Data Science Life Cycle | Productionization and Deployment of ML Models | EP #10

Описание: #Productionization_and_deployment_of_ML_Models

👇 Topics are covered in this video:
Data Science Life cycle, What are the steps we need to maintain for an ML project

🖐Related Topics:
1) deployment of ml models,
2) deployment of machine learning models,
3) deploying machine learning models to production,
4) deploy ml model to production,
5) how to deploy a machine learning model in production,
6) deploying a machine learning model

Previous Series:
👉 Mathematics for Data Science
1) Probability and Statistics for Data Science: https://bit.ly/3rvJ0Tu
2) Solving Optimization Problems: https://bit.ly/3ry4j6L

👉 Supervised Learning Models:
1) K-Nearest Neighbors: https://bit.ly/2YWgKwZ
2) Naïve Bayes Classifiers: https://bit.ly/2O9zQxs
3) Logistic Regression: https://bit.ly/3cT9tX6
4) Linear Regression: https://bit.ly/2YZB0O8
5) Support Vector Machine: https://bit.ly/3aJv95b
6) Decision Tree: https://bit.ly/3tF0FKo
7) Ensemble Models: https://bit.ly/3p0GyTk

👉 Unsupervised Learning Models:
1) K Means Clustering: https://bit.ly/3ruD7Gb
2) Hierarchical Clustering: https://bit.ly/3q2DirN
3) Density-based Clustering (DBSCAN): https://bit.ly/3qdZ7VH

👉 Recommendation System & Matrix Factorization: https://bit.ly/2NirSBN
👉Natural Language Processing: https://bit.ly/379QcwN

👉 Productionization and deployment of ML Models
1) Feature Engineering: https://bit.ly/3pjwpBk
2)Productionization and Deployment of ML Models: https://cutt.ly/JlzVDkT


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