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MLOps Engineering Labs Recap Part 2

Posted Mar 02
# Open Source
# Panel
# Model Serving
# Interview
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SPEAKERS
Laszlo Sragner
Laszlo Sragner
Laszlo Sragner
Founder @ Hypergolic

Laszlo worked as a quant researcher at multiple investment managers and as a DS at the world's largest mobile gaming company. As Head of Data Science at Arkera, he drove the company's data strategy delivering solutions to Tier 1 investment banks and hedge funds. He currently runs Hypergolic (hypergolic.co.uk) an ML Consulting company helping startups and enterprises bring the maximum out of their data and ML operations.

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Laszlo worked as a quant researcher at multiple investment managers and as a DS at the world's largest mobile gaming company. As Head of Data Science at Arkera, he drove the company's data strategy delivering solutions to Tier 1 investment banks and hedge funds. He currently runs Hypergolic (hypergolic.co.uk) an ML Consulting company helping startups and enterprises bring the maximum out of their data and ML operations.

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Artem Yushkovsky
Artem Yushkovsky
Artem Yushkovsky
MLOps Engineer @ Neu.ro
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Paulo Maia
Paulo Maia
Paulo Maia
Data Scientist @ NILG.AI
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Dimitrios Mangonskis
Dimitrios Mangonskis
Dimitrios Mangonskis
MLE @ Big 4
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SUMMARY

This is a deep dive into the most recent MLOps Engineering Labs from the point of view of Team 3. Artem, Dimi, Laszlo, and Paulo chose to use Yelp Review dataset for training an NLP model for classifying provided texts as positive or negative reviews. The data includes reviews on restaurants, museums, hospitals, etc., and the number of stars associated with this review (0–5). Team 3 modeled this task as a binary classification problem: determining whether the review is positive (has >=3 stars) or negative (otherwise). Check the diagram Link here., This is a deep dive into the most recent MLOps Engineering Labs from the point of view of Team 3. Artem, Dimi, Laszlo, and Paulo chose to use Yelp Review dataset for training an NLP model for classifying provided texts as positive or negative reviews. The data includes reviews on restaurants, museums, hospitals, etc., and the number of stars associated with this review (0–5). Team 3 modeled this task as a binary classification problem: determining whether the review is positive (has >=3 stars) or negative (otherwise). Check the diagram Link here. , This is a deep dive into the most recent MLOps Engineering Labs from the point of view of Team 3. Artem, Dimi, Laszlo, and Paulo chose to use Yelp Review dataset for training an NLP model for classifying provided texts as positive or negative reviews. The data includes reviews on restaurants, museums, hospitals, etc., and the number of stars associated with this review (0–5). Team 3 modeled this task as a binary classification problem: determining whether the review is positive (has >=3 stars) or negative (otherwise). Check the diagram Link here., This is a deep dive into the most recent MLOps Engineering Labs from the point of view of Team 3. Artem, Dimi, Laszlo, and Paulo chose to use Yelp Review dataset for training an NLP model for classifying provided texts as positive or negative reviews. The data includes reviews on restaurants, museums, hospitals, etc., and the number of stars associated with this review (0–5). Team 3 modeled this task as a binary classification problem: determining whether the review is positive (has >=3 stars) or negative (otherwise). Check the diagram Link here.

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