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via Udemy |
Go to Course: https://www.udemy.com/course/deploy-machine-learning-model/
Certainly! Here's a comprehensive review and recommendation of the Coursera course on Machine Learning and Deep Learning Model Deployment, based on the provided details: --- **Course Review: Machine Learning & Deep Learning Model Deployment on Coursera** If you're looking to bridge the gap between building machine learning models and deploying them in real-world production environments, this course by Ankit Mistry is an excellent choice. It is designed for practitioners who want practical, hands-on knowledge about deploying models at scale across various cloud platforms. **What You'll Learn:** - **Fundamentals of Model Deployment:** The course starts with an introduction to what model deployment entails, including the underlying workflow in machine learning system design and options available at a cloud level. - **Practical Web Development Skills:** You will get a crash course on Flask, a Python web framework, enabling you to create web services that can serve your models. - **Hands-On Deployment Techniques:** The course covers deploying models using Flask, serializing/deserializing models with scikit-learn and Keras (TensorFlow), and testing APIs with Postman and Python's requests module. - **Cloud Deployment Platforms:** The curriculum includes deploying models on several prominent cloud platforms: - Heroku - Google Cloud (functions, app engine, managed AI) - Amazon Web Services (Lambda, ECS with Docker Containers) **Pros:** - **Comprehensive Content:** The course covers a broad scope—from model serialization to deploying on multiple cloud platforms. - **Hands-On Approach:** Emphasis on practical skills with real-world examples, including deploying models on cloud services. - **Skill Enhancement:** Adds valuable deployment skills to your machine learning toolkit, making you more versatile as a data scientist or ML engineer. - **30-Day Money-Back Guarantee:** Risk-free enrollment. **Cons:** - **Software Requirements:** The course requires downloading and installing Anaconda and Docker Desktop, which may need some prior setup knowledge. - **Prerequisites:** Basic understanding of Python, machine learning models, and web development will help, although the Flask crash course is designed for beginners. **Who Should Enroll?** - Data scientists and machine learning engineers looking to add deployment skills to their repertoire. - Developers interested in deploying ML models to cloud platforms. - Professionals wanting to understand the end-to-end process of bringing ML models from development to production. **Final Recommendation:** This course is highly recommended for anyone serious about deploying machine learning models in production environments. Its practical focus, covering multiple cloud services and deployment techniques, makes it a valuable addition to your professional skillset. Whether you're a beginner in web development or an experienced practitioner, the hands-on tutorials and real-world examples will significantly enhance your deployment capabilities. **Note:** Make sure to review the software requirements and check your organization's policies if you're using a corporate account to ensure compliance before downloading Anaconda or Docker Desktop. --- **Enroll today and take a significant step forward in your machine learning career!**
Disclaimer:This course requires you to download Anaconda and Docker Desktop from their official websites. If you are a Udemy Business user, please check with your employer before downloading any software to ensure compliance with your organization's policies.Hello everyone, welcome to one of the most practical course on Machine learning and Deep learning model deployment production level.What is model deployment:Let's say you have a model after doing some rigorous training on your data set. But now what to do with this model. You have tested your model with testing data set that's fine. You got very good accuracy also with this model. But real test will come when live data will hit your model. So This course is about How to serialize your model and deployed on server.After attending this course:you will be able to deploy a model on a cloud server. You will be ahead one step in a machine learning journey.You will be able to add one more machine learning skill in your resume.What is going to cover in this course?1. Course IntroductionIn this section I will teach you about what is model deployment basic idea about machine learning system design workflow and different deployment options are available at a cloud level.2. Flask Crash courseIn this section you will learn about crash course on flask for those of you who is not familiar with flask framework as we are going to deploy model with the help of this flask web development framework available in Python.3. Model Deployment with FlaskIn this section you will learn how to Serialize and Deserialize scikit-learn model and will deploy owner flask based Web services. For testing Web API we will use Postman API testing tool and Python requests module.4. Serialize Deep Learning Tensorflow Model In this section you will learn how to serialize and deserialize keras model on Fashion MNIST Dataset.5. Deploy on Heroku cloudIn this section you will learn how to deploy already serialized flower classification data set model which we have created in a last section will deploy on Heroku cloud - Pass solution.6. Deploy on Google cloudIn this section you will learn how to deploy model on different Google cloud services like Google Cloud function, Google app engine and Google managed AI cloud.7. Deploy on Amazon AWS LambdaIn this section, you will learn how to deploy flower classification model on AWS lambda function.8. Deploy on Amazon AWS ECS with Docker ContainerIn This section, we will see how to put application inside docker container and deploy it inside Amazon ECS (Elastic Container Services)This course comes with 30 days money back guarantee. No question ask. So what are you waiting for just enroll it today.I will see you inside class.Happy learningAnkit Mistry