Deploying AI & Machine Learning Models for Business | Python

Learn to build Machine Learning, Deep Learning & NLP Models & Deploy them with Docker Containers (DevOps) (in Python)

4.54 (2044 reviews)

Udemy

platform

English

language

Data Science

What you will learn

How to synchronize the versatility of DevOps & Machine Learning

Master Docker , Docker Files, Docker Applications & Docker Containers (DevOps)

Flask Basics & Application Program Interface (API)

Build & Deploy a Random Forest Model

Build a Text based (Natural Language Processing : NLP ) CLUSTERING (KMeans) Model and expose it as an API

Build an API which will run a Deep Learning Model (Convolutional Neural Network : CNN) Model for Image Recognition & Classification

Course Gallery

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Charts

Students

Price

Rating & Reviews

Enrollment Distribution

Comidoc Review

Our Verdict

This Udemy course bridges the gap between machine learning model creation and deployment using Docker containers. It is an engaging and practical resource for beginners seeking to develop a foundational understanding of ML deployment processes, despite minor issues with closed captions, audio levels, and instructor responsiveness. However, its limited cloud deployment exploration falls short in mirroring real-world scenarios, making it less suitable for those pursuing advanced or specialized knowledge.

What We Liked

Comprehensive coverage of deploying ML models through Docker containers, providing a valuable end-to-end overview.
Instructor's problem resolution approach helps build practical skills and encourages critical thinking for problem-solving.
Hands-on experience with various tools like Flask, Docker, and deep learning models allows learners to explore different techniques.
Clear explanations of fundamental concepts making the course suitable for beginners in ML deployment.

Potential Drawbacks

Limited exploration of cloud environments and real-world scenarios for deploying solutions hinders comprehensive understanding.
Lack of a git repository and insufficient responsiveness from the instructor may hinder collaborative learning experiences.
Minor issues like low audio levels and closed caption quality may distract learners from focusing on key course concepts.
Certain lessons might be too superficial, potentially causing difficulties in deploying solutions for inexperienced students.