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via Udemy |
Go to Course: https://www.udemy.com/course/mlops-exhaustive-guide-aws-gcp-apple-cases/
Certainly! Here's a comprehensive review and recommendation for the Coursera course on MLOps: --- **Course Review: Mastering MLOps and Cloud Deployment** If you're looking to deepen your understanding of deploying, managing, and monitoring machine learning models in real-world environments, this Coursera course is an outstanding choice. It offers a comprehensive journey through the modern practices of MLOps, combining theoretical foundations with practical applications that are crucial for any aspiring data scientist or ML engineer. **What makes this course stand out?** 1. **Updated and Industry-Relevant Content:** The course consistently evolves, with recent updates incorporating cutting-edge topics like the "MLOps Market Overview," exploring industry trends, salary expectations, and job roles. The latest modules include practice-focused topics such as "Data Drifts," integrated with advanced tools like EvidentlyAI and MLFlow, to help you maintain the robustness of your ML systems over time. 2. **Hands-On Practice:** Beyond theory, this course emphasizes practical application. You will learn to deploy ML models as web apps on AWS, package models into Docker containers, and set up CI/CD pipelines. These skills are vital for transitioning from academic understanding to professional implementation. 3. **Diverse Technologies and Cloud Platforms:** Covering popular tools and platforms like AWS SageMaker, GCP Vertex AI, MLFlow, Kubeflow, Azure Machine Learning, and DVC, the course ensures you're familiar with the ecosystem of modern MLOps solutions. This breadth prepares you to integrate and choose the right tools for different project requirements. 4. **Focus on Model Monitoring and Data Quality:** Understanding data drift is key to maintaining model performance post-deployment. The course’s detailed modules on data analysis, drift detection, and advanced performance diagnostics using EvidentlyAI add significant value. 5. **Career Advancement Opportunities:** By completing this course, you'll gain skills that are highly sought after in the industry. You’ll be capable of setting up end-to-end ML pipelines, operating model registries, and deploying scalable ML-powered web applications—crucial competencies for roles in AI/ML operations. **Who should consider this course?** - Data scientists and ML Engineers eager to operationalize models effectively - DevOps and software engineers transitioning into AI/ML roles - Anyone interested in cloud-based ML deployment and MLOps best practices - Professionals aiming to stay ahead of industry trends in AI and automation **Final Thoughts & Recommendation:** This course offers a well-rounded, practical, and continuously updated curriculum prepared by an instructor with real-world experience. Its mix of broad technological coverage and deep dives into critical topics like data drift and experiment tracking makes it ideal for those who want to not only learn but also apply MLOps principles effectively. **Would I recommend it?** Absolutely. Whether you're starting your MLOps journey or looking to refine your deployment and monitoring skills, this course will equip you with the knowledge and tools to excel in the field. --- Feel free to ask if you'd like a personalized learning plan or more details about specific modules!
xxxxxxxxxxxx[Course Updates]:- 08.2023: + New Extra materials in Chapters #3 & #7- 10.2023: + "MLOps Market Overview" chapter. Learn key Market stats, trends + Salaries & Role Expectations- 11.2023: + "Data Drifts" section (+1 hour of content). Learn to discover data drifts & deal with them.EvidentlyAI + MLFlow integration- 12.2023: Course Structure Updates + new "What you will learn" video- 02.2024: + "MLFLow" Section + "ML-powered Web App Deployment to AWS" SectionxxxxxxxxxxxWould you like to learn best practices of Automation & ML models Deployment?Maybe you would also like to practice doing it?You've come to the right place!There's no better way to achieve that than by creating a strong theoretical foundation and getting hands dirty by applying newly learnt concepts in practice straight away!MLOps has been helping me automate & roll out robust, easily maintainable and state-of-the-art ML in IT, Food and Travel industries over the last 6 years.With the help of modern Cloud Computing and open source software I've brought live dozens of ML research projects, successfully solved very complex Business challenges and even changed the country where I live & work!There are many different technologies powering modern ml ops. Some of them are: AWS Sagemaker, Kubeflow, Azure machine learning, mlflow, GCP Vertex AI, dvc etc. We will cover many of them and see how they work together.The course will also teach you about Data Drifts: a common issue arising in the world of Machine Learning models. We will learn what these are, how to discover them in a timely manner and what actions to take to mitigate their effect on model's performance.We will use a variety techniques for that: from simple visual analysis using histograms & box plots all the way to learning EvidentlyAI.Additionally, we will look into using MLFlow to track your ML experiments. There're 2 ways to do that covered in the Course: using MLFlow locally on your machine and using SaaS service called dagshub.Finally, we will deploy an ML-powered Web App (Flask micro-service + UI) to the internet using AWS Cloud.Join me in this fun and Industry-shaped course to get new skills and improve your MLOps & Cloud acumen!By the end of this course you will be able to:Set up CI & CD pipelinesPackage ML models into DockerRun AutoML locally & in the CloudTrain ML models for Apple devicesMonitor and Log ML experiments with mlflow frameworkSet up and manage MLOps pipelines in AWS SageMakerOperate Model Registry & Endpoints in GCP VertexAIUse EvidentlyAI to discover Data Drifts and conduct advanced analysis of ML model performanceUse EvidentlyAI together with MLFlow to track ML experimentsUse MLFLow to track your ML ExperimentsLearn dagshub - a Cloud-based ML tracking environment building on top of MLFlowDeploy ML-powered Web App with UI to AWS CloudBoost your Career and MLOps studying efficiencyThe course isn't static! I collect students' feedback and periodically update the materials: add new lectures and practice cases!