September 27, 9am PST / 6pm CET

MLOps: Maturity level 0 to 1

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Webinar hosts

oscar ornberg

Oscar Örnberg

Solutions Engineering Team Lead at SuperAnnotate
david arakelyan

David Arakelyan

ML Project Coordinator
vahan petrosyan

Vahan Petrosyan

Co-founder & CTO at SuperAnnotate

Webinar speaker

luis riera

Luis Riera

AI Solutions Engineer at SuperAnnotate

Setting up an efficient ML pipeline is crucial for operationalizing services and products. The ability to automate the necessary parts of the pipeline allows ML Engineers to reliably flow data from source(s) to destination(s). Intermediate steps in the pipeline allow for transformation, versioning, and storing for later consumption of data. ML training experiments can be more easily integrated into a continuous process, instead of relying on manual execution of partial portions of the ML Pipeline.

We will walk through a Sports Analytics project that uses real-world data to showcase the difference between an ML Pipeline at maturity stage 0 and maturity stage 1. We will see how to integrate and leverage SuperAnnotate as the Labeling/QAing/Feature Extraction tool in our ML Workflow. The data extracted with the help of SuperAnnotate will serve as the input for training a series of models which in turn are used to implement the real-world solution to our Sports Analytics problem.

  • Named Entity Recognition/NLP (bounding box annotation of named entities)
  • Optical Character Recognition/OCR (character annotation of the words from the annotated bounding box)

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