MLOps and comparison of different models

Artificial Intelligence has the potential to change the way humans interact and behave ...

The automatic collection of data, it's cleaning, processing or feature engineering and the ever increasing compute-power are improving the existing models at an exponential rate.

• Date: January 1, 2021
• Author: Irfan TOOR
• keywords: MLOps, MLFlow, traçage, organisation, modèles m/l, optimisation, hyper-paramètres, versioning, autolog, grid-search, reproduction, erreurs, package, partage, model simple, LSTM, BERT, transfer-learning, comparaison, embeddings, word2vec, Glove.6B, TweetEval, roBERTa, sentiment, sst-2, analysis

A B S T R A C T

"Je crois qu'on ne perdure pas si on perd les repères."

— David de Rothschild

Artificial Intelligence has the potential to change the way humans interact and behave with the existing or the new phenomenas of the world. The automatic collection of data, it's cleaning, processing or feature engineering and the ever increasing compute-power are improving the existing models at an exponential rate. More inclined we are to this ever changing environment and the it's associated dependencies, which are changing at a phenomenal rate, more we are prone to the risks associated with finding ourself in the unknown lands and the territories of the machine control, thereby falling prey to this self invented trap and to an ultimate oblivion.

AI is not a new technology, which is taking the world by storm but its the logical evolution of what we have already been creating through machine learning models. We, the humans, have understood after the experience of millenniums that keeping track of whatever we are doing is fundamental to our boundless potential, flawless evolution and behemoth success in every domain we touch. So, its quite natural that we transfer and apply the knowledge and the concepts learned in one domain to another, which could not have been possible without tracking every bit of our experiments -- be it psychology or science.

The concepts of DevOps have found their way into the M/L world, not only facilitating the versioning, logging and tracking of models but are extended into keeping track of the data, the parameters and the performance of these models as well. This concept of tracking and managing all of the life-cycle of M/L models, the associated data, their versioning, performance and parameter tracking and even staging from development to deployment is handled by Machine/Learning Operations i.e MLOps. We will discuss about MLOps, its deployment, and its usage through the comparison of three models namely: Simple, LSTM and BERT.

1. I N T R O D U C T I O N

1.1 MLOps - Experiment tracking

What is MLOps?

MLOps is to Machine Learning models, as DevOps is to development. Since the requirements for Machine Learning are more than that of the Development, the MLOps have more to manage and handle as compared to DevOps. Its not about keeping track of the code, the dependencies or staging alone, but that of the data versioning, hyper-parameters, performance metrics also.

Furthermore the life cycles of the models, their different versions, development, staging and deployment etc. are all managed by a combination of tools, protocols, policies and practices known as MLOps.

How an MLOps paradigme helps us?

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