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Building and End-to-end MLOps Pipeline

Posted Jun 09, 2023 | Views 834
# MLOps
# ML Project Lifecycle
# Neptune Ai
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Aurimas Griciūnas
Founder & CEO @ SwirlAI

Aurimas Griciūnas is an AI expert, LinkedIn Top Voice in AI, and the founder of SwirlAI. He previously served as Chief Product Officer at Neptune.ai where he worked closely with top ML teams to scale infrastructure, evaluation, and LLMOps practices across industries. With over a decade of experience at the intersection of data science, machine learning, and software engineering, Aurimas has led AI initiatives in both startups and enterprise environments. His mission is to bridge the gap between hype and reality by teaching engineers how to build systems that work in the real world.

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SUMMARY

Aurimas Griciūnas, a Senior Solutions Architect at Neptune Ai and the CEO of Swirl AI delivers a talk on MLOps (Machine Learning Operations) and the lifecycle of ML projects. He highlights the stages involved in the ML project lifecycle, including feedback loops, deployment, monitoring, and experimentation. Griciūnas explores training and inference pipelines, specifically batch pipelines and real-time inference pipelines. The talk emphasizes the significance of CI/CD (Continuous Integration/Continuous Deployment) and high ML maturity pipelines while acknowledging the challenges associated with their implementation.

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