Welcome to the Aiku Youtube Chanel. This is our second video in our series on Machine Learning in Production. In the first video, we introduced MLOps. Today, we want to discuss the challenges that come with running ML applications in a production environment. Click the subtitle (English-UK) to watch with text.
Running ML in a production environment can present a plethora of challenges. Whether you are in the preparation stage or already working with Machine Learning in production, this video provides valuable insights into the common challenges you may encounter during implementation. Join us as we delve into the potential challenges you will come across when deploying ML in your operations.
Whether you're a data scientist, engineer, or simply curious about the intersection of machine learning and operations, this video is tailored for you. Gain valuable insights, learn practical tips, and stay at the forefront of the MLOps revolution.
Videos in our MLOps Series:
What is MLOps? https://www.youtube.com/watch?v=BSLWcc_wKjw&t=25s
Challenges of MLOps https://www.youtube.com/watch?v=kcxuETFf8uw&t=204s
Values and Principles of MLOps https://www.youtube.com/watch?v=oRh-gSwOAxc&t=112s
Why do We Need MLOps? https://www.youtube.com/watch?v=gmBBHRgSesY&t=72s
When do We Need MLOps? https://www.youtube.com/watch?v=WeFPi7Vi_Qc
References
Mark Treveil, Nicolas Omont, Clément Stenac, Kenji Lefevre, Du Phan, Joachim Zentici, Adrien Lavoillotte, Makoto Miyazaki, Lynn Heidmann, 2020. Introducing MLOps. https://www.oreilly.com/library/view/introducing-mlops/9781492083283/
David Sweenor, Steven Hillion, Dan Rope, Dev Kannabiran, Thomas Hill, Michael O'Connell, 2020. MLOps: Operationalising Data Science. https://www.oreilly.com/library/view/ml-ops-operationalizing/9781492074663/
Noah Gift, Alfredo Deza, 2021. Practical MLOps. https://www.oreilly.com/library/view/practical-mlops/9781098103002/
Google. 2023. MLOps: Continuous delivery and automation pipelines in machine learning. https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning
Larysa Visengeriyeva, Anja Kammer, Isabel Bär, Alexander Kniesz, Michael Plöd, 2023. Machine Learning Operations. https://ml-ops.org/
About Aiku
To learn more about MLOps and explore how Aiku can help you leverage its power, visit our website at https://aiku.tech . Unlock the potential of seamless and efficient machine learning operations with Aiku. Watch the video now and enhance your understanding of MLOps.
To learn more on Data Science, Artificial Intelligence, Machine Learning and CICD for ML, check out our blogposts here: https://aiku.tech/aiku-blog/
To learn more about our solutions, CICD for Machine Learning, click the link here: https://aiku.tech/continuous-integration-and-deployment-for-machine-learning/
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