Welcome to PyCon India 2024
Join us for the 15th edition of PyCon India, the premier Python conference in India, happening in the bustling tech hub of Bengaluru, famously dubbed as the "Silicon Valley of India." From September 20 to 23, 2024, the Python community will come alive in a celebration of innovation, learning, and collaboration.
This annual gathering brings together Python enthusiasts, developers, and industry professionals for four days of inspiring Talks, hands-on Workshops, and networking opportunities. Visit the official website for more information: https://in.pycon.org/2024/
Call For Proposals (CFP)
Proposals for Talks and Workshops are invited from Python enthusiasts. Submissions open on 10th March 2024 and end on 9th June 2024.
Talks
Technical talks are the most important event at PyCon India, the core of the conference. Two of the four days are dedicated to talks. Talks are short lectures that are 30 minutes long (including Q&A – 5 mins) and can be on any topic related to Python. Speakers come from the Python community.
Talks are selected through a CFP (Call For Proposals) process. Interested members of the community may propose their talks through an application. An editorial panel designated by the organizers has been entrusted with a procedural methodology for selecting the talk. In the 2023 edition, the conference received an impressive 228 proposals, out of which 39 were selected.
For more details and insights, read the CFP announcement blog post.
CFP applications from the previous year can be seen here.
Important Points
- Talk duration is 30 minutes (25 mins for the talk, 5 mins - Q&A)
- CFP closes on 9th June 2024
- Schedule shall be released on 1st September 2024
- Talks will be presented on 21st and 22nd September 2024
- We may have up to 3 parallel tracks of talks in each session. The morning session will run from 10:00 AM to 1:00 PM. The evening session will run from 2:00 PM to 6:00 PM.
Format
- The format of each talk will be up to the speaker but should include a mix of lectures, demos, and Q&A.
Workshops
These are 3-hour-long interactive sessions where attendees can learn by doing. Hands-on learning is as important as talks for a fulfilling conference experience. Like talks, workshops are also conducted by the members of the Python community.
Anyone can propose conducting a workshop. Any topic of interest to the Python community is okay - the workshop should help attendees learn a new skill, technology, or library. To get a sense of the topics from last year, take a look here.
In the CFP proposal, please mention an outline of the workshop and the prerequisites and the slides if possible. Also, mention if you have conducted the same workshop before.
The workshop should be interspersed with proper hands-on exercises. After the workshop people should be ramped up on the workshop topic and should be able to take it forward themselves.
To know about Best practices please check the Workshop Proposal Announcement blog post.
Important Points
- Workshop duration is 3 hours (A small break in between as planned by the speaker)
- CFP closes on 9th June 2024
- Schedule shall be released on 1st September 2024
- Workshops will be conducted on 20th September 2024
- We may have up to 3 parallel tracks of workshops in each session. The morning session will run from 10:00 AM to 1:00 PM. The evening session will run from 2:00 PM to 5:00 PM.
Format
- Workshops are aimed at beginner, intermediate, and advanced experience-level participants.
- Workshop proposals must include a detailed outline of the covered topics and allocated time.
Important Dates
- CFP for Talks and Workshop Open on 10th March 2024 and closes on 9th June 2024.
What to Propose
Anything of interest to Python programmers is welcome. However, there are a few topics that we feel might be great -
- Lessons from using Python in your project. Did you find something against conventional wisdom? Something confirming conventional wisdom? Do you have advice for people solving similar problems? Example - I tried Python for video processing, or in my medical imaging project, and here are the lessons.
- Something you're doing to make the language/ecosystem better. Writing a library to solve an interesting problem? Or have some new ideas on optimization.
- Something you learned from a different language that may be useful to the Python community. How about a type system? Or patterns from functional programming. Or logic programming maybe?
- Thoughts on tech culture and living. Ideas on improving diversity and inclusiveness. On programmers’ physical and mental health. On getting better at productivity. On workplace issues. Anything that can make an impact, especially if you have used Python for any of the above or have seen someone using Python.
And if you don't get any ideas along these lines, try plain and simple teaching. Pick up a niche topic (maybe a recent technology, or a scientific paper), and help us learn. A well-delivered lecture even at a beginner level is often well-received.
The Review Process
- Authors should propose their talks using the CFP application
- CFP volunteers review the proposals for completeness
- Once the proposals are ready, they are reviewed by a panel of experts
- If the proposal does not look complete, or the reviewers need clarifications, the author is notified via comments
- The panel of experts finally vote on the proposals
- A pre-final shortlist is eventually prepared based on the votes
- The shortlisted proposals go through a round of rehearsals (more details in the section below)
- A final list is created and published.
Rehearsals
Shortlisted speakers will be expected to participate in rehearsal sessions. Rehearsals will be done via teleconferencing, where the speaker shall give a mock run of their talks in a time-bound manner. The audience will consist of volunteers, reviewers, and possibly other speakers. The speakers will be given feedback if necessary.
The point of this exercise is to make sure speakers are ready with their talks ahead of time. And also, to make sure they can finish the talk in the stipulated time. It is useful for the speakers too as they'd get feedback on the content delivery and presentation.
Participation in the rehearsal sessions is likely to be a required step - chances of an unrehearsed talk making it to the final stage are substantially lower.
Diversity
We in the Python community believe in making our community more diverse. This means we are encouraging content from diverse walks of life. This also means we want to improve participation from under-represented groups.
Our goal is to maximize content from under-represented groups. You can help us by encouraging your friends, family, and colleagues to submit talks. You can also help by mentoring.
Also note that we have a strict code-of-conduct. This is to make it clear, in intent and practice, that we are committed to making the conference a pleasant, welcoming, and harassment-free experience for everyone, especially for under-represented groups.
Best Practices for Speakers
1. Apply
Even if you have a vague idea, submit a proposal. We're available for help with ideas and feedback (contact information is in the section below). Don't worry about communication skills or English - we are there to help with that too. And our focus is more on the content.
2. Make it detailed
Add as much detail as possible to the proposal. Add the presentation slides if you already have one. Add a short minute video giving a summary of the proposal. More detail helps reviewers make better judgments.
3. Propose early
We will start the review process as the proposals come in, and not at the end. Proposals submitted early will get more attention and feedback
4. The code of conduct
Take a look at the code of conduct, and be mindful of it. The gist is, to be nice and avoid using sexist language.
5. Add a preview video
Add a small intro video about what your talk is about to provide a preview to what's to be expected.
We've put together a set of detailed best practices - take a look. It also contains links to some well-written proposals from previous years.
Questions and Discussions
Ping us on Zulip or IRC (#pyconindia) Or contact the coordinators through email:
CFP Lead - Dr. Murali, CFP shadow lead - DM on Zulip
The team: cfp@in.pycon.org
Proposal Sections
- Python in Platform Engineering and Developer Operations - Using Python to leverage cloud-based services and infrastructure like Cloud services (AWS, GCP, Azure, etc.) for Python, and IaaS for Python. Techniques for processing data across multiple machines or processes using Python like PySpark, Dask, Distributed ML (Horovod), and MPI (Message Passing Interface). Tools and practices for automating development workflows and enhancing productivity like DevOps, Testing, CI/CD, Containers, Orchestration, Logging, and Monitoring, Selenium. MLOps. Concepts and practices for secure, efficient networking using Python like Network Programming, Network Security, and Encryption.
- Artificial Intelligence and Machine Learning
- Python on Hardware - Applications of Python in constrained environments like edge devices, embedded hardware, and robots using lighter runtimes like MicroPython on Open Source Hardware platforms like Arduino and Raspberry Pi
- Community - Exploring cultural aspects and societal influences like Diversity, health, life, education, productivity, workspace issues, community, and coding for causes. Examination of ethical considerations and philosophical questions in technology like Govt. Policies, Responsible AI, Privacy and mitigating biases.
- Artificial Intelligence and Machine Learning - Exploration and implementation of Artificial Intelligence, data science techniques, and machine learning algorithms using Python, such as Generative AI, large language models, artificial general intelligence, natural language processing, computer vision, etc.
- Core Python - Fundamental Python concepts like language runtime, AST, standard library, new features, documentation, and concurrency primitives like multithreading.
- Python in Education and Research - Python as an introductory programming language in educational programs. Use of Python to solve complex scientific and mathematical problems like GIS, Simulations, Game Development, and 3D models. Use of Python's comprehensive libraries across diverse scientific fields like genetic data analysis and biological modeling, celestial simulations, materials modeling, environmental research and neuroscience, etc.
- Python in Web and Applications - Building websites, mobile applications, and desktop applications using Python-like APIs built through frameworks such as PyScript, Django, Flask, FastAPI, Kivy, PyQt, and PyOpenGL, including but not limited to newer age applications like Decentralized Apps and Web 3.0.
- Other - Topics not explicitly covered in the categories.
Proposal Types
- Workshops - The workshop is a 3-hour interactive session where attendees can learn by doing. Workshops can have one or more facilitators.
- Talk - Talk is a 30-minute presentation on a specific topic related to Python.
Selected Proposals
Talk
0 3
2. Why Knowing Cython Helps in Understanding Python: A Deep Dive into Cython & PVM
2 10
3. Faster Applications: Harnessing Python with C/Zig libraries and my story of building fastest LLM tokenizer
0 11
6. Python and the Sun: Transforming Space Science with Open-Source
0 2
8. Transforming Automotive Electronics Testing with Python and Robot Framework
1 2
9. Concurrency and parallel execution in Python and how the GIL affects it
6 0
10. Leveraging Python and Quantum Principles for Enhanced Network Operations and Design
0 2
12. Asynchronous Programming for Scalable Machine Learning Pipelines in Python
0 3
13. Live Coding: Building a Unit Testing Library like Pytest
0 7
15. Beyond the Bot: Overcoming Pitfalls in Building Customer-Facing Chatbots
1 5
16. Database Change Management - 1000s of tables across multiple environments and 1 Alembic to rule them all
0 1
17. Deep Dive into Contextual Logging for Modern Python Applications
5 22
18. Unleashing the Power of Faster Python: Exploring NUMBA & Python’s JIT Compilers
2 12
21. Python in the browser: my journey towards enhancing the Scientific Python ecosystem's interoperability with Pyodide
0 10
23. Beyond Single Models: The Secret Sauce of Predictive Success
0 1
24. Build Your Own Live Streaming Device Behind Your Home Router
0 13
25. Enhancements made in SciPy at Quansight Labs
0 12
27. Managing custom, reproducible Python virtual environments for PySpark and Jupyter Notebooks @ Uber
0 8
28. Big Models, Small Tweaks: Exploring the LoRA way of Fine-Tuning
0 1
29. From Code to Craft: Software Engineering Principles for Python Programmers
0 1
30. QuickGrpc - grpc 101 tailored towards rest devs and showcase of quickgrpc framework
Talk
1 3
2. Enhancing Data Integrity in Engineering: Python's Role in Automated Data Quality Checks
0 7
5. I achieved peak performance in python, here's how ...
0 19
7. Live-Coding: Mastering Python Web Scraping with Scrapoxy
0 0
9. Augmenting LLM Prompts for Contextual Clarity: Building a Retrieval Augmented Generation (RAG) System using Gemma & MongoDB
0 0
10. Building a data layer with FastAPI and Azure Cosmos DB
0 13
12. A faster way to build and share data apps with Pure Python
0 3
13. From Ancient Epic to Modern Marvel: Demystifying the Mahabharata Chatbot with GraphRAG
0 1
15. Building Super Bots with Python and OpenVINO™: Leveraging Multimodal AI for Vision, Audio, and Text.
0 4
16. Create Retrieval-Augmented Generation (RAG) Apps for Enterprise Use Cases in 2024
2 5
17. Unveiling the Potential: Large Language Models and Natural Language Processing in Lung Cancer Diagnosis
2 29
18. Pythonic Infrastructure: Ditch the YAML, Embrace the Charm (cdktf)
0 -1
19. Building an autoscale micro-services architecture using Celery on Kubernetes
1 13
23. Building multi-agent automation workflows using lyzr-automata ( talk )
0 3
24. Unleash the Power of Generative AI with Lyzr's SDKs: Empowering Enterprise Application Development
1 2
25. Unleashing Agent Intelligence: Power Up Your Applications with Lyzr Automata
1 2
28. Conversational GenAI applications with your existing APIs and knowledge base
0 27
30. Improving vector search relevance with reranking & fusion 🚀
0 9
31. Achieving true parallelism in Python: the past, present & future of parallel code in Python
0 2
33. Ansys Python Manager - Python QT app for Python installation, creation and management of virtual environments
0 -2
34. Gen AI Revolution: Exploring Transformer Architecture Layer by Layer
2 1
35. Generative AI in Disaster Response and Management: Enhancing Efficiency and Effectiveness in Crisis Situations
0 5
36. Empowering Code: From Learning Python to Scaling With AI
0 0
37. Optimizing AI Agents for Targeted Applications
1 0
38. Pythonic Harmony: Orchestrating Projects with Poetry
0 0
40. Building Blocks - Integrating GPT API in your web application.
3 6
44. Leveraging Python for Efficient Video Processing: Best Practices and Learned Lessons
1 1
45. Unlock Data with Natural Language: Building Data Assistant for Business using Code LLMs
0 4
47. Python WSGI & ASGI: Python's Evolution From Scripting Tool to Web Development
3 1
49. Using DSPy to build Retrieval-Augmented Generation (RAG) Apps
0 1
50. Django SSE 5.0: Transitioning from Polling to Real-Time Magic
0 2
51. From Quicksort to Timsort to Powersort: Unveiling the Evolution of CPython's Sorting Algorithm
0 0
54. Mastering Python's Magic Methods: Unleashing the Power of Dunder Methods
1 3
57. Streamlining Python Web Development: Building a Modular, Fast, and Scalable, Cloud Ready Framework
3 10
58. Differentiation Engines: The Elves behind the AI Christmas
2 3
59. Igniting Young Minds: Sparking Creativity in Kids Through Python
1 1
61. Unveiling DSPy - Farewell, LLM Prompting; Welcome, Machine learning programming!
0 1
64. Are Our Classrooms Ready for the Fourth Industrial Revolution?
3 0
65. Evaluation Techniques for Large Language Model and Retrieval Augmentation Generation
0 0
66. The unsung hero of Vector Database -- Metric Learning and Self-Supervised learning
0 0
67. Exploring Game Development: Crafting a Car Game Using Python
0 0
68. A custom wrapper over JSONSchema to validate query parameters more efficiently
0 0
73. Building a robust OAuth Provider using Flask, AuthLib and MongoDB
0 15
74. Safeguarding Privacy with NLP: Leveraging Topic Modeling for Ethical AI
0 12
76. Empowering Hardware Development: Python's Role in Accelerated Chip Design and Beyond
0 1
77. Using Python for Rapid Prototyping and Development of Brain Computer Interfaces.
0 1
78. Architecting data products at scale with Python and AWS Serverless
0 2
83. Feature or Preprocessing Step? How to Correctly Set a Baseline in NLP Classification Tasks
0 1
84. Adversarially attack ML models. Now defend against them!
3 2
87. Next Generation Authorisation – a developers guide to Cedar
0 0
89. Extending Python with Rust: Simplicity and Speed in one
0 1
90. Automating the Web Workflows: How LLMs are Redefining Data Extraction and Processing
0 1
91. TMVA SOFIE: CERN's Fast Machine Learning Inference Engine
1 0
93. Python Web App Deployment: Blue-Green deployment and GitOps without the Kubernetes complexity
0 4
96. Crafting Beautiful User Interfaces with Python and GTK
0 4
97. Boosting Python Performance: Harnessing Rust's Power with PyO3
0 4
98. How to Hack Together Your Own Database Client in Python
0 3
101. Wielding Python's Wizards: A Guide to itertools and functools
0 0
105. Revolutionizing Financial Health Analysis: Harnessing Open Source LLM and Langchain Technologies
0 0
106. Let's build a thread-safe HTTP Connection Pool in 30 minutes
0 0
107. Crafting AI-Driven Applications with .NET: A Journey with Python and Azure
0 2
108. When Transformers Learn: Harnessing Python for Deep Learning Breakthroughs
0 0
110. Getting started with Retrieval-Augmented Generation (RAG): Boosting Your LLM Experience
0 0
111. Unlocking the Power of Ragas: A Framework for evaluating Retrieval Augmented Generation (RAG) Pipelines
0 2
112. Sync vs. Async in Python: Tools, Benchmarks, and ASGI/WSGI Explained
0 1
113. RAG Brag - Building Production ready LLM apps
0 74
114. Securing Django APIs: Best Practices for Robust Web Development
1 1
118. Harnessing eBPF with Python: Next-Level Observability and Security
1 0
121. Real-Time Data Pipelines with Snowflake and Redpanda for Python Developers
0 0
122. Automating Data Visualization with Streamlit: From S3 to Interactive Dashboards
0 5
123. Unlocking the Power of gRPC: A High-Performance Protocol for Modern Applications
0 1
124. Python Power-Up: Elevating Your Workflows with Docker on Functions as a service
0 6
125. Turbocharged Microservices: Harnessing Python, gRPC, and Kafka for Unmatched Scalability
0 5
126. Pytest Unleashed: Supercharging Your Python Testing Arsenal
4 3
127. How I ended up maintaining a python package with over 500,000+ downloads
0 4
129. From REST to GraphQL: Transforming API Development with Python and Graphene
0 0
130. Supercharging Deep Learning: Elevating Your Models and results via first principles
0 5
131. Effortless ORM with MongoEngine: Harnessing MongoDB in Python
4 0
133. GenAI in FinTech: Revolutionizing Finance through Cutting-Edge AI Technologies
1 0
134. Robot Revolution: Leveraging Python to Transform Robotics
5 0
135. Simplifying Python Web App Operations: Automating K8s Ops with Open Source
0 4
137. AI Superalignment: Building Pro-Humanity Neural Networks with Mathematical Proofs
0 5
141. Create app in a minute - DazzlePy's Instant Backend Alchemy
5 7
142. Rhinestone: Simplifying API Documentation and API Testing Across Every Tech Stack
1 6
144. Transforming Logs into Real-Time Insights: Creating a Multi-Client Streaming Service With Python
0 4
148. Kivy Mobile App for PyConf 2024 Feedback
0 0
151. How to trust LLM against hallucinations using langkit & whylogs
0 1
152. Geospatial Data Analysis using geopandas and Folium: A business case-study
0 0
153. Bridging the Technical Gap in Embedded Development with BDD & Python
3 0
154. Source Radar: A Versatile Open-Source Code Analysis Tool for Development Teams
0 9
155. Practical tips for building AI applications using LLMs - Best practices and trade-offs
1 0
156. Investigate sending choices flawlessly viable with simultaneous applications..
0 1
157. Next-Gen Apps: Enhancing User Experience with Large Language Models
0 1
158. Chat with Docs: Building custom Chatbots using RAG
0 2
159. MonoRepos in Python: How Not to Duplicate Code Across Multiple Repos
0 11
160. From Concept to Deployment: Streamlit for LLM-Powered Applications
2 0
161. Live Coding: Building a Finite State Machine Library from Scratch
0 3
162. Unlocking Deep Learning fundamentals with PyTorch
2 1
163. The magic of Scipy Spatial Data Structures - Think beyond machine learning
1 9
164. Balancing Supply and Demand: Solving Complex Optimization Problems with Python
0 19
165. Building Event-Driven Python service using FastStream and AsyncAPI
0 4
166. Revolutionizing Python with Ray: A New Era in Distributed Computing
3 0
168. InGen - An Open Source Extract Transform and Transfer Library by BlackRock
0 0
171. Demystifying Machine Learning Predictions: A Hands-on Guide with SHAP
0 2
172. Black Box Debugging - A hitchhiker's guide to debugging python production code with ease
0 54
174. AI-Powered Marketing: Streamlining Campaign Management with Python
0 2
175. Retrieval Augmented Generation: Using your data with LLMs
0 0
177. Click & Cook: Transforming Your Kitchen with Python and GPTScript
6 0
178. Real time example of programming concepts using python
0 0
180. From laptop to Production: Building distributed AI application using Ray
0 0
184. Behind the Curtain: Unraveling the Backend Complexities of AI Applications - A Pythonista’s Perspective
2 1
186. Creating backends using `entry_points` for Python libraries
0 0
187. Robyn: A fast async Python web framework with a Rust runtime
0 1
188. Implementing a National Pediatric Orthopedic Disease Registry using Python, Django
0 5
189. UnicornTask: Streamlined Task Orchestration for Python Web Applications using gunicorn
2 0
190. Striking the Balance: How Much Random is Too Much Random, and How Python Achieves It
1 2
191. Data Dashboarding: Exploring Tools and Frameworks for Python
0 1
194. Memory Management in CPython and Its Impact on Performance
0 2
196. Supercharging ML Data Processing with PySpark Optimizations
0 2
197. Code Less, Do More: Building Serverless Apps with Python and Amazon CodeWhisperer
0 1
198. Chalice: Building Serverless Microservices in Python on AWS
0 1
199. Code Cleaning with Python: Shedding the Scales!
0 4
200. Introducing FireDucks: a must-have DataFrame library to accelerate your voluminous data analysis with pandas at zero cost
6 11
201. 1 Billion rows vs Python: Navigating the 1BRC in pure python
3 2
203. Large Language Models (LLMs) for Code Generation and Assistance
1 5
205. Exploring GenAI Beyond Chat: Leveraging Retrieval Augmented Generation
0 1
206. Enhancing Healthcare Information Systems with Multimodal RAG
0 1
211. Exploring RAG for Creative Writing and Content Generation
0 2
214. Architecting Event Driven Federated GraphQL Subscription for Python Micro services
0 8
218. NLP4Devs: How to Perform Common NLP Tasks with GPT and Build LLM-based Virtual Assistants
0 0
219. Transformers are coming for time-series: Exploring transformers for time-series forecasting
4 1
221. Building Scalable and Reliable Data Pipelines with Python, Apache Airflow with Amazon Q
0 1
222. Designing a Google Meet Transcription Bot: From Concept to Deployment
5 1
224. How I monitored Airflow without using its REST API, socket data transmission or DB Querying?
3 0
227. Borrowing Batteries from Django: Integrating Django Components into FastAPI Applications
1 66
229. Unified Backend for Productionizing and Orchestrating Foundational Models with Python
0 3
231. Supercharge Python Development with Automation and Custom Libraries
0 16
233. Unleashing Python's Power: Lightning-fast speed with a C++ Backend
0 0
235. Enhancing Code Coverage with CodiumAI Cover Agent: Automating Unit Test Generation with AI
0 1
240. The Generative AI Reality Check: Challenges, Solutions, and Best Practices
1 1
241. Unleash the power of AWS Lambda with AWS Powertools for Python
0 1
243. Unlocking the Parallel Universe: Subinterpreters and Free-Threading in Python 3.13
2 1
244. A Low-cost automatic compressing and noise-removing algorithm for satellite images.
2 19
246. Accelerating India's Open Science Journey with Python
0 18
247. Technique to improve Performance in python for critical products
0 0
248. Experimenting with AI for Dynamic Website Creation: From Concept to Live Web Servers
0 5
249. How We Built a Celery-Powered Task Queue Service to Scale Infra Jobs
0 41
250. Mastering Multi-Model Deployment: Ray Serve Strategies for Low Latency
0 1
251. Beyond Documentation: IDE Integration Agents for Your Python Packages
0 2
252. Bringing Life to Hardware: MicroPython in Action
0 40
253. Efficient ML: Achieving Low Latency in Real-Time Systems
2 0
254. Demystifying Python: Python 3.12, Latest trends and Beyond
0 8
256. Turbocharge Your Django Apps: Mastering Python for Peak Performance
0 2
257. A Python Powered Measurement Device for Science Experiments
1 0
259. Is gevent still worthy? Understanding Worker Classes in Gunicorn
0 5
260. Building a Multi-LLM Copilot: A Comprehensive End-to-End Design Approach
0 1
261. Supercharge data analysis: Integrating Python in PowerBI and Excel
1 0
262. Our FastAPI journey in implementing Beckn protocol on ONDC
0 0