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AWS

Machine Learning & MLOps Foundations

Whizlabs Instructor

"AWS: Fundamentals of Machine Learning & MLOps is the first course of Exam Prep (MLA-C01): AWS Certified Machine Learning Engineer – Associate Specialization. This course assists learners in building foundational knowledge of core machine learning concepts, including types of learning, data preparation, model evaluation, and operationalization. Learners gain a strong understanding of the difference between AI, Deep Learning, and Machine Learning, and how to identify and apply real-world ML use cases using AWS services.

This course allows learners to explore key topics such as model selection, classification workflows, confusion matrices, and regression evaluation techniques. In addition, learners are introduced to the concepts of MLOps and the AWS services used to streamline ML deployment and monitoring in production environments.

The course is divided into two modules, and each module is further segmented by Lessons and Video Lectures. This course facilitates learners with approximately 2:30–3:00 hours of video lectures that provide both theory and hands-on knowledge using AWS tools. Also, Graded and Ungraded Quizzes are provided with every module to test the understanding and application readiness of learners."

Module 1: Machine Learning and MLOps Concepts

Module 2 : Model Development & Evaluation Techniques

By the end of this course, learners will be able to:

- Apply foundational machine learning and MLOps concepts using AWS tools

- Build and evaluate ML models with services like Amazon SageMaker

- Understand end-to-end ML workflows, including data preparation, model training, and deployment

- Strengthen their preparation for the AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam

This course is ideal for aspiring ML practitioners, data engineers, and developers with 6 months to 1 year of AWS experience who want to build practical skills in machine learning and MLOps. It also supports learners preparing for the AWS Certified Machine Learning Engineer – Associate (MLA-C01) exam and professionals seeking hands-on knowledge of implementing and managing ML workflows using AWS services.

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What's inside

Syllabus

Machine Learning Concepts & Use Cases [Machine Learning and MLOps Concepts & Use Cases]

Welcome to Week 1 of the AWS: Machine Learning & MLOps Foundations course. This week, you’ll explore the fundamentals of Machine Learning (ML) and how it differs from AI and Deep Learning. We'll cover types of data, types of ML (supervised, unsupervised, reinforcement), and how to identify suitable ML use cases. You’ll walk through the ML lifecycle—from data ingestion to deployment—and get introduced to key AWS services that support ML workflows. We’ll also touch on MLOps concepts and AWS tools that help scale and manage ML models in production.

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Career center

Learners who complete AWS: Machine Learning & MLOps Foundations will develop knowledge and skills that may be useful to these careers:

Machine Learning Operations Engineer

A Machine Learning Operations Engineer specializes in the deployment, monitoring, and maintenance of machine learning models in production. This course is exceptionally well-suited for an aspiring Machine Learning Operations Engineer, as "MLOps" is explicitly covered, including the concepts and AWS services used to streamline ML deployment and monitoring. Learners develop a strong understanding of end-to-end ML workflows, encompassing data preparation, model training, and crucial operationalization aspects. By applying foundational MLOps concepts using AWS tools and exploring topics like batch versus real-time inferencing, participants acquire practical skills directly applicable to creating robust and scalable ML production systems.

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Machine Learning Engineer

As a Machine Learning Engineer, you design, build, and deploy machine learning systems and models. This course directly addresses core competencies for a Machine Learning Engineer by building foundational knowledge of machine learning concepts, including various types of learning, data preparation, and model evaluation techniques. Learners gain practical experience with AWS services like Amazon SageMaker for building and evaluating models, which is crucial for operationalizing ML solutions. The emphasis on end-to-end ML workflows, from data ingestion to deployment and monitoring in production environments through MLOps concepts, provides an essential skillset for success in this role, particularly for those looking to leverage AWS.

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Applied Scientist - Machine Learning

An Applied Scientist Machine Learning researches, designs, and implements machine learning algorithms and systems to solve specific real-world problems. This course provides a strong foundation in applying foundational machine learning and MLOps concepts using AWS tools, directly aligning with the practical, implementation-focused nature of an Applied Scientist role. Learners explore real-world ML use cases and gain hands-on knowledge of building and evaluating ML models with services like Amazon SageMaker. Understanding end-to-end ML workflows, including data preparation and deployment, equips individuals to translate theoretical models into deployable and scalable solutions, making this course particularly relevant for those aiming for a hands-on applied science career. This role typically requires an advanced degree.

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Data Scientist Machine Learning Focus

A Data Scientist with a Machine Learning Focus analyzes complex datasets and builds predictive models to extract insights and drive decisions. This course helps build a foundation in core machine learning concepts, including types of learning, model selection, classification workflows, and regression evaluation techniques, which are fundamental to the work of a Data Scientist. Although Data Scientists often delve deeply into statistical theory and exploratory data analysis, this course's focus on building and evaluating ML models with services like Amazon SageMaker and understanding the ML lifecycle from data ingestion to deployment provides practical skills that may be useful for implementing and operationalizing models.

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Artificial Intelligence Engineer

An Artificial Intelligence Engineer designs, develops, and implements AI-driven solutions and applications. While AI is a broad field, machine learning forms a core component, and this course helps clarify the difference between AI, Deep Learning, and Machine Learning. The course assists learners in building foundational knowledge of core machine learning concepts, model evaluation, and operationalization. By exploring how to identify and apply real-world ML use cases using AWS services, individuals can develop practical skills in a significant subset of AI engineering. The specific focus on building and evaluating ML models and understanding end-to-end ML workflows prepares aspiring Artificial Intelligence Engineers to contribute to the creation and deployment of intelligent systems.

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Cloud Engineer Machine Learning Specialization

A Cloud Engineer with a Machine Learning Specialization focuses on designing, implementing, and managing cloud infrastructure specifically tailored for machine learning workloads. This course deeply explores AWS services used to support ML workflows, deployment, and monitoring in production environments, making it highly relevant. Learners apply foundational machine learning and MLOps concepts using AWS tools, gaining a strong understanding of the end-to-end ML lifecycle from data ingestion to deployment. This specific knowledge of AWS ML services and MLOps principles directly equips a Cloud Engineer to build, manage, and optimize the underlying cloud architecture required for scalable and efficient machine learning operations.

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Solutions Architect Machine Learning

A Solutions Architect Machine Learning designs and implements scalable, secure, and cost-effective machine learning solutions on cloud platforms. This course is highly beneficial for an aspiring Solutions Architect focused on ML, as it provides foundational knowledge of core machine learning concepts and, crucially, a deep exploration of AWS services used to streamline ML deployment and monitoring. Learners develop a strong understanding of end-to-end ML workflows, including data preparation, model training, and deployment. This comprehensive view of the ML lifecycle and specific AWS tools enables a Solutions Architect to effectively design robust and efficient ML architectures, align solutions with business needs, and advise on best practices for operationalizing ML models within the AWS environment.

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Technical Trainer Machine Learning

A Technical Trainer specializing in Machine Learning educates professionals and students on ML concepts, tools, and best practices. This course directly aligns with the needs of a Technical Trainer, as it is designed to build foundational knowledge of core machine learning concepts, including types of learning, data preparation, model evaluation, and operationalization. Learners gain hands-on knowledge using AWS tools and explore key topics such as model selection and MLOps concepts. The structured nature of the course, with modules, lessons, and quizzes, mirrors the content delivery a trainer provides, and the focus on AWS Certified Machine Learning Engineer Associate exam preparation provides a clear path for teaching practical, industry-relevant skills.

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Data Engineer Machine Learning Pipelines

A Data Engineer specializing in Machine Learning Pipelines designs, constructs, and maintains the infrastructure for data ingestion, processing, and transformation essential for ML models. This course provides a valuable perspective for a Data Engineer, as it covers critical aspects of the end-to-end ML workflow, including data preparation. Learners gain a strong understanding of the ML lifecycle from data ingestion to deployment, which is crucial for building robust and efficient data pipelines that feed into machine learning systems. The introduction to MLOps concepts and AWS tools that help scale and manage ML models in production provides insights into how well-structured data pipelines contribute to successful model operationalization within the AWS ecosystem.

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Technical Product Manager Machine Learning Products

A Technical Product Manager for Machine Learning Products defines the strategy, roadmap, and features for products that leverage artificial intelligence and machine learning. This course is particularly helpful for a Technical Product Manager because it provides foundational knowledge of core machine learning concepts, the differences between AI, Deep Learning, and Machine Learning, and how to identify real-world ML use cases. Understanding the end-to-end ML workflow, from data preparation to model deployment and operationalization using MLOps concepts, gives a Product Manager the technical depth needed to make informed decisions, communicate effectively with engineering teams, and anticipate challenges in bringing ML-powered products to market using AWS services.

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Quantitative Analyst (Machine Learning)

A Quantitative Analyst specializing in Machine Learning applies advanced mathematical, statistical, and computational methods, often in financial contexts, using ML models to analyze markets, develop trading strategies, or manage risk. This course may be useful for a Quantitative Analyst because it covers core machine learning concepts, model selection, classification workflows, confusion matrices, and regression evaluation techniques, which are directly applicable to quantitative modeling. While focusing on AWS tools, the fundamental understanding of model development and evaluation provided helps build a foundation in the practical application of ML. This role typically requires an advanced degree in a quantitative field such as mathematics, statistics, or physics.

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Software Engineer Backend Machine Learning Integration

A Software Engineer Backend Machine Learning Integration is responsible for seamlessly embedding machine learning models into larger software systems and ensuring their efficient interaction. This course helps build a foundation in understanding the end-to-end ML workflow, including data preparation, model training, and crucial deployment strategies. Learners are introduced to MLOps concepts and the AWS services used to streamline ML deployment and monitoring in production environments. This knowledge is highly relevant for a Software Engineer, as it directly informs how to design robust APIs, manage model inference, and implement monitoring hooks, ensuring that ML models are effectively integrated and operational within a backend service architecture using AWS tools.

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Machine Learning Research Scientist

A Machine Learning Research Scientist focuses on developing novel algorithms, pushing the boundaries of machine learning theory, and publishing findings. This course may be useful for a Machine Learning Research Scientist to build foundational knowledge of core machine learning concepts, including types of learning, model evaluation techniques, and the differences between AI, Deep Learning, and Machine Learning. While this course emphasizes practical application and AWS tools rather than theoretical research, understanding fundamental ML workflows, data preparation, and model development provides a valuable context for those who may transition into more theoretical roles. This role typically requires an advanced degree, often a PhD.

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Data Analyst Machine Learning Insights

A Data Analyst specializing in Machine Learning Insights interprets the outputs of ML models to extract actionable business intelligence and communicate findings to stakeholders. This course may be helpful for a Data Analyst because it provides foundational knowledge of core machine learning concepts, including data preparation, model evaluation techniques like confusion matrices and regression metrics, and understanding model selection. While a Data Analyst typically focuses on interpreting data rather than building models, understanding the principles of model development and how ML models are operationalized gives them deeper insight into the data they receive from ML systems, allowing for more nuanced analysis and communication of ML-driven recommendations.

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AI Ethics and Governance Specialist

An AI Ethics and Governance Specialist develops and implements policies and frameworks to ensure artificial intelligence systems are developed and deployed responsibly, equitably, and transparently. This course may be useful for an AI Ethics and Governance Specialist to build foundational knowledge of core machine learning concepts, including model selection, evaluation techniques, and understanding the differences between AI, Deep Learning, and Machine Learning. While the course does not explicitly cover ethics, a deep understanding of the end-to-end ML workflow, from data preparation to model deployment and operationalization, is crucial for identifying potential ethical issues, biases, and governance challenges inherent in ML systems and their real-world application.

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Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in AWS: Machine Learning & MLOps Foundations.

Artificial Intelligence

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The definitive benchmark textbook of modern artificial intelligence, rigorously unifying search algorithms, probabilistic reasoning, knowledge representation, and machine learning into an elegant, coherent agent-based framework.

Artificial Intelligence: A Modern Approach

Hardcover

$$$$

Hands-On Machine Learning with Scikit-Learn, Keras,...

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The premier applied machine learning masterwork, taking practitioners through intuitive concept breakdowns, production-grade Scikit-Learn workflows, and deep neural architecture implementations.

Hands-On Machine Learning with Scikit-Learn, Keras,...

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