From the course: AWS Certified Machine Learning Engineer Associate (MLA-C01) Cert Prep

Introduction to MLA

From the course: AWS Certified Machine Learning Engineer Associate (MLA-C01) Cert Prep

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Introduction to MLA

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(ethereal music) - [Instructor] Hello everyone, and welcome to the Machine Learning Engineer Associate course. In today's section, we're going to have a brief introduction of the MLA course itself, and we're going to have a brief introduction to the exam as well. So the main purpose of this course is to validate the ability to build, operationalize, deploy, and maintain machine learning solutions on AWS, and also to gain hands-on experience for the machine learning workflows. It's best suited for machine learning engineers, data scientists, DevOps engineers, and backend developers. And for the prerequisites, you would need at least one year of experience with Amazon SageMaker or related AWS machine learning services. So, we're going to dive into multiple fields, so first of all, we're going to talk about the data preparation, how to ingest the data, transform the data, and validate the data for machine learning modeling. For model development, we're going to talk about how to select the appropriate modeling approaches, how to train the machine learning models, how to perform hyperparameter tuning, and how to analyze the machine learning model performance. For the deployment section, we're going to talk about how to choose the infrastructure, how to provision the compute instances, and how to configure auto scaling, and how to deploy machine learning models. And you would learn how to set up CI/CD pipelines for continuous machine learning workflow orchestration. And for the monitoring and the security, you will learn how to monitor the machine learning systems and implement robust security practices. So now let's mention some of the recommended prerequisites for the IT knowledge from AWS. First of all, you'd have basic understanding of common machine learning algorithms and their use cases. You'd also have some of the data engineering fundamentals, so knowledge of common data formats, data ingestion, and data transformation. You'd also have knowledge of querying and transforming the data, and you would be familiar with software engineering best practices for modular and reusable code. Also for the development, the deployment, and the debugging. You'd also have experience with provisioning and monitoring cloud and on-premises machine learning resources, and you would have experience with CI/CD pipelines and infrastructure as code. You'd also have experience with code repositories for version control. Now for the recommended AWS knowledge, so you would have knowledge of SageMaker capabilities and algorithms for model building and model deployment. You'd be familiar with AWS data storage and processing services in order to prepare the data for machine learning modeling. And you'd have experience deploying applications and infrastructure on AWS. You'd also understand the monitoring tools that AWS has, so the CloudWatch and the CloudTrail services, for example. You'd also be familiar with AWS services for automation and orchestration of the CI/CD pipelines, and you would have knowledge of AWS security best practices for identity and access management, encryption, and data protection. So now let's talk about the domains included in the exam. The first basic domain would be the data preparation for machine learning. And this is a really important domain, because it's the basis of any machine learning workflow. You would need to have proper data in order to train good machine learning models. So in this domain, we're going to talk about the data ingestion, the transformation, and the data preparation. Next domain, we would have the machine learning model development, and in this domain you will learn how to select the appropriate machine learning model, how to train the machine learning model, and how to perform hyper parameter tuning while training, and even how to do performance analysis. For the deployment and orchestration domain, you would learn about the infrastructure itself, you would learn about the different deployment strategies that we have, and you will learn more about the CI/CD pipelines and how to use that in the machine learning workflows. The last domain would be the monitoring, the maintenance, and the security. So you will learn more about the model monitoring, how to optimize the cost, and how to secure the machine learning workflows. So a bit of a detailed information for the exam content. First of all, for the data preparation, we're going to learn about the different ingestion methods, the different data formats, and the different storage options. For the model development, we're going to learn about the different algorithms that we have and how to select the most suitable machine learning algorithm for our problem. We're going to learn about the different training methodologies, the hyperparameter tuning, and the version management. For the deployment and orchestration, we're going to learn how to deploy on AWS, so how to use the SageMaker endpoints, the containerization, and the auto scaling techniques. And we're going to learn how to script the infrastructure using the available infrastructure as code tools. For the monitoring and security, we're going to learn about the techniques for model inference monitoring, and we're going to learn about the cost tracking, the performance metrics, and how to secure the machine learning pipelines. For the machine learning associate course structures, we're going to have lessons, we're going to have hands-on labs for practical usage on AWS, and we're going to have our articles as well. So now, let's dive into details about the course itself. First of all, we would have the data storage and ingestion section. So in this section we're going to have an overview of the data storage, the ingestion, and the processing options. We also have the exploratory data analysis section, so we're going to learn about different techniques for understanding and transforming the data. For the machine learning and AI concepts, we're going to talk about the core machine learning principles and the AI applications. And for the managed AI services and SageMaker built-in algorithms, we would have an introduction to the AWS AI services, and also the built-in modeling tools for the Amazon SageMaker services section, we're going to talk about key Amazon SageMaker services for data labeling, model monitoring, and feature management. For the model deployment section, we're going to talk about different deployment strategies, like the real-time and batch inference. For the AWS infrastructure, the ML ops, and the orchestration section, we're going to talk about how to build scalable machine learning pipelines and CI/CD integration. For the foundation models and AWS Gen AI services, we're going to have an overview of foundation models and AWS Gen AI offerings. For monitoring and optimization, we're going to ensure performance, security, and cost efficiency of machine learning solutions. So now let's talk about the different exam question types that you could have. First of all, for multiple choices, you would choose only one correct response. For the multiple responses, you would choose two or more correct responses, which would be out of five or even more options. For the ordering question type, you would be placing three to five responses in their correct order. For the matching type, you would match responses with three to seven prompts. And for the case study, it would involve one scenario with two or more related questions.

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