Update 467-pycon-data-sci-panel.txt

transcripts/467-pycon-data-sci-panel.txt

@@ -2,7 +2,7 @@
 
 00:00:06 the chance to sit down with some amazing people from the data science side of things. Jody Burchill,
 
-00:00:11 Maria Jose Molina Contreras, and Jessica Green. We cover a whole set of recent topics from a data
+00:00:11 Maria Jose, Molina Contreras, and Jessica Green. We cover a whole set of recent topics from a data
 
 00:00:18 science perspective. Though we did have to cut the conversation a bit short as they were coming
 
@@ -176,7 +176,7 @@
 
 00:08:00 just to come away on a geek holiday with friends. Yeah, and we were actually all just at PyCon DE
 
-00:08:06 just before this, like a month ago. Yeah, well, yeah, it's it's a different scale. Let's put it
+00:08:06 just before this, like a month ago. Yeah, well, yeah, it's a different scale. Let's put it
 
 00:08:12 that way. But I think it's a similar feel like one thing that I value so much about the Python
 
@@ -188,7 +188,7 @@
 
 00:08:34 who have never coded and who are career changers, because I'm also a career changer from academia.
 
-00:08:39 And this is what makes I think Python special, the community and I think the PyCons are an
+00:08:39 And this is what makes I think Python special, the community and I think the PyCon are an
 
 00:08:44 absolute representation of that. Yeah, absolutely. For me, it's the same feeling I love to go to
 
@@ -198,9 +198,9 @@
 
 00:09:07 awesome. I came here and meet friends that this is my third time in here. And I'm super, super
 
-00:09:13 excited and happy. And I'm super eager to next year. And also the Python in Espanol. Yeah,
+00:09:13 excited and happy. And I'm super eager to next year. And also the Python in Espanola. Yeah,
 
-00:09:20 of course. And also we have even here we have a track that is PyCon charlas to be even more
+00:09:20 of course. And also we have even here we have a track that is PyCon Charla's to be even more
 
 00:09:26 welcoming to different people from different communities. And it's just amazing. It's super
 
@@ -234,9 +234,9 @@
 
 00:10:43 And this is not that cheap, but it's relatively cheap compared. So I was going to say you could
 
-00:10:50 do a plug for EuroPython while you're here. We have also the option to have grants. There is a
+00:10:50 do a plug for Euro Python while you're here. We have also the option to have grants. There is a
 
-00:10:56 different programs, PyLadiesGrants or the conference organizers grants. Also, this is
+00:10:56 different programs, PyLadies Grants or the conference organizers grants. Also, this is
 
 00:11:02 something that could help people to try to apply or come here. Yeah, they mentioned that at the
 
@@ -248,29 +248,29 @@
 
 00:11:24 big deal. And I suppose all three of you being from Berlin, we should say generally the same
 
-00:11:29 stuff applies to EuroPython as well, I imagine. Right? Yeah. So if you're in Europe, the biggest
+00:11:29 stuff applies to Euro Python as well, I imagine. Right? Yeah. So if you're in Europe, the biggest
 
-00:11:34 deal is to get all the way to the US, maybe go to EuroPython as well, which would be fun. Yeah.
+00:11:34 deal is to get all the way to the US, maybe go to Euro Python as well, which would be fun. Yeah.
 
 00:11:39 Or something more local. This portion of Talk Python to Me is brought to you by
 
-00:11:44 OpenTelemetry support at Sentry. In the previous two episodes, you heard how we use Sentry's error
+00:11:44 Open Telemetry support at Sentry. In the previous two episodes, you heard how we use Sentry's error
 
 00:11:50 monitoring at Talk Python, and how distributed tracing connects errors, performance and slowdowns
 
-00:11:56 and more across services and tiers. But you may be thinking, our company uses OpenTelemetry. So
+00:11:56 and more across services and tiers. But you may be thinking, our company uses Open Telemetry. So
 
-00:12:03 it doesn't make sense for us to switch to Sentry. After all, OpenTelemetry is a standard, and you've
+00:12:03 it doesn't make sense for us to switch to Sentry. After all, Open Telemetry is a standard, and you've
 
 00:12:08 already adopted it, right? Did you know, with just a couple of lines of code, you can connect
 
-00:12:14 OpenTelemetry's monitoring and reporting to Sentry's backend. OpenTelemetry does not come
+00:12:14 Open Telemetry's monitoring and reporting to Sentry's backend. Open Telemetry does not come
 
 00:12:20 with a backend to store your data, analytics on top of that data, a UI or error monitoring. And
 
-00:12:26 that's exactly what you get when you integrate Sentry with your OpenTelemetry setup. Don't fly
+00:12:26 that's exactly what you get when you integrate Sentry with your Open Telemetry setup. Don't fly
 
-00:12:31 blind, fix and monitor code faster with Sentry. Integrate your OpenTelemetry systems with Sentry
+00:12:31 blind, fix and monitor code faster with Sentry. Integrate your Open Telemetry systems with Sentry
 
 00:12:38 and see what you've been missing. Create your Sentry account at talkpython.fm/sentry-telemetry.
 
@@ -420,7 +420,7 @@
 
 00:19:35 like, that's a very good point, actually. Yeah. I was just talking to a fourth Berlin based data
 
-00:19:41 science woman. I was talking to Enos Montagna last week. I was hoping she could be here, but she's,
+00:19:41 science woman. I was talking to Enos Montagne last week. I was hoping she could be here, but she's,
 
 00:19:46 she's not making the conference this year. Anyway. Hi Enos. And she was talking about how
 
@@ -464,15 +464,15 @@
 
 00:21:37 more or easier than others. For instance, some tools that I'm using currently, just for giving
 
-00:21:45 you an example, this Lanchain or Discard and yeah, and they are two open source libraries.
+00:21:45 you an example, this LangChain or Discord and yeah, and they are two open source libraries.
 
-00:21:55 Lanchain is more focusing in that chat system in case that you want to develop a chat system or
+00:21:55 LangChain is more focusing in that chat system in case that you want to develop a chat system or
 
-00:22:03 of course has a lot of more applications because Lanchain is super useful also for handling all the
+00:22:03 of course has a lot of more applications because LangChain is super useful also for handling all the
 
 00:22:11 large language models. Yeah. There's some cool boosts here that are boosted with cool products
 
-00:22:16 based on Lanchain as well. Oh, really? I'm going to take a look.
+00:22:16 based on LangChain as well. Oh, really? I'm going to take a look.
 
 00:22:20 That then you export as a Python application. It's very neat. Anyway. Very good. Yeah. But you
 
@@ -598,7 +598,7 @@
 
 00:28:09 these large models just one time is as much as, say, a person driving a car for a year type of
 
-00:28:14 energy. And you're like, oh, that's that's no joke. And so that that might encourage you to run
+00:28:14 energy. And you're like, oh, that's no joke. And so that that might encourage you to run
 
 00:28:20 smaller models or things like that, which I think for a long time we were thinking like, oh, it's
 
@@ -612,7 +612,7 @@
 
 00:28:46 because then we get a sense of like how daunting that is. I think like comparing it to like air
 
-00:28:52 travel or like to cars and so forth is is good. But we tend to focus a little bit on like, oh,
+00:28:52 travel or like to cars and so forth is good. But we tend to focus a little bit on like, oh,
 
 00:28:58 it's just this part of the system and not the system as a whole. Well, I think the training
 
@@ -751,4 +751,3 @@
 00:34:16 Now get out there and write some Python code.
 
 00:34:18 [Music]
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