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Aws Certified Machine Learning Engineer - Associate Mla-C01
Published 9/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 9.75 GB | Duration: 25h 51m

The ONLY course you need to PASS the AWS Certified Machine Learning Engineer Exam | MLA-C01 | Incl. FULL Practice Exam!

What you'll learn

PASS the AWS Certified Machine Learning Engineer Associate Exam (MLA-C01)

Full Practice Exam incl. Full Explanations to ACE the exam

All Slides available as downloadable PDFs

All Topics Covered & 100% up-to-date

Hands-on Demos with Real-World Scenarios

Start your Machine Learning Career

Build, Train & Deploy Machine Learning Models in Amazon SageMaker

Data Ingestion and Preprocessing with SageMaker Data Wrangler

Full Machine Learning Pipelines with SageMaker & Much More

Master the Full Machine Learning Lifecycle with Real-World Skills

Requirements

No previous experience with AWS or Machine Learning is needed!

Description

The ONLY course you need to prepare and PASS the AWS Certified Machine Learning Engineer - Associate exam (MLA-C01) and become an AWS Certified Machine Learning Engineer - Associate!Make your exam preparation and learning Machine Learning on AWS fun, easy, and highly effective with real-life hands-on projects, quizzes, and a full practice exam!This course teaches you every single topic you need to master the exam with ease.Why is this the ONLY course you need to pass the AWS Certified Machine Learning Engineer exam?Every single topic is covered in depth100% up-to-date!Hands-On & PracticalFull practice exam including all explanationsPractical & Real-World SkillsTips for successThis course guides you step-by-step to prepare in the best possible way for the examDon't waste your time but focuses on what really matters to master the exam!This course guides you step-by-step to prepare in the best possible way for the exam and start your successful career in machine learning!Your InstructorHi, my name is Nikolai, and I am AWS Certified and I teach AWS and data analytics in over 200 countries. My mission with this course is to take the stress out of your exam prep, make it fun but very effective to maximize your preparation time. I want to make sure you have the best chances of succeeding and moving your career forward with the AWS Machine Learning Engineer certification in your professional journey.Enroll Now and Get:Lifetime Access including all future updatesHigh quality video contentAll slides & project files as downloadable resourcesFull practice exam with explanationsTipps for success & expert-level support30-day money-back guarantee (no-questions-asked!)Become an Expert & Learn the Full Machine Learning Lifecycle in AWS:PASS the AWS Certified Machine Learning Engineer examMaster Machine Learning on AWS and become an expertBuild, train, and deploy machine learning models with SageMakerOrchestrate ML workflows with SageMaker PipelinesPerform data ingestion and transformation with SageMaker Data Wrangler and AWS GlueUse SageMaker Feature Store for feature engineeringDeploy models using real-time, batch, and serverless inferenceMonitor models in production with SageMaker Model MonitorDebug and optimize models with SageMaker Debugger and ProfilerImplement responsible AI practices with SageMaker ClarifyUnderstand AWS storage and data processing services relevant to MLSecure your ML workflows with IAM, KMS, and VPCsImplement CI/CD pipelines for ML using SageMaker and AWS CodePipelineOptimize costs and monitor ML workloads with CloudWatch and AWS Cost Management toolsAnd much more!Whether you're new to machine learning or looking to expand your AWS expertise, this course offers everything you need-practical labs, a full practice exam, and up-to-date content that covers every aspect of Machine Learning on AWS.Take this chance today - this can be your first step into a successful machine learning engineering career!Looking forward to seeing you inside the course!

Overview

Section 1: Introduction

Lecture 1 Welcome!

Lecture 2 About the exam & this course

Lecture 3 Important tips for this course

Lecture 4 All Slides

Section 2: SageMaker: Basics & Setup

Lecture 5 AWS Free Tier Account ML

Lecture 6 SageMaker Overview

Lecture 7 SageMaker Notebooks

Lecture 8 Setting Up SageMaker Notebook Instance

Lecture 9 Basic Operations in SageMaker Notebook Instance

Lecture 10 SageMaker Studio Setting Up Domain & Users

Lecture 11 SageMaker Studio Overview

Lecture 12 AWS Budgets Machine Learning

Section 3: SageMaker: Data Ingestion & Feature Engineering

Lecture 13 Data Preparation with Data Wrangler

Lecture 14 Import data using Data Wrangler

Lecture 15 Data Wrangler - Get Insights

Lecture 16 Data Wrangler Transform Data

Lecture 17 Export Data in Data Wrangler

Lecture 18 Stop Running Instances

Lecture 19 Understanding Feature Engineering

Lecture 20 SageMaker Feature Store

Lecture 21 Feature Store - Creating Features & Feature Group

Lecture 22 SageMaker Notebooks- Setting up Features

Lecture 23 SageMaker Ground Truth

Lecture 24 Create Labeling Jobs in Groud Truth

Lecture 25 Setting up Groud Truth Workforce

Lecture 26 Ground Truth Plus

Section 4: SageMaker: Training & Hyperparameter Tuning

Lecture 27 Training with Built-in Algorithms

Lecture 28 SageMaker JumpStart

Lecture 29 Deploy a Model Using JumpStart

Lecture 30 Training Models - Potential Paths

Lecture 31 Prepare The Training Of The Model

Lecture 32 Train Model

Lecture 33 Reviewing the Trained Model

Lecture 34 Model Tuning & Hyperparameters

Lecture 35 Hyperparamter Optimization Techniques

Lecture 36 Hyperparameter Tuning in Notebooks

Lecture 37 Hyperparameter Tuning in the UI

Lecture 38 SageMaker Canvas

Lecture 39 SageMaker Canvas Using AutoML

Lecture 40 SageMaker Canvas Predict & Deploy

Lecture 41 Custom Training Script

Lecture 42 Custom Docker Containers

Lecture 43 Distributed Training

Section 5: SageMaker: Experiment Tracking & Debugging

Lecture 44 SageMaker Experiments

Lecture 45 MLflow Setting Up Tracking Server

Lecture 46 MLflow Setup Experiment

Lecture 47 MLflow Track & Record Experiments

Lecture 48 SageMaker Neo

Section 6: SageMaker: Clarify & Responsible AI

Lecture 49 Challenges of Responsible Al

Lecture 50 Strategies Against Bias & Variance

Lecture 51 SageMaker Clarify

Lecture 52 SageMaker Clarify Pre-Training Analysis

Lecture 53 SageMaker Clarify Review Pre-Training Analysis

Lecture 54 SageMaker Clarify Model Bias Analysis

Lecture 55 SageMaker Clarify Explainability Report

Section 7: SageMaker: Debugging & Deployment

Lecture 56 SageMaker Debugger

Lecture 57 SageMaker Debugger (Hands-on)

Lecture 58 Model Deployment Strategies in SageMaker

Lecture 59 Deploy Real-Time Inference Endpoint

Lecture 60 Deploying Endpoint using Model Artifact

Lecture 61 Serverless Inference Endpoint

Lecture 62 Deploy Using Batch Transform

Lecture 63 Deploy as Asynchronous Inference Endpoint

Lecture 64 Multi-Model & Multi-Container Endpoints in SageMaker

Lecture 65 Deploying a Multi-Model Endpoint

Section 8: SageMaker: Monitoring Models

Lecture 66 Monitoring Models

Lecture 67 SageMaker Model Monitor

Lecture 68 Monitoring Data Quality in SageMaker

Lecture 69 Monitor Model Quality with SageMaker

Lecture 70 Model Monitoring Create a Baseline

Lecture 71 SageMaker Monitor Create a Schedule

Section 9: SageMaker: Pipelines & Model Registry

Lecture 72 SageMaker Pipelines

Lecture 73 SageMaker Pipelines (Hands-on)

Lecture 74 Model Registry

Lecture 75 SageMaker Model Registry

Section 10: Machine Learning Concepts

Lecture 76 Understanding Machine Learning Models

Lecture 77 Supervised Learning

Lecture 78 Unsupervised Learning

Lecture 79 Text Analysis Algorithms

Lecture 80 Image Classification

Lecture 81 Reinforcement Learning

Lecture 82 Reinforcement Learning with SageMaker

Lecture 83 Model Evaluation Concepts

Lecture 84 Performance Evaluation Metrics

Lecture 85 Machine Learning Development Lifecycle

Lecture 86 MLOps

Section 11: AWS Machine Learning Services

Lecture 87 What is Amazon Bedrock?

Lecture 88 Amazon Bedrock - Architecture

Lecture 89 Amazon Bedrock - Use Cases

Lecture 90 Hands-on: Exploring Amazon Bedrock

Lecture 91 Hands-on: Installing Visual Studio Code

Lecture 92 Hands-on: Setting up Visual Studio Code

Lecture 93 Hands-on: Invoking Amazon Titan Model

Lecture 94 Hands-on: Image Generation in Bedrock

Lecture 95 Amazon Personalize

Lecture 96 Hands-on: Dataset Group (Amazon Personalize)

Lecture 97 Hands-on: Training Dataset (Amazon Personalize)

Lecture 98 Hands-on: Train Model (Amazon Personalize)

Lecture 99 Hands-on: Make Predictions (Amazon Personalize)

Lecture 100 Amazon Fraud Detector

Lecture 101 Setup & Event Type (Amazon Fraud Detector)

Lecture 102 Build & Train Model (Amazon Fraud Detector)

Lecture 103 Evaluate our Model (Amazon Fraud Detector)

Lecture 104 Create Detector & Make Predictions (Amazon Fraud Detector)

Lecture 105 Cleaning up Resources (Amazon Fraud Detector)

Lecture 106 Amazon Augmented AI

Lecture 107 Amazon Comprehend

Lecture 108 Hands-on: Amazon Comprehend

Lecture 109 Amazon Comprehend Medical Hands on

Lecture 110 Amazon Rekognition

Lecture 111 Hands-on: Amazon Rekognition

Lecture 112 Hands-on: Using Rekognition in Lambda Function

Lecture 113 Amazon Textract

Lecture 114 Hands-on: Amazon Textract

Lecture 115 Amazon Kendra

Lecture 116 Hands-on: Create an Index & Sync (Amazon Kendra)

Lecture 117 Hands-on: Create Experience (Amazon Kendra)

Section 12: Data Ingestion

Lecture 118 AWS S3 - Basics

Lecture 119 Create a Bucket in S3 (Hands-on)

Lecture 120 Uploading files to S3 (Hands-on)

Lecture 121 Streaming vs Batch Ingestion

Lecture 122 AWS Glue

Lecture 123 Setting Up Crawlers (Hands-on)

Section 13: Querying with Athena

Lecture 124 AWS Athena - Overview

Lecture 125 Query data using Athena (Hands-on)

Lecture 126 Federated Queries

Lecture 127 Performance & Cost

Lecture 128 Workgroups

Lecture 129 Workgroups (Hands-on)

Section 14: AWS Data Processing Services

Lecture 130 Glue Costs

Lecture 131 Run Glue ETL Jobs (Hands-on)

Lecture 132 Scheduling Crawlers & ETL Jobs (Hands-on)

Lecture 133 Stateful vs Stateless

Lecture 134 Stateless Data Ingestion in Glue (Hands-on)

Lecture 135 Stateful Ingestion with Bookmarks (Hands-on)

Lecture 136 Glue Transformations (ETL)

Lecture 137 Glue Data Quality (Hands-on)

Lecture 138 Glue Workflows

Lecture 139 Glue Workflows - (Hands-on)

Lecture 140 Glue Job Types

Lecture 141 Glue Job Types (Hands-on)

Lecture 142 Partitioning

Lecture 143 AWS Glue DataBrew

Lecture 144 AWS Glue DataBrew - Transformations

Lecture 145 AWS Glue DataBrew (Hands-On)

Lecture 146 AWS Lambda

Lecture 147 Event-Driven Ingestion with AWS Lambda (Hands-on)

Lecture 148 Lambda Layers

Lecture 149 Replayability

Lecture 150 Amazon Kinesis for Streaming Data

Lecture 151 Amazon Kinesis Data Streams

Lecture 152 Throughput and Latency

Lecture 153 Creating a Data Stream (Hands-on)

Lecture 154 Enhanced Fan-Out for Kinesis Consumers

Lecture 155 Pull and Consume Data From Stream (Hands-on)

Lecture 156 Calling a Lambda Function From Amazon Kinesis (Hands-on)

Lecture 157 Common Issues & Troubleshooting

Lecture 158 Kinesis Firehose

Lecture 159 Creating Data Firehose Stream (Hands-on)

Lecture 160 Data Firehose - Transformations with Lambda (Hands-on)

Lecture 161 Amazon Managed Service for Apache Flink

Lecture 162 Amazon MSK

Lecture 163 MSK Connect & MSK Serverless

Lecture 164 Amazon EMR

Lecture 165 AWS EMR Cluster Types & Storage

Lecture 166 AWS EMR Storage & Scaling

Lecture 167 AWS EMR Deployment Options

Section 15: AWS Storage Solutions

Lecture 168 Importance of Partitioning

Lecture 169 Partitioning with Glue (Hands-on)

Lecture 170 Lifecycle Management & Storage Classes

Lecture 171 Using Lifecycle Rules

Lecture 172 Storage Classes (Hands-on)

Lecture 173 Intelligent Tiering (Hands-on)

Lecture 174 Lifecycle Rules (Hands-on)

Lecture 175 Versioning in S3

Lecture 176 Versioning (Hands-on)

Lecture 177 Replication

Lecture 178 Replication (Hands-on)

Lecture 179 Security in S3

Lecture 180 Security (Hands-on)

Lecture 181 Bucket Policies

Lecture 182 Access Points in S3

Lecture 183 Object Lambda

Lecture 184 S3 Event Notifications

Lecture 185 S3 Event Notifications (Hands-on)

Lecture 186 S3 Select & Glacier Select

Lecture 187 S3 Select (Hands-on)

Lecture 188 Data Mesh

Lecture 189 Data Exchange

Lecture 190 Amazon Elastic Block Store (EBS)

Lecture 191 EBS Provisioning

Lecture 192 EBS Volumes (Hands-on)

Lecture 193 Amazon Elastic File System (EFS)

Section 16: AWS Security and Compliance

Lecture 194 IAM Overview

Lecture 195 IAM Users, Groups & Role

Lecture 196 IAM Policies

Lecture 197 IAM Create User (Hands-on)

Lecture 198 IAM Policies (Hands-on)

Lecture 199 IAM Create Groups & Roles (Hands-on)

Lecture 200 AWS KMS Overview

Lecture 201 AWS KMS Key Management & Pricing

Lecture 202 AWS KMS Cross-Region & Cross-Account

Lecture 203 AWS Macie

Lecture 204 AWS Secrets

Lecture 205 AWS Secrets (Hands-on)

Lecture 206 AWS Shield

Lecture 207 Virtual Private Cloud & Subnets

Lecture 208 Gateways

Lecture 209 VPN & VPC Peering

Lecture 210 Security Groups & NACLs

Lecture 211 Additional VPC features

Lecture 212 AWS CloudTrail

Lecture 213 AWS CloudTrail Lake

Lecture 214 AWS Config

Lecture 215 AWS Config (Hands-on)

Lecture 216 AWS Well-Architected Framework

Lecture 217 AWS Well-Architected Tool

Section 17: AWS Deployment and Orchestration Services

Lecture 218 AWS CloudFormation

Lecture 219 AWS CloudFormation (Hands-on)

Lecture 220 Docker Containers

Lecture 221 Amazon ECS

Lecture 222 Amazon ECS - Launch Types

Lecture 223 Amazon ECS - IAM Roles

Lecture 224 Amazon ECR

Lecture 225 Amazon EKS

Section 18: AWS Monitoring and Cost Management Tools

Lecture 226 Amazon CloudWatch Overview

Lecture 227 Amazon CloudWatch Metrics (Hands-on)

Lecture 228 Amazon CloudWatch Metrics Stream

Lecture 229 Amazon CloudWatch Alarms

Lecture 230 CloudWatch Alarms (Hands-on)

Lecture 231 Amazon CloudWatch Logs

Lecture 232 CloudWatch Logs (Hands-on)

Lecture 233 Amazon CloudWatch Log Filtering & Subscription

Lecture 234 Amazon CloudWatch Logs Agent

Section 19: AWS AI Services

Lecture 235 What is Amazon Q Business

Lecture 236 Hands-on: Create Amazon Q Business Application

Lecture 237 Hands-on: Assign Users & Test Application

Lecture 238 Hands-on: Using Global Controls

Lecture 239 Hands-on: Blocking Words

Lecture 240 Hands-on: Topic Controls

Lecture 241 Amazon Transcribe

Lecture 242 Hands-on: Amazon Transcribe

Lecture 243 Amazon Polly

Lecture 244 Hands-on: Pricing & Models (Amazon Polly)

Lecture 245 Hands-on: Text-to-Speech (Amazon Polly)

Lecture 246 Hands-on: SSML to modify speech output (Amazon Polly)

Lecture 247 Hands-on: Real-time translation (Amazon Translate)

Lecture 248 Hands-on: Batch job translation (Amazon Translate)

Section 20: Practice Exam, Exam Tips & Scheduling

Lecture 249 Exam Signup & Get 30min more time!

Lecture 250 Final Exam Tips

Aspiring Machine Learning Engineers looking to get certified and kickstart their ML careers,Data Scientists, Data Engineers, Developers, and IT Professionals,Professionals seeking to expand their knowledge of Machine Learning on AWS

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