AWS SageMaker Machine Learning Engineer in 30 Days + ChatGPT

Build 30+ ML Projects in 30 Days in AWS, Master SageMaker JumpStart, Canvas, AutoPilot, DataWrangler, Lambda & S3

4.40 (1077 reviews)

Udemy

platform

English

language

Data Science

What you will learn

Build, Train, Test and Deploy Machine Learning Models in AWS

Leverage ChatGPT and GPT-4 to Automate Coding Tasks, Perform Code Debugging, Write Documentation and Add New Features to your Code

Define and Perform Image and Text Labeling Jobs Using AWS SageMaker GroundTruth

Prepare, Clean and Visualize data Using AWS SageMaker Data Wrangler without Writing any Code

Optimize ML model hyperparameters using GridSearch, Bayesian & Random Search Optimization Techniques

Master Key AWS services such as Simple Storage Service (S3), Elastic Compute Cloud (EC2), Identity and Access Management (IAM) and CloudWatch

Understand Machine Learning workflow automation using AWS Lambda, Step functions and SageMaker Pipelines.

Learn how to define a lambda function in AWS management console, understand the anatomy of Lambda functions, and how to configure a test event in Lambda

Train a Machine Learning Regression and Classifier Models Using No-code AWS Canvas

Learn how to leverage Amazon SageMaker Autopilot and SageMaker Canvas to train multiple models without writing any code.

Perform Exploratory Data Analysis and Visualization Using Pandas, Searborn and Matplotlib Libraries

Understand Regression Models KPIs Such as RMSE, MSE, MAE, R2 and Adjusted R2

Understand Classification Models KPIs such as Accuracy, Precision, Recall, F1-Score, ROC, and AUC

Define a Machine Learning Training Job Using AWS SageMaker JumpStart

Deploy an Endpoint Using Amazon SageMaker, Perform Inference and Generate Predictions

Define a Lambda function using Boto3 SDK and Test the lambda function using Eventbridge (cloudwatch events)

Understand the difference between synchronous and asynchronous Lambda Functions invocations

Perform AI/ML Models Prototyping Using AutoGluon Library

How to monitor billing dashboard, set alarms, S3/EC2 instances pricing and request service limits increase

Understand the difference between Artificial Intelligence (AI), Machine Learning (ML), Data Science (DS) and Deep Learning (DL)

Learn the fundamentals of Amazon SageMaker, SageMaker Components, training options including built-in algorithms, AWS Marketplace, & customized ML Algorithms

Leverage a Yolo V3 Object Detection Algorithm available on the AWS Marketplace

Understand the format and Use Case of Json Lines and Manifest Files

Learn auto-labeling workflow and understand the difference between SageMaker GroundTruth and GroundTruth Plus

Learn how to define a labeling job with bounding boxes (object detection), pixel-level Semantic Segmentation, and text data

Understand the difference between data labeling workforces in AWS such as public mechanical Turks, private labelers and AWS curated third-party vendors

Learn the difference between Supervised, Unsupervised and Reinforcement Machine Learning Strategies

Perform data visualization using Seaborn & Matplotlib libraries, plots include line plot, pie charts, subplots, pairplots, countplots, and correlations heatmaps

Export a data wrangler workflow into Python script, create a custom formula and apply it to a given column in the data, and generate summary tables/bias report

Learn how to train an XG-boost algorithm in SageMaker using AWS JumpStart, assess trained model performance, plot residuals, & deploy an endpoint

Understand Bias-Variance Trade-off, L1 and L2 Regularization Techniques

Train/Test several ML Classifiers such as Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Trees, and Random Forest Classifiers

Learn SageMaker Built-in Algorithms such as Linear Learner, XG-Boost, Principal Component Analysis (PCA), and K-Nearest Neighbors

Course Gallery

AWS SageMaker Machine Learning Engineer in 30 Days + ChatGPT – Screenshot 1 — Screenshot 1 – AWS SageMaker Machine Learning Engineer in 30 Days + ChatGPT

AWS SageMaker Machine Learning Engineer in 30 Days + ChatGPT – Screenshot 2 — Screenshot 2 – AWS SageMaker Machine Learning Engineer in 30 Days + ChatGPT

AWS SageMaker Machine Learning Engineer in 30 Days + ChatGPT – Screenshot 3 — Screenshot 3 – AWS SageMaker Machine Learning Engineer in 30 Days + ChatGPT

AWS SageMaker Machine Learning Engineer in 30 Days + ChatGPT – Screenshot 4 — Screenshot 4 – AWS SageMaker Machine Learning Engineer in 30 Days + ChatGPT

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Comidoc Review

Our Verdict

While the course provides valuable insights into AWS SageMaker and various machine learning concepts, potential drawbacks include confusing billing issues and repetitive content. Additionally, some students may want more depth on machine learning engineering topics.

What We Liked

The course offers a comprehensive and detailed look at AWS SageMaker, covering various aspects of the platform such as data wrangling, model training, deployment, and monitoring.
Dr. Ryan Ahmed is praised for his engaging teaching style, clear explanations of tricky concepts, and choice of meaningful example datasets.
The course balances theoretical explanations with practical implementation, using both SageMaker and scikit-learn for model training and deployment.
Includes a variety of topics in machine learning such as evaluation metrics, linear regression, and various ML algorithms like XG-Boost and K-Nearest Neighbors.

Potential Drawbacks

Some students have experienced issues with AWS billing after completing the course, including unexpected charges despite being within the free tier limits and difficulty understanding SageMaker's pricing structure.
A few reviewers find the course to be repetitive with unnecessary videos and similar content throughout.
The presentation of half the course focusing on plain old Jupyter notebooks may not add much value for students already familiar with using them locally.
There is a lack of in-depth coverage on machine learning engineering topics, such as end-to-end CI/CD pipelines and AWS Lambda application in MLOps.