AWS SageMaker ML Engineer ChatGPT
 
Machine Learning is a leading tech sector with the potential to redefine the future. Comparable to the transformative impact of electricity a century ago, Machine Learning (ML) and Artificial Intelligence (AI) are poised to revolutionize various industries. ML finds wide-ranging applications in finance, banking, healthcare, transportation, and technology, offering abundant opportunities and promising career prospects.

Amazon Web Services (AWS) emerges as a globally utilized cloud computing platform, serving as a critical infrastructure for numerous companies. AWS SageMaker, a fully managed service, empowers data scientists and AI practitioners to efficiently train, test, and deploy AI/ML models.

This course boasts unique features, incorporating numerous practice sessions, quizzes, and final capstone projects. Students enrolled in this course will attain proficiency in developing production-level ML models using AWS. The curriculum is organized into eight main sections, each covering essential aspects of ML model creation and deployment.

What you'll learn:

- Build, Train, Test, and Deploy Machine Learning Models in AWS.
- Utilize ChatGPT and GPT-4 for automating coding tasks, performing code debugging, writing documentation, and adding new features to your code.
- Define and execute image and text labeling jobs using AWS SageMaker GroundTruth.
- Prepare, clean, and visualize data using AWS SageMaker Data Wrangler without writing 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.
- Train a Machine Learning Regression and Classifier model using No-code AWS Canvas.
- Leverage Amazon SageMaker Autopilot and SageMaker Canvas to train multiple models without writing code.
- Perform Exploratory Data Analysis and Visualization Using Pandas, Seaborn, 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 (cloud watch events).
- Perform AI/ML Models Prototyping Using AutoGluon Library.
- Monitor the 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.
- 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 the Seaborn and Matplotlib libraries. Plots include line plots, pie charts, subplots, pair plots, counterplots, and correlation 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 reports.
- 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.

Requirements:

- Basic Python Programming Knowledge.
- Basic knowledge of AWS.
- Basic knowledge of machine learning.