Consider how the software development life cycle (SLDC) is well-defined at this point: planning, creating, testing, deploying, maintaining – or some variant, depending on your software methodology. The gist remains consistent. Computer software runs "logic" in hardware, the test suites are repeatable, it's a relatively deterministic process. Consider how, with machine learning, we're working with probabilistic systems instead. Instead of writing code as instructions, we're guiding these systems to learn from data. IBM has a mission to help bring machine learning capabilities to all, so we can all participate in the AI economy responsibly. Consequently, there are many different participants and stakeholders in this emerging field of ML Ops. This meetup will review the current state of what it takes to build successful pipelines in this probabilistic setting so that we can build a shared understanding of why we treat our models as living products and ask the right questions: Are they healthy? Are the representative? Are they biased?
Just like in-person meetups, our virtual events allow you to casually meet and connect with other community members from around the world in a small-group setting (breakouts). Our presenter will be available for questions during the "mix and mingle" portion of the event immediately following his talk.
Join us tomorrow at 8am Pacific (11am Eastern, 5pm Berlin, 11pm Singapore).
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Tim Bonnemann
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