Polars over Pandas

I'm giving a talk on polars in July. It's been pretty fast for us, but I'm curious to hear some examples of improvements other people have seen. I got one process down from over three minutes to around 10 seconds.
Also curious whether people have switched over to using polars instead of pandas or they reserve it for specific use cases.

/r/Python
https://redd.it/1cy9vpt

Thank You PyConUS 2024 !!!

First timer this year, currently at the airport leaving Pittsburgh after 6 days of PyCon...

I've never seen such an intelligent, inclusive, humble, diverse, and inspiring group of human beings. The Python community serves as a beautiful model of what tech culture should strive towards. I could go on and on about how much fun I had, but in short, thanks to all the volunteers, staff, and FOSS developers that have cultivated such an amazing culture.

/r/Python
https://redd.it/1cyceoq

I'm stuck, please help.

Hi guys,

I'm learning Python as a completely new in programming and I'm stuck in VS code. Running python3 on macOS Sonoma, last version VS code.

Look what it does to me:

a = ("Hi ")
b = ("guys")
c = a + b
print(c)

//now if I run it it returns>

>>> print(c)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'c' is not defined
>>> // all runs in macOS terminal seamlessly.

//VS doesnt see all code, it runs just one line. When I sellect all and run, it returns this>

>>> a = ("Hi ")


/r/IPython
https://redd.it/1cynv2h

TPC-H Cloud Benchmarks: Spark, Dask, DuckDB, Polars

I hit publish on [a blogpost](https://docs.coiled.io/blog/tpch.html) last week on running Spark, Dask, DuckDB, and Polars on the TPC-H benchmark across a variety of scales (10 GiB, 100 GiB, 1 TiB, 10 TiB), both locally on a Macbook Pro and on the cloud.  It’s a broad set of configurations.  The results are interesting.

No project wins uniformly.  They all perform differently at different scales: 

* DuckDB and Polars are crazy fast on local machines
* Dask and DuckDB seem to win on cloud and at scale
* Dask ends up being most robust, especially at scale
* DuckDB does shockingly well on large datasets on a single large machine
* Spark performs oddly poorly, despite being the standard choice 😢

Tons of charts in this post to try to make sense of the data.  If folks are curious, here’s the post:

[https://docs.coiled.io/blog/tpch.html](https://docs.coiled.io/blog/tpch.html)

And here's [the code.](https://github.com/coiled/benchmarks/tree/main/tests/tpch) Performance isn’t everything of course.  Each project has its die-hard fans/critics for loads of different reasons. I'd be curious to hear if people want to defend/critique their project of choice.

/r/Python
https://redd.it/1cyqj6c

SSO and Multiple Django Apps

Hi there,

I'm working on distinct interconnected Django projects and I would like to use a single SSO provider for all of them.

Similar to Stack Exchange, where each portal is separate but there is a federated identity across all , I want to implement a similar solution.

The only self-hosted option that seems to fit my use cases is Zitadel. However, before committing to try it, I would like to know if anyone else has already tinkered with this idea and if it's worth investigating further.

Thanks !

/r/django
https://redd.it/1cyu5yv

Need to understand System Design related to Django Rest Framework.

Hi there,

I’ve been working on Django Rest Framework for a couple of months now.
The thing is I’m working on building APIs Models and view etc.
My go to approach is to have model classes (PosgreSQL tables) then use serializer, and make API inside a view set class.
So is this the approach used every where else to make scalable systems? I’m I doing the right thing?

I’m working for a small company and internal application so not many users but I want to know whether the approach I’m using is correct or not.

Are there any blogs and channels specific to Django System design because when I see system design youtube channels they teach from Java perspective and I can’t relate to things like interface (nothing like that in python afaik)?

Thanks!

/r/django
https://redd.it/1cytl2c