Machine Learning Lifecycle 2021 Conference Preview

Automatic TRANSCRIPT

In today's episode though we wanted to highlight our upcoming machine learning life cycle event. That's taking place. January twenty sixth through twenty eight. Twenty twenty one. I know that some of our podcast listeners signed up for our data for a conference that took place in september of twenty twenty and that was a really awesome event. we had hundreds of sessions. And you know lots of live sessions. Many more on demand with some incredible speakers at that event so just like with our data for a icon machine. Learning life cycle event is going to have that as well. We're going to have incredible. Keynotes incredible presenters and sessions as well really focused on that whole learning life cycle including m l. operations building models model management. So we'll have key five. He is for our sessions and our content for the upcoming machine learning life cycle event. If you like to check out that event we encourage you to go to life cycle. Comp dot com so m. l. life cycle co nf dot com. You can sign up as always. Our events are free to attend but so we wanted to spend you know a little bit of time going into the key. Main areas of focus for the sessions. And then some highlight. Some of the presenters sessions as well so the first area of focus is going to be on machine learning model development sessions in this will include and be focused on building and developing models so for supervised learning unsupervised learning reinforcement learning and then also across all different kinds of algorithms. So we talk about the three different types of learning and a lot of our podcast but just to go over that supervised learning through example unsupervised is learning through discovery and then reinforcement learning is learning through trial and error so that that area is going to focus really on model development for for that That all of that stuff. The next area of focus is going to be the machine. Learning model management so sessions in this category will focus on how companies should go about managing their models once. They're actually in production version eating life cycle management and then also aspects of that human side of management as. Well you know we don't want to forget at the end of the day. There are humans that are overseeing and managing this. So let's not forget about that aspect as well related to this idea of emo model management is this emerging space called ops mo operations which does overlap a bit and he talks about dealing with model life cycle in terms of The version being an iteration of models. And that's our of stuff but mls also keep an eye on the model as it's in production so things like model drift and data drift so these sessions on mo ops. I talk about what. Emma lops is how it relates to a related area which is taking a development code in the operational aspects of managing the development life cycle. There's a lot of things in common with that. There's a lot of different things though. Dealing with data drift a model drift and aspects of modernisation and models security and all sorts of things. So people definitely have sessions on. Emily ops quite a few and some emerging technology vendors that are expanding in the space. You're gonna wanna hear from them. Also we're to be looking at model governance so machine learning model governance. This is really focused on aspects of the the management of models in terms of access and control and decision making and who gets access to the various models and maybe differences in version in terms of. Maybe wanna keep multiple versions of a model around for different purposes. Maybe models trained in different data sets for different things you know. How do you handle. That can become very complicated. And you have to. Of course deal security and privacy and all these sorts of aspects. We're gonna be talking about that in the nfl model governance aspects. And of course we have lots of other sessions focused on adoption and development and management of machine learning models across the life cycle from people across the board who have been implemented on implementing machine learning. So just like our events we've have in the past We have three tracks. Are we have a track. That's focused on the technology side. So you can learn about how to do things and get insights on the from a from a technology perspective. We also have an industry track which is really focused on the industry applications. How are different industries applying these technologies. What are their use cases. What are the some of their lessons. Learned there's also some panels we might have from people. We do have actually throw people in different industries who are share around a similar topic and then of course we have large government and public sector contingency because governments around. The world are making use of machine learning for a lot of things. We're dealing with on our day to day basis of course dealing with things like pandemic and public health but still many other aspects of our life because the government's you know facing the same remote world that we are an ai machine learning Forms apart so we have incredible speakers and sessions and we'll we'll highlight a few of them right here in this podcast right so we have some great keynotes that we wanted to talk about. We have harrison smith. Who's the director of enterprise digitisation at the internal revenue. Service the irs. He's going to be talking about from digitisation to ai. How technology is transforming the government and in particular the irs. I know that many government agencies especially in twenty twenty digitisation and digitalization has been a big focus for that because as we shift from in person to remote work they've needed to really ramp up quickly a lot of their processes to make sure that people are able to stay functioning in their roles. So that's going to be a really incredible keynote. We also have maria wrote. Who's the deputy federal chief information officer from the office of management and budget ownby. She's going to be delivering a keynote as well about Ai in the government. We're also going to have tim persons whose chief scientists in managing director of the science technology assessment analytics team at the. Us government accountability office gao. He's going to be delivering a keynote about planning an agile government with artificial intelligence

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