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MLOps Community

The MLOps Community is where machine learning practitioners come together to define and implement MLOps. Our global community is the default hub for MLOps practitioners to meet other MLOps industry professionals, share their real-world experience and challenges, learn skills and best practices, and collaborate on projects and employment opportunities. We are the world's largest community dedicated to addressing the unique technical and operational challenges of production machine learning systems.
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4:00 PM - 5:00 PM, Oct 12 GMT
Creative AI - Using ML to Create Art, Music, and Jokes
Can AI be truly creative? Can model ML models augment human creativity? How do we leverage ML for creating works of Art, Music, and even Jokes?This presentation will answer these questions and more by carefully looking at various open source ML Models and
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4:00 PM - 5:00 PM, Oct 5 GMT
Applying DevOps Practices in Data and ML Engineering


ML Unicorn Start-up Investor Tells-IT-All
What's so enticing about enterprise software? It's incredible to see George's idea and vision to invest in generationally enduring companies. Let's look at the way how George likes to structure deals with companies while he's reviewing them and let's look at the MLOps ecosystem through the eyes of the investors.
Oct 4th, 2022 | Views 49
Databricks Model Serving V2
From our experience helping customers in the Data and AI field, we learned that the most challenging part of Machine Learning is deploying it. Putting models into production is complex and requires additional pieces of infrastructure as well as specialized people to take care of it - this is especially true if we are talking about real-time REST APIs for serving ML models. With Databricks Model Serving V2, we introduce the idea of Serverless REST endpoints to the platform. This allows teams to easily deploy their ML models in a production-grade platform with a few mouse clicks (or lines of code 😀).
Sep 30th, 2022 | Views 104
Towards an Automated R&D Workflow for Edge AI Systems
The R&D workflow of an AI-based product is inherently characterized by the experimental nature of the deep-tech research process. Adding to the challenges of edge technology - working on various ARM-based SOMs with multiple GPUs and DSP types, the inevitable conclusion is that a bespoke R&D methodology is required. This talk discusses SightX AI's design and successful application of an end-to-end MLOPS methodology. The proposed design enabled us to tackle the management of deep learning research aimed to be deployed on various platforms and to become faster and better with every version release. SightX AI recently added a feedback loop to this methodology which gets us a step closer to the holy grail of automated and continuously learning R&D workflow for edge AI.
Sep 23rd, 2022 | Views 31
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