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Study mode:
on
1
intro
2
preamble
3
development phase of ml algorithm
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natural language processing tasks
5
natural language processing : the concept
6
hugging face
7
use case 1 - example - text classification
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mlflow
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ml model experiments
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model development without mlflow
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mlflow tracking api: simple & pythonic!
12
model training framework
13
hugging face fine tuning nlp...
14
model evaluation
15
pytorch serving - ml models
16
productionizing in aws cloud
17
thank you
Description:
Explore a comprehensive conference talk on productionizing AI and ML algorithms in cloud environments. Delve into the development phase of machine learning algorithms, focusing on natural language processing tasks and concepts. Learn about Hugging Face, a popular platform for NLP models, and discover MLflow for managing the machine learning lifecycle. Examine practical use cases, including text classification, and understand the benefits of using MLflow's tracking API for model development. Gain insights into model training frameworks, fine-tuning NLP models with Hugging Face, and effective model evaluation techniques. Explore PyTorch serving for ML models and learn strategies for productionizing algorithms in AWS cloud. This 25-minute presentation by Deepak Karunanidhi at Conf42 Cloud Native 2024 offers valuable knowledge for data scientists and machine learning engineers looking to deploy AI and ML solutions in cloud environments.

Productionize AI and ML Algorithms in Cloud Environments

Conf42
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