Data Science Duniya

TensorFlow Model Experiment Tracking using MLFlow

June 13, 2023 Ashutosh Tripathi

Experiment tracking is very important steps when it comes to model deployment in production. When we make model ready for deployment we compare the performances of different recorded experiments and check which one is the optimal one. That is where it becomes very much important to understand how to do experiment tracking for different models trained under different machine learning frameworks. In this post I am explaining step by step approach to do experiment tracking for TensorFlow Based image classification model.

Machine Learning Model Deployment using Docker Container

March 25, 2023 Ashutosh Tripathi

Model deployment is the next and very important steps once you finalized your model training and development. There are many methods available to deploy the models depending upon the type of serving. There are many serving methods like batch serving, online serving, real time serving or live streaming based serving. In this article I am going to explain one of the deployment mechanism which does online serving using APIs. So I will be explaining how to deploy models using Docker container and run them on production efficiently and reliably.

Linear Regression Interview Questions and Answers

August 1, 2022 Ashutosh Tripathi

While working in regression analysis, you should be familiar with some very basic but very impactful concepts. In machine learning interviews, you can always expects questions from regression analysis. Regression analysis also develop the basic understanding of machine learning model building as we mostly start our machine learning journey from regression analysis only.

Feature Store in Machine Learning

July 25, 2022 Ashutosh Tripathi

Feature store in machine learning is the concept to store features in both online and offline stores for model training and serving purposes. Feature store make sure to provide the consistency between the data used for model training and the data used during online serving to models. In other words, it guarantees that you’re serving the same data to models during training and prediction, eliminating training-prediction skew. Feast is one of the open source tools used for feature store.

Experiment Tracking using MLFlow in Machine Learning

July 18, 2022 Ashutosh Tripathi

Experiment tracking is the process of recording all the important components such as hyper parameters, metrics, models and artifacts like plots PNG images, files etc. Experiment tracking helps to reproduce the old results by using the stored parameters.

Statistics (Topics) for Machine Learning

June 22, 2022 Guest Author

Statistics is a subject and a branch of mathematics that is related to all the collection, analysis, interpretation, and visualization of empirical data, and there are two major areas of statistics are descriptive statistics and inferential statistics. If we talk about, descriptive statistics are used to describe the characteristics of sample and population data (what has happened). These properties are used by inferential statistics to test hypotheses, reach conclusions, and make predictions (what can you expect).

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