AWS Certified ML Engineer Associate - Theory,Hands-On, Exams

Discover how Amazon Athena delivers serverless, ad hoc SQL queries on data in S3 with Spark integration, plus metadata managed by the Glue Data Catalog and table creation basics.

Shows how to set up Amazon Athena with an S3 bucket for query results, create a data catalog database, define a CloudFront logs table, and run browser-based queries.

Amazon Data Firehose delivers real-time streaming data to destinations such as S3, Redshift, OpenSearch, Splunk, and more, with optional Lambda-based transformations and configurable buffering.

Take a hands-on demo of Amazon EMR: set up S3 input and output, launch a single-node cluster, run a Spark job to sum category sales, and store parquet results.

Master AWS Glue for serverless data integration, covering discover and organize data, transform and clean data for analysis, and build and monitor data pipelines.

Explore an end-to-end aws glue hands-on demo: set up s3 buckets, crawl data into the glue data catalog, run a python spark etl job, and query results with athena.

Explore Amazon Kinesis Data Streams architecture, producers and consumers, shard and partition key concepts, and how on demand mode and provision mode control throughput, retention, and encryption.

Create an end-to-end AWS Kinesis Data Streams pipeline by generating sample sensor data with a Lambda, processing it through a second Lambda, and storing results in a DynamoDB table.

Discover how AWS Lake Formation centralizes data lake governance with granular grants, cross-account sharing, and secure access via the Glue Data Catalog integration with Athena, Redshift Spectrum, QuickSight, and EMR.

Learn to build a data lake with AWS Lake Formation by creating an S3 bucket, registering locations, cataloging using Glue crawler, configuring permissions, querying with Amazon Athena, and cleaning up resources.

Explore Amazon managed service for Apache Flink and its Studio option, enabling streaming apps with Kinesis Data Streams, Kafka sources, and Flink APIs including data stream and table APIs.

Discover OpenSearch service, a fully managed search and analytics engine built on Apache Lucene for real-time log analytics, full-text search, and observability.

Provision and configure an Amazon OpenSearch service domain and set up OpenSearch dashboards. Ingest sample Apache log data, create index patterns and visualizations, and run basic searches before cleanup.

Discover Amazon QuickSight, a cloud-based BI tool that connects diverse data sources, leverages Spice for fast analytics, and enables ML features like forecasting, anomaly detection, auto narratives, and row-level security.

learn to sign up for amazon quicksight, connect aws or on-prem data sources with spice for fast analytics, and create and publish interactive dashboards from data sets.

Explore Amazon Redshift's serverless and provisioned modes, including clusters, RPU capacity, and workgroups. Learn how serverless automatically provisions resources and scales capacity with pay-per-use pricing and S3 data lake sharing.

Join a hands-on Amazon Redshift demo that builds a cluster, configures network security, loads sample data from S3, runs queries in the Query Editor, uses the copy command.

Explore Amazon EventBridge, a serverless event bus enabling decoupled, event-driven architectures across AWS services. Learn event flow, event structure, rules and targets, pipes, scheduler, retries, dead-letter queues, and schema discovery.

Explore event sources across AWS services and SaaS partners, compare EventBridge with SNS, SQS, and Step Functions, and learn filtering, archiving, and integration capabilities.

Demonstrate wiring an EventBridge rule to an EC2 instance state change to trigger an SNS email notification when the instance stops.

Explore how Amazon Managed Workflows for Apache Airflow provides a scalable, secure hosted orchestration service to write, schedule, and monitor cloud data pipelines with a sample dag.

Learn Amazon SNS pub-sub basics, including topics, publishers, subscriptions, standard versus FIFO topics, message filtering, and dead-letter queues in fan-out to SQS.

Explore a hands-on amazon simple queue service (sqs) demo that creates standard and fifo queues, sends and receives messages, and reviews message IDs, attributes, and deduplication options.

Demonstrates a hands-on AWS Step Functions state machine coordinating three Lambda functions—check inventory, process payment, and update order status—using a simulated 90% stock and practical deployment and testing steps.

Explore AWS budgets to monitor cost and usage, including cost, usage, and utilization budgets. Set up alerts, forecasts, and automation with IAM policies, Cost Explorer integration, and budget reports.

Explore AWS Cost Explorer to visualize cost and usage, monitor daily and monthly unblended costs, forecast 12 months; enable data preparation, anomaly detection, Cost Explorer reports, and amortized costs.

Master AWS Cost Explorer by building amortized cost reports for the last three months, grouped by service, EC2 instance type, and usage type, then save to the report library.

Explore how AWS Batch orchestrates scalable batch workloads on AWS ECS and Amazon EKS, detailing job definitions, queues, compute environments, and the life cycle from submission to success or failure.

Explore a hands-on AWS Batch workflow from creating an IAM role and ECR repository to configuring a compute environment, building a Docker image, submitting a job, and cleaning up resources.

Explore Amazon EC2 fundamentals, including instances, AMIs, instance types, and purchasing options from on-demand to spot. Learn how regions, security groups, and volumes drive scalable, secure cloud computing.

Launch an EC2 instance with Amazon Linux 2023 and a free-tier t2 micro. Configure a key pair and security group, then connect via SSH or EC2 Instance Connect.

Learn AWS Lambda fundamentals, including serverless compute, function anatomy, and deployment options. Examine invocation types, concurrency, and event sources like S3, DynamoDB Streams, SQS, and API Gateway for exam readiness.

Explore a hands-on AWS Lambda demo that integrates API gateway with SNS to trigger email notifications, including creating topics, subscriptions, a Python 3.12 function, and an HTTP API trigger.

Explore how the AWS Serverless Application Repository enables publishing, discovering, and deploying serverless apps with emphasis on IAM roles, resource policies, and nested applications.

Explore Amazon Elastic Container Registry (ECR): a fully managed private registry for Docker and OCI images, with repositories, access policies, lifecycle rules, image scanning, and cross-region replication.

This hands-on demo walks you through Amazon Elastic Container Registry, creating a private repository, tagging and pushing a Docker image with AWS CLI and Cloud Shell.

Discover Amazon ECS, its capacity, controller, and provisioning layers, and how to deploy EC2 or Fargate while exploring on-premises options like ECS on AWS Outposts, Local Zones, and ECS Anywhere.

Learn to create an Amazon ecs cluster with Fargate, define a web app task using Apache, deploy a long-running service, verify deployment via the url, and perform cleanup.

Explore Amazon EKS, offering managed node groups, self-managed nodes, and Fargate for cloud, outpost, and Amazon EKS Anywhere. Grasp architecture, EBS CSI storage, VPC basics, deployment options, and monitoring.

Explore Amazon Elastic Kubernetes Service with a hands-on demo that builds an EKS cluster, node group, and Nginx deployment exposed by a load balancer, using Cloud Shell and kubectl.

Explore Amazon DocumentDB with MongoDB compatibility, featuring scalable clusters and replica sets, read replicas with reader endpoints, cluster and instance endpoints, automatic failover, encryption with KMS, and point-in-time backups.

Demonstrates Amazon DocumentDB with MongoDB compatibility by creating a cluster with read replicas, connecting from EC2, performing crud operations and queries on a product catalog, and cleaning up resources.

Discover how Amazon DynamoDB provides a fully managed, serverless, no-SQL database with key-value and document models, primary keys, partition design, indexes, and global tables.

Learn to create a DynamoDB table in the AWS console, define partition and sort keys, insert and read items, and build a global secondary index.

Explore Amazon ElastiCache to deploy serverless caching or custom clusters, compare Valki, memcached, and Redis engines, and learn use cases like database query caching, session state, and real-time analytics.

Explore Amazon Neptune, a high-performance graph database for billions of relationships with millisecond queries, offering open graph api support for property graphs and rdf sparql.

Conduct a hands-on Amazon Neptune demo by creating a Neptune cluster and an EC2 client. Load a social network graph, run friend-recommendation queries and interactive queries, and clean up resources.

Launch an EC2 instance to connect to an RDS PostgreSQL database, configure SSH and security groups, provision a free-tier database, connect, create a database and table, then delete resources.

Explore the AWS Cloud Development Kit (CDK), a framework to define infrastructure with familiar languages using constructs and stacks, integrating with CloudFormation through a construct library and CLI.

Explore how AWS CodeArtifact securely stores and shares private packages, organizes assets into domains, repositories, and packages, and integrates with npm, pip, Maven, and CodeBuild.

Demonstrates an end-to-end AWS CodeArtifact workflow: create domain and repositories, publish a node package, configure npm registry, test with a consumer app, and clean up.

AWS CodeBuild is a fully managed, scalable CI service that compiles code, runs tests, and produces deployment artifacts using a buildspec.yml. Plan builds with build projects and environments.

Explore a hands-on AWS CodeBuild demo that creates an ECR repository, links GitHub, builds and tests a Docker image, and cleans up resources to demonstrate end-to-end CI/CD.

Explore how AWS CodeDeploy automates deployments across EC2, on-premises, Lambda, and ECS, with in-place and blue/green strategies using canary, linear, or all-at-once traffic shifts.

Build and deploy a sample web app using AWS CodeDeploy with IAM roles, EC2 instances, and S3 revisions. Test, monitor deployment status, and clean up resources.

Explore AWS CodePipeline as an end-to-end continuous delivery solution, detailing pipelines, stages, actions, artifacts, triggers, and executions to automate build, test, and deployment.

Perform an end-to-end hands-on demo of AWS CodePipeline, creating an S3 bucket with static website hosting, connecting GitHub via code connections, building a pipeline, and validating continuous deployment.

Explore how AWS X-Ray instruments applications, tracks requests with traces, segments, and subsegments, and visualizes service graphs and maps to diagnose performance in microservices.

Explore end-to-end tracing with AWS X-Ray by wiring IAM roles, two Lambda functions, API gateway, and DynamoDB, then generate traffic, visualize with the service map, and analyze traces.

Explore qualitative and quantitative data types, including nominal, ordinal, binary, discrete, and continuous data, with interval and ratio subtypes and examples.

Explore structured, unstructured, and semi-structured data, and see how format, schema, and examples like SQL tabular data, natural language text, images, videos, audio, and XML/JSON/YAML influence search and analysis.

Explore exploratory data analysis (EDA) to summarize data with visual methods like scatter plots, box plots, and histograms, revealing patterns and outliers, and guiding data-driven modeling.

learn data analysis and visualization techniques, including histograms, scatter plots, and heat maps, using seaborn and matplotlib to explore distributions, correlations, and feature relationships in a housing dataset.

Explore data science, artificial intelligence, machine learning, and deep learning, highlighting their relationships and key learning types—supervised, unsupervised, and reinforcement—alongside neural networks.

Explore the three main types of machine learning: supervised, unsupervised, and reinforcement learning, with core techniques like classification, regression, clustering, and dimensionality reduction.

Explore supervised learning, a machine learning branch using labeled data to train models for prediction, covering regression and classification, and choosing suitable algorithms based on data and labels.

Explore linear regression and classification, predicting continuous tip amounts from total bill and classifying outcomes, while noting mean squared error, cross entropy, discretizing, and embedding features.

Explore logistic regression and regularization to predict binary outcomes with sigmoid probabilities, optimize thresholds, and prevent overfitting through early stopping and l1/l2 regularization methods.

Explore the differences between machine learning and deep learning, and learn how automated machine learning handles data wrangling, exploratory data analysis, and deployment from problem to production.

Cover five popular algorithms—linear regression, logistic regression, decision trees, random forests, and support vector machines—and learn how they handle continuous prediction, binary classification, tree-based ensembling, and kernel methods.

Discover how k-means initializes k centroids, assigns data points to the nearest centroid by euclidean distance, updates centroids by the mean, and iterates until convergence.