MLOps Platforms: Amazon SageMaker and Azure ML

Go to Course: https://www.coursera.org/learn/mlops-aws-azure-duke

Introduction

### Course Review: MLOps Platforms: Amazon SageMaker and Azure ML In the rapidly evolving world of machine learning, understanding how to effectively manage the lifecycle of machine learning models is essential. The course **"MLOps Platforms: Amazon SageMaker and Azure ML"** on Coursera emerges as a comprehensive guide to mastering the deployment and operationalization of machine learning solutions on two of the leading cloud platforms, AWS and Azure. Whether you are a data scientist, a software engineer, or someone aspiring to enter the domain of machine learning, this course promises to equip you with the practical skills necessary for success in a production environment. #### Course Overview This course is structured in a way that progressively builds your knowledge and skills in using Amazon SageMaker and Azure Machine Learning. It not only focuses on the theoretical aspects but also emphasizes practical applications through hands-on exercises. The course is particularly beneficial for individuals preparing for AWS or Azure machine learning certifications, providing insights into essential tools and practices. #### Syllabus Breakdown The course is divided into several weeks, each focusing on crucial components of MLOps: 1. **Data Engineering with AWS Technology** - This initial week sets a strong foundation by teaching you how to construct data engineering solutions using AWS. You will engage in building a data engineering pipeline with AWS Step Functions and AWS Lambda, gaining a clear understanding of how data flows through systems. 2. **Exploratory Data Analysis with AWS Technology** - You will learn to apply data engineering solutions by creating data science notebooks. This week emphasizes the importance of exploratory data analysis (EDA) in understanding datasets and preparing them for modeling. 3. **Modeling with AWS Technology** - In this segment, you will delve into machine learning modeling using AWS technologies. The hands-on exercise of building a linear regression model executed through a command-line tool enhances your command over practical modeling techniques. 4. **MLOps with AWS Technology** - A pivotal week dedicated to deploying and operationalizing machine learning solutions. Fine-tuning a Hugging Face model with SageMaker Studio Lab is a highlight that reinforces your skills to bring models into production effectively. 5. **Machine Learning Certifications** - The final week addresses the various machine learning certifications available from major cloud providers. This is an invaluable resource for learners looking to advance their careers and demonstrates how to leverage AutoML and related services in MLOps. #### Pros - **Comprehensive Content:** The course covers a wide range of critical skills, from data engineering to deployment, ensuring a holistic understanding of the MLOps lifecycle. - **Hands-on Learning:** Practical exercises promote deeper learning and retention of concepts, preparing students for real-world applications. - **Certification Preparation:** Ideal for those looking to gain industry-recognized certifications, enhancing your qualifications in the job market. #### Cons - **Focused on AWS:** While the course also touches on Azure ML, a significant portion is dedicated to AWS, which may limit exposure to Azure's MLOps capabilities. - **Pace May Vary:** Depending on your prior knowledge of AWS or Azure, some learners might find certain segments either too easy or challenging. ### Recommendation I highly recommend **"MLOps Platforms: Amazon SageMaker and Azure ML"** for anyone serious about advancing their career in machine learning and operations. This course provides the tools, knowledge, and hands-on experience necessary to thrive in today's data-driven landscape. Whether you are an aspiring data scientist or an experienced professional looking to formalize your skills, you will find this course to be an invaluable resource that enriches your understanding of MLOps. If you are eager to become proficient in deploying and managing machine learning models using leading cloud technologies, don’t hesitate to enroll. It’s an investment in your future that you won’t regret!

Syllabus

Data Engineering with AWS Technology

This week you will learn how to build data engineering solutions on AWS and apply it by building a data engineering pipeline with AWS Step Functions and AWS Lambda.

This week you will compose data engineering solutions using AWS technology and apply it by building data science notebooks.

This week you will compose machine learning modeling solutions using AWS technology and apply it by building a linear regression model that runs inside a command-line tool.

This week you will learn to deploy and operationalize machine learning solutions using AWS technology and apply it by fine-tuning a Hugging face model using Sagemaker Studio Lab.

This week you will learn about Machine Learning certifications from the major cloud providers and how to apply them to MLOps. You will learn about services related to Machine Learning and ML Engineering tasks like AutoML and how they apply to the certifications.