https://i124.fastpic.org/big/2024/0922/0e/a04cb89c4f6b43c6256d4c9ad6ed2b0e.jpg
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, Identit
Видео
