List of videos

Read the abstract ➤ https://www.conf42.com/Python_2024_Sharon_Yelenik_personalizing_images_aipowered_features Other sessions at this event ➤ https://www.conf42.com/python2024 Join Discord ➤ https://discord.gg/DnyHgrC7jC Support our mission ➤ https://www.conf42.com/support Chapters 0:00 intro 0:20 preamble 0:30 how do you handle your images? 1:06 what if you have too many images to handle manually? 1:57 ai can help you manage your images 5:31 demo 23:32 get the code on github, learn more, be in touch

Read the abstract ➤ https://www.conf42.com/Python_2024_Joshua_Arvin_Lat_Sophie_Soliven_power_augmented_generation_ai Other sessions at this event ➤ https://www.conf42.com/python2024 Join Discord ➤ https://discord.gg/DnyHgrC7jC Support our mission ➤ https://www.conf42.com/support Chapters 0:00 intro 0:20 preamble 0:31 about speakers 1:21 can we build a generative ai-powered application in 24 hours?

Read the abstract ➤ https://www.conf42.com/Python_2024_Deepak_Karunanidhi_neural_language_processingi_healthcare Other sessions at this event ➤ https://www.conf42.com/python2024 Join Discord ➤ https://discord.gg/DnyHgrC7jC Support our mission ➤ https://www.conf42.com/support Chapters 0:00 intro 0:20 preamble 1:05 anomaly detection 2:27 the challenges we set out to solve 4:51 anomaly detection at a glance 5:47 ml algorithm development process 7:20 why data annotation? 8:24 data annotation... 10:04 a comprehensive ai impl journey 12:19 anomaly identification framework 14:29 machine learning model framework - continuous training for optimized performance 16:33 llm + guardrails 17:18 challenges in bert models 19:35 generative ai and prompt engineering 19:35 gen ai - llm's approach 19:35 thank you

Read the abstract ➤ https://www.conf42.com/Python_2024_Duncan_Blythe_superduperdb_database Other sessions at this event ➤ https://www.conf42.com/python2024 Join Discord ➤ https://discord.gg/DnyHgrC7jC Support our mission ➤ https://www.conf42.com/support Chapters 0:00 intro 0:20 preamble 0:57 data and ai live in separate silos 1:24 bringing machine learning to production is very complicated 1:59 ai needs and vector search need to be integrated directly with the data source to remove overhead 2:30 superduperdb brings ai to the database 2:51 building, deploying and managing ai applications without moving data 3:17 superduperdb: the centerpiece of the modern data-centric ai stack 4:13 all-in-one platform for all data-centric enterprise ai use-cases 4:43 python-first: build for developers with the ecosystem in mind 5:05 open-source: free apache 2.0 software 5:24 how superduperdb works code to: 8:23 - connect 8:43 - code to query 9:25 - code to create custom datatype 10:09 - code to create inline model 10:09 - code to map model to superduperdb 11:52 - create custom superduperdb model 12:36 - apply model 13:04 - apply model to data updates in real time 13:43 - create vector-index 14:07 - create superduperdb stack 14:28 - parametrize superduperdb stack 15:09 - share superduperdb app 15:25 setup even the most complex ai workflows effortlessly 21:56 learn more

Read the abstract ➤ https://www.conf42.com/Python_2024_Jayram_Palamadai_visual_generative_AI Other sessions at this event ➤ https://www.conf42.com/python2024 Join Discord ➤ https://discord.gg/DnyHgrC7jC Support our mission ➤ https://www.conf42.com/support Chapters 0:00 intro 0:20 preamble 0:26 table of contents 0:52 introduction 1:47 diffusion models 5:05 current research 9:59 future implications 13:06 current limitations 18:06 navigating ai growth 24:28 summary 25:03 thanks!

Read the abstract ➤ https://www.conf42.com/Python_2024_Anais_DotisGeorgiou_apache_flight_influxdb Other sessions at this event ➤ https://www.conf42.com/python2024 Join Discord ➤ https://discord.gg/DnyHgrC7jC Support our mission ➤ https://www.conf42.com/support Chapters 0:00 intro 0:20 preamble 0:35 about anais 1:26 agenda 2:04 introduction to influxdb and time series data 6:17 influxdb 3.0 6:47 some of our customers 7:50 ingest benchmark 8:35 commitment to open data architecture with the fdap stack 9:06 data storage 11:34 advantages of columnar data storage (sidebar) 13:58 leveraging the arrow flight client influxdb v3 python client library 15:23 arrow flight sql client 16:35 influxdb v3 python client library 17:36 projects that leverage the arrow flight 17:51 grafana flow 18:24 try it yourself - grafana quick starts 19:33 mage & influxdb - anomaly detection 20:07 quix + influxdb 22:10 solution architecture 23:20 join the influxdb community

Read the abstract ➤ https://www.conf42.com/Python_2024_Tim_Spann_apache_nifi_2_processors Other sessions at this event ➤ https://www.conf42.com/python2024 Join Discord ➤ https://discord.gg/DnyHgrC7jC Support our mission ➤ https://www.conf42.com/support Chapters 0:00 intro 0:20 preamble 0:34 about tim 0:52 flank stack weekly by tim spann 1:04 future of data - nyc + nj + philly + virtual 1:26 generative ai 3:05 rapid innovation in the llm space 3:58 cloudera generative ai stack 5:39 apache nifi and real-time genai 7:18 readyflow gallery 7:41 cloudera + llms 8:28 nifi 2.0.0 features 9:04 python processors 12:00 extract company names 13:47 get compound gtfs data 14:50 extract text from web vtt 15:37 watsonx sdk to foundation 16:11 system / process monitoring 16:58 generate synthetic records w/ faker 17:41 download a wiki page as html or wikiformat (text) 18:30 get gtfs data 19:09 other python processors 21:29 demo 28:35 thank you

Read the abstract ➤ https://www.conf42.com/Python_2024_Gustavo_Amigo_cloud_infrastructure_optimization Other sessions at this event ➤ https://www.conf42.com/python2024 Join Discord ➤ https://discord.gg/DnyHgrC7jC Support our mission ➤ https://www.conf42.com/support Chapters 0:00 intro 0:20 preamble 0:37 intro 1:09 agenda 1:53 scaling is hard 3:24 problem 7:04 context 9:31 approach 10:34 load test one host 11:05 retrieve data from aws to pandas 12:59 analyse one host performance 13:14 we can do better 14:28 another thing - latency 17:03 approaches 18:45 create a scaling simulator 20:12 create a traffic shape generator 20:52 and run a first local experiment 22:40 what does it mean? 22:59 first attempt 24:13 find the best parameters 25:08 and the winner is 26:29 which actually looks better 26:53 testing new parameters in production 28:42 simulation versus real life 31:19 conclusion 31:41 code 33:22 about gustavo

Read the abstract ➤ https://www.conf42.com/Python_2024_Ivan_Chiou_virtualphysical_ros_python_threejs Other sessions at this event ➤ https://www.conf42.com/python2024 Join Discord ➤ https://discord.gg/DnyHgrC7jC Support our mission ➤ https://www.conf42.com/support Chapters 0:00 intro 0:20 preamble 1:02 today's agenda 2:36 speaker introduction 3:43 tools & skills 5:40 architecture 6:29 demo 8:08 what is ros and three.js 11:43 figma 16:13 three.js 27:16 webrtc 32:42 ros bridge 35:13 backend - python 37:51 backend - c# 38:27 devops 39:28 conclusion