Aws Certified Machine Learning Engineer Associate: Hands On!
Last updated 8/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 7.82 GB | Duration: 22h 59m

Master the MLA-C01 AWS Machine Learning Engineer Exam: SageMaker, Bedrock, and AI Skills for Certification Success!

What you'll learn

Prepare confidently for the AWS Certified Machine Learning Engineer Associate exam.

Understand and apply key AWS machine learning services like SageMaker, Bedrock, and more.

Perform data preparation, feature engineering, and data validation for ML models.

Master hyperparameter tuning, model training, and deployment strategies on AWS.

Implement CI/CD pipelines and automation for scalable machine learning workflows.

Secure, monitor, and optimize AWS ML infrastructure for performance and cost-efficiency.

Requirements

This course is ideal for individuals with at least one year of experience using Amazon SageMaker and other AWS services for machine learning. A background in data engineering, DevOps, or software development, along with a basic understanding of machine learning algorithms and cloud infrastructure, is recommended.

Description

Get certified by Amazon for your knowledge of machine learning on AWS! Prepare to ace one of the most challenging certifications in the cloud domain—the AWS Certified Machine Learning Engineer Associate Exam! Whether you're a backend developer, data engineer, or data scientist, this comprehensive course is your gateway to success.Why This Course?This course is expertly crafted by industry veterans Frank Kane and Stephane Maarek, who have collectively educated over 3 million students on Udemy. Frank Kane, with over 9 years of experience at Amazon, has specialized in machine learning and AI, and Stephane Maarek is an AWS expert and renowned instructor. Together, they bring an unparalleled depth of knowledge to guide you through every aspect of the exam.What You'll Learn:Master AWS ML Services: Dive deep into Amazon SageMaker, Amazon Bedrock, and a host of other AWS services like Comprehend, Rekognition, and Translate, which are crucial for the exam.Hands-on Labs: Gain practical experience with hands-on activities, labs, and demos that reinforce your understanding and help you build confidence.Practice Questions: 110 quiz questions throughout the course test your knowledge, in a style similar to the examData Preparation & Feature Engineering: Learn how to ingest, transform, and validate data for ML modeling, ensuring data integrity and model readiness.Model Development & Deployment: Explore hyperparameter tuning, model performance analysis, and best practices for deploying scalable ML solutions on AWS.Monitoring & Security: Discover how to monitor ML models and infrastructure, optimize costs, and secure your AWS environment, ensuring compliance and performance.Why Choose Us?Proven Track Record: Our instructors have helped millions of students achieve their AWS certification goals.Real-World Experience: Learn from experts who have worked at Amazon and have extensive experience with AWS services.Comprehensive Coverage: This course covers everything you need to pass the exam—from AWS service knowledge to advanced machine learning topics that the exam will test you on.Who Should Enroll?This course is perfect for anyone preparing to take the AWS Certified Machine Learning Engineer Associate Exam. If you're serious about your certification and want to ensure you walk into the exam center with confidence, this course is for you.Don't Leave Your Success to ChanceThis certification is tough, and the stakes are high. Don't risk hundreds of dollars on an exam until you're fully prepared. Enroll now and take the first step towards becoming an AWS Certified Machine Learning Engineer!Enroll Today and Start Your Journey to Certification Success!- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -InstructorMy name is Stéphane Maarek, I am passionate about Cloud Computing, and I will be your instructor in this course. I teach about AWS certifications, focusing on helping my students improve their professional proficiencies in AWS.I have already taught 2,500,000+ students and gotten 800,000+ reviews throughout my career in designing and delivering these certifications and courses!With AWS becoming the centerpiece of today's modern IT architectures, I have decided it is time for students to learn how to be an AWS Data Analytics Professional. So, let's kick start the course! You are in good hands!- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -InstructorHey, I'm Frank Kane, and I'm also co-instructing this course. I spent nine years working for Amazon from the inside as a senior engineer and senior manager, and I'm best known for my top-selling courses in "big data", data analytics, machine learning, AI, Apache Spark, system design, and Elasticsearch.I've been teaching on Udemy since 2015, where I've reached over 850,000 students all around the world!I've worked hard to keep this course up to date with the latest developments in AWS machine learning, and to make sure you're prepared for the latest version of this exam. Let's dive in and get you ready!- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -This course also comes with:Lifetime access to all future updatesA responsive instructor in the Q&A SectionUdemy Certificate of Completion Ready for DownloadA 30 Day "No Questions Asked" Money Back Guarantee!Join us in this course if you want to pass the AWS Certified Machine Learning Engineer Associate MLA-C01 exam and master the AWS platform!

Overview

Section 1: Introduction

Lecture 1 Introduction and Course Overview

Lecture 2 Udemy 101

Lecture 3 Get the Course Materials and Slides

Lecture 4 Setting Up an AWS Billing Alarm

Section 2: Data Ingestion and Storage

Lecture 5 Intro: Data Ingestion and Storage

Lecture 6 Types of Data

Lecture 7 Properties of Data (The Three V's)

Lecture 8 Data Warehouses, Lakes, and Lakehouses

Lecture 9 Data Mesh

Lecture 10 ETL & ETL Pipelines and Orchestration

Lecture 11 Common Data Sources and Data Formats

Lecture 12 Amazon S3

Lecture 13 Amazon S3 - Hands On

Lecture 14 Amazon S3 Security - Bucket Policy

Lecture 15 Amazon S3 Security - Bucket Policy - Hands On

Lecture 16 Amazon S3 - Versioning

Lecture 17 Amazon S3 - Versioning - Hands On

Lecture 18 Amazon S3 - Replication

Lecture 19 Amazon S3 - Replication - Notes

Lecture 20 Amazon S3 - Replication - Hands On

Lecture 21 Amazon S3 - Storage Classes

Lecture 22 Amazon S3 - Storage Classes - Hands On

Lecture 23 Amazon S3 - Lifecycle Rules

Lecture 24 Amazon S3 - Lifecycle Rules - Hands On

Lecture 25 Amazon S3 - Event Notifications

Lecture 26 Amazon S3 - Event Notifications - Hands On

Lecture 27 Amazon S3 - Performance

Lecture 28 Amazon S3 - Select & Glacier Select

Lecture 29 Amazon S3 - Encryption

Lecture 30 About DSSE-KMS

Lecture 31 Amazon S3 - Encryption - Hands On

Lecture 32 Amazon S3 - Default Encryption

Lecture 33 Amazon S3 - Access Points

Lecture 34 Amazon S3 - Object Lambda

Lecture 35 Amazon EBS

Lecture 36 Amazon EBS - Hands On

Lecture 37 Amazon EBS Elastic Volumes

Lecture 38 Amazon EFS

Lecture 39 Amazon EFS - Hands On

Lecture 40 Amazon EFS vs. Amazon EBS

Lecture 41 Amazon FSx

Lecture 42 Amazon FSx - Hands On

Lecture 43 Amazon Kinesis Data Streams

Lecture 44 Amazon Kinesis Data Streams - Producers

Lecture 45 Amazon Kinesis Data Streams - Consumers

Lecture 46 Amazon Kinesis Data Streams - Hands On

Lecture 47 Amazon Kinesis Data Streams - Enhanced Fan Out

Lecture 48 Amazon Kinesis Data Streams - Scaling

Lecture 49 Amazon Kinesis Data Streams - Handling Duplicates

Lecture 50 Amazon Kinesis Data Streams - Security

Lecture 51 Amazon Kinesis Data Firehose

Lecture 52 Kinesis Tuning and Troubleshooting

Lecture 53 Amazon Managed Service for Apache Flink

Lecture 54 Kinesis Analytics Costs; RANDOM_CUT_FOREST

Lecture 55 Amazon MSK

Lecture 56 Amazon MSK - Connect

Lecture 57 Amazon MSK - Serverless

Lecture 58 Amazon Kinesis vs. Amazon MSK

Section 3: Data Transformation, Integrity, and Feature Engineering

Lecture 59 Intro: Data Transformation, Integrity, and Feature Engineering

Lecture 60 Elastic MapReduce (EMR) and Hadoop Overview

Lecture 61 Apache Spark on EMR

Lecture 62 Feature Engineering and the Curse of Dimensionality

Lecture 63 Lab: Preparing Data for TF-IDF with Spark and EMR Studio, Part 1

Lecture 64 Lab: Preparing Data for TF-IDF with Spark and EMR Studio, Part 2

Lecture 65 Imputing Missing Data

Lecture 66 Dealing with Unbalanced Data

Lecture 67 Handling Outliers

Lecture 68 Binning, Transforming, Encoding, Scaling, and Shuffling

Lecture 69 SageMaker Overview

Lecture 70 Data Processing, Training, and Deployment with SageMaker

Lecture 71 Amazon SageMaker Ground Truth and Label Generation

Lecture 72 Amazon Mechanical Turk

Lecture 73 SageMaker Data Wrangler

Lecture 74 Demo: SageMaker Studio, Canvas, and Data Wrangler

Lecture 75 SageMaker Model Monitor and SageMaker Clarify

Lecture 76 Partial Dependence Plots (PDPs), Shapley values, and SHAP

Lecture 77 SageMaker Feature Store

Lecture 78 AWS Glue

Lecture 79 AWS Glue Studio

Lecture 80 AWS Glue Data Quality

Lecture 81 AWS Glue DataBrew

Lecture 82 Demo: Glue DataBrew

Lecture 83 Handling PII in DataBrew Transformations

Section 4: AWS Managed AI Services

Lecture 84 Intro: AWS Managed AI Services

Lecture 85 Why AWS Managed Services?

Lecture 86 Amazon Comprehend

Lecture 87 Amazon Comprehend - Hands On

Lecture 88 Amazon Translate

Lecture 89 Amazon Translate - Hands On

Lecture 90 Amazon Transcribe

Lecture 91 Amazon Polly

Lecture 92 Amazon Polly - Hands On

Lecture 93 Amazon Rekognition

Lecture 94 Amazon Forecast

Lecture 95 Amazon Lex

Lecture 96 Amazon Lex - Hands On

Lecture 97 Amazon Personalize

Lecture 98 Amazon Textract

Lecture 99 Amazon Textract - Hands On

Lecture 100 Amazon Kendra

Lecture 101 Amazon Augmented AI

Lecture 102 Amazon Augmented AI - Hands On

Lecture 103 Amazon's Hardware for AI

Lecture 104 Amazon's Hardware for AI - Hands On

Lecture 105 Amazon Lookout

Lecture 106 Amazon Fraud Detector

Lecture 107 Amazon Q Business

Lecture 108 Amazon Q Business - Hands On

Lecture 109 Amazon Q Apps

Lecture 110 Amazon Q Apps - Hands On

Lecture 111 Amazon Q Business - Hands On - Cleanup

Lecture 112 Amazon Q Developer

Lecture 113 Amazon Q Developer - Hands On

Section 5: SageMaker Built-In Algorithms

Lecture 114 Intro: SageMaker Built-In Algorithms

Lecture 115 Introducing Amazon SageMaker

Lecture 116 SageMaker Input Modes

Lecture 117 Linear Learner in SageMaker

Lecture 118 XGBoost in SageMaker

Lecture 119 Seq2Seq in SageMaker

Lecture 120 DeepAR in SageMaker

Lecture 121 BlazingText in SageMaker

Lecture 122 Object2Vec in SageMaker

Lecture 123 Object Detection in SageMaker

Lecture 124 Image Classification in SageMaker

Lecture 125 Semantic Segmentation in SageMaker

Lecture 126 Random Cut Forest in SageMaker

Lecture 127 Neural Topic Model in SageMaker

Lecture 128 Latent Dirichlet Allocation (LDA) in SageMaker

Lecture 129 K-Nearest-Neighbors (KNN) in SageMaker

Lecture 130 K-Means Clustering in SageMaker

Lecture 131 Principal Component Analysis (PCA) in SageMaker

Lecture 132 Factorization Machines in SageMaker

Lecture 133 IP Insights in SageMaker

Section 6: Model Training, Tuning, and Evaluation

Lecture 134 Intro: Model Training, Tuning, and Evaluation

Lecture 135 Introduction to Deep Learning

Lecture 136 Activation Functions

Lecture 137 Convolutional Neural Networks

Lecture 138 Recurrent Neural Networks

Lecture 139 Tuning Neural Networks

Lecture 140 Regularization Techniques for Neural Networks (Dropout, Early Stopping)

Lecture 141 L1 and L2 Regularization

Lecture 142 The Vanishing Gradient Problem

Lecture 143 The Confusion Matrix

Lecture 144 Precision, Recall, F1, AUC, and more

Lecture 145 Ensemble Methods: Bagging and Boosting

Lecture 146 Automatic Model Tuning (AMT) in SageMaker

Lecture 147 Hyperparameter Tuning in AMT

Lecture 148 SageMaker Autopilot / AutoML

Lecture 149 SageMaker Studio, SageMaker Experiments

Lecture 150 SageMaker Debugger

Lecture 151 SageMaker Model Registry

Lecture 152 Analyzing Training Jobs with TensorBoard

Lecture 153 SageMaker Training at Large Scale: Training Compiler, Warm Pools

Lecture 154 SageMaker Checkpointing, Cluster Health Checks, Automatic Restarts

Lecture 155 SageMaker Distributed Training Libraries and Distributed Data Parallelism

Lecture 156 SageMaker Model Parallelism Library

Lecture 157 Elastic Fabric Adapter (EFA) and MiCS

Section 7: Generative AI Model Fundamentals

Lecture 158 Intro: Generative AI Model Fundamentals

Lecture 159 The Transformer Architecture

Lecture 160 Self-Attention and Attention-Based Neural Networks

Lecture 161 Applications of Transformers

Lecture 162 Generative Pre-Trained Transformers: How they Work, Part 1

Lecture 163 Generative Pre-Trained Transformers: How they Work, Part 2

Lecture 164 Fine-Tuning and Transfer Learning with Transformers

Lecture 165 Lab: Tokenization and Positional Encoding with SageMaker Notebooks

Lecture 166 Lab: Multi-Headed, Masked Self-Attention in SageMaker

Lecture 167 Lab: Using GPT within a SageMaker Notebook

Lecture 168 AWS Foundation Models and SageMaker JumpStart with Generative AI

Lecture 169 Lab: Using Amazon SageMaker JumpStart with Huggingface

Section 8: Building Generative AI Applications with Bedrock

Lecture 170 Intro: Building Generative AI Applications with Bedrock

Lecture 171 Building Generative AI with Amazon Bedrock and Foundation Models

Lecture 172 Lab: Chat, Text, and Image Foundation Models in the Bedrock Playground

Lecture 173 Fine-Tuning Custom Models and Continuous Pre-Training with Bedrock

Lecture 174 Retrieval-Augmented Generation (RAG) Fundamentals with Bedrock

Lecture 175 Vector Stores and Embeddings with Amazon Bedrock Knowledge Bases

Lecture 176 Implementing RAG with Amazon Bedrock Knowledge Bases

Lecture 177 Lab: Building and Querying a RAG System with Amazon Bedrock Knowledge Bases

Lecture 178 Content Filtering with Amazon Bedrock Guardrails

Lecture 179 Lab: Building and Testing Guardrails with Amazon Bedrock

Lecture 180 Building LLM Agents / Agentic AI with Amazon Bedrock Agents

Lecture 181 Lab: Build a Bedrock Agent with Action Groups, Knowledge Bases, and Guardrails

Lecture 182 Other Amazon Bedrock Features (Model Evaluation, Bedrock Studio, Watermarks)

Section 9: Machine Learning Operations (MLOps) with AWS

Lecture 183 Intro: MLOps

Lecture 184 Deployment Guardrails and Shadow Tests

Lecture 185 SageMaker's Inner Details and Production Variants

Lecture 186 SageMaker On the Edge: SageMaker Neo and IoT Greengrass

Lecture 187 SageMaker Resource Management: Instance Types and Spot Training

Lecture 188 SageMaker Resource Management: Automatic Scaling

Lecture 189 SageMaker: Deploying Models for Inference

Lecture 190 SageMaker Serverless Inference and Inference Recommender

Lecture 191 SageMaker Inference Pipelines

Lecture 192 SageMaker Model Monitor

Lecture 193 Model Monitor Data Capture

Lecture 194 MLOps with SageMaker, Kubernetes, SageMaker Projects, and SageMaker Pipelines

Lecture 195 What is Docker?

Lecture 196 Amazon ECS

Lecture 197 Amazon ECS - Create Cluster - Hands On

Lecture 198 Amazon ECS - Create Service - Hands On

Lecture 199 Amazon ECR

Lecture 200 Amazon EKS

Lecture 201 Amazon EKS - Hands On

Lecture 202 AWS CloudFormation

Lecture 203 AWS CloudFormation - Hands On

Lecture 204 AWS CDK

Lecture 205 AWS CDK - Hands On

Lecture 206 AWS CodeDeploy

Lecture 207 AWS CodeBuild

Lecture 208 AWS CodePipeline

Lecture 209 Git Review: Architecture and Commands

Lecture 210 Gitflow, GitHub Flow

Lecture 211 Amazon EventBridge

Lecture 212 Amazon EventBridge - Hands On

Lecture 213 AWS Step Functions

Lecture 214 AWS Step Functions: State Machines and States

Lecture 215 Amazon Managed Workflows for Apache Airflow (MWAA)

Section 10: Security, Identity, and Compliance

Lecture 216 Intro: Security, Identity, and Compliance

Lecture 217 Principle of Least Privilege

Lecture 218 Data Masking and Anonymization

Lecture 219 SageMaker Security: Encryption at Rest and in Transit

Lecture 220 SageMaker Security: VPC's, IAM, Logging and Monitoring

Lecture 221 IAM Introduction: Users, Groups, Policies

Lecture 222 IAM Users & Groups - Hands On

Lecture 223 IAM Policies

Lecture 224 IAM Policies - Hands On

Lecture 225 IAM MFA

Lecture 226 IAM MFA - Hands On

Lecture 227 IAM Roles

Lecture 228 IAM Roles - Hands On

Lecture 229 Encryption 101

Lecture 230 AWS KMS

Lecture 231 AWS KMS - Hands On

Lecture 232 Amazon Macie

Lecture 233 AWS Secrets Manager

Lecture 234 AWS Secrets Manager - Hands On

Lecture 235 AWS WAF

Lecture 236 AWS Shield

Lecture 237 VPC, Subnets, Internet Gateway, NAT Gateway

Lecture 238 NACL, Security Groups, VPC Flow Logs

Lecture 239 VPC Peering, Endpoints, VPN, Direct Connect

Lecture 240 VPC Cheat Sheet & Closing Comments

Lecture 241 AWS PrivateLink

Section 11: Management and Governance

Lecture 242 Intro: Management and Governance

Lecture 243 Amazon CloudWatch - Metrics

Lecture 244 Amazon CloudWatch - Logs

Lecture 245 Amazon CloudWatch - Logs - Hands On

Lecture 246 Amazon CloudWatch - Logs Unified Agent

Lecture 247 Amazon CloudWatch - Alarms

Lecture 248 Amazon CloudWatch - Alarms - Hands On

Lecture 249 AWS X-Ray

Lecture 250 AWS X-Ray - Hands On

Lecture 251 Overview of Amazon Quicksight

Lecture 252 Types of Visualizations, and When to Use Them

Lecture 253 Amazon CloudTrail

Lecture 254 Amazon CloudTrail - Hands On

Lecture 255 AWS Config

Lecture 256 AWS Config - Hands On

Lecture 257 CloudWatch vs. CloudTrail vs. Config

Lecture 258 AWS Budgets

Lecture 259 AWS Budgets - Hands On

Lecture 260 AWS Cost Explorer

Lecture 261 AWS Trusted Advisor

Section 12: Machine Learning Best Practices

Lecture 262 Intro: Machine Learning Best Practices

Lecture 263 Designing ML Systems with AWS: Responsible AI

Lecture 264 ML Design Principles and Lifecycle

Lecture 265 ML Business Goal Identification

Lecture 266 Framing the ML Problem

Lecture 267 Data Processing

Lecture 268 Model Development

Lecture 269 Deployment

Lecture 270 Monitoring

Lecture 271 AWS Well-Architected Machine Learning Lens

Section 13: Wrapping Up

Lecture 272 Intro: Wrapping Up

Lecture 273 Walkthrough of the Exam Guide

Lecture 274 Additional Training Resources

Lecture 275 Overview of the New Question Types (Ordering, Matching, Case Study)

Lecture 276 What to Expect

Lecture 277 Exam Walkthrough and Signup

Lecture 278 Save 50% on your AWS Exam Cost!

Lecture 279 Get an Extra 30 Minutes on your AWS Exam - Non Native English Speakers Only

Lecture 280 AWS Certification Paths

Lecture 281 Bonus Lecture

Data engineers, data scientists, DevOps professionals, and software developers who are looking to advance their careers by obtaining the AWS Certified Machine Learning Engineer Associate certification,IT professionals who have experience working with AWS services and want to deepen their understanding of machine learning solutions on the AWS platform.