AWS AI practitioner
I am a developer from Mumbai with 2.5 years of experience in C,Python and shell scripting. Also worked as GreenPlum database administrator for more than a year at National Stock Exchange of India. 2x aws certified in solution architect
Lect 01 : Beta exam overview
Aug 13th 2024 - Registration opens
85 Questions , 120 min ,75$ cost
The exam includes these topics:
Week 1
Fundamental concepts and terminologies of AI, ML and generative AI
Use cases of AI, ML and generative AI
week 2
Model Training and fine tuning
Design considerations for foundation models
week3
Foundation model evaluation criteria
Prompt engineering
week4
Responsible AI
Security and compliance for AI systems
Difference between AI,ML and Gen-AI
any way computer can mimic the human intelligence
| AI | any way computer can mimic the human intelligence | |
| ML | subset of AI. or finding pattern among data | |
| Deep learning | subset of ML. or neural network - works like human brain | |
| Gen AI | subset of deep learning - used to generate stuff using LLM etc |
Traditional programming : Input + Rules (code) = Output
Machine learning : Input + Rules (algorithms) = Outputs (ML model: Prediction based on input)
USE ML when...
when you cant code it
eg: recognizing speech/images
when you cant scale it
eg: recommendations, spam, fraud detection, machine translation
when you have to adapt/personalize
eg: recommendation and personalizing. That's how amazon started as book store which can recommends
when you cant track it
eg: automated driving
ML types
Supervised learning
\=> like How human learns
\=> here we have data and a lable on it (
data with lable)
\=> Human intervention and validation is required. eg: photo classification and tagging
\=> I have a dataset (image with label) , I used some mathematics to teach machine that for this type of image this should be the label. based on this next time machine will predict the result based on given dataset.
SUPERVISED
RegressionClassification
NumericBinary
Multiclass
Unsupervised learning
input => ML algorithm => Predictioneg: hey! you bought this product, similarly people who brought this also purchased this product.
Grouping/Clustering
Labels unknown
Find patterns in data
Reinforcement learning
\=> Dog does not know anything , but after some training he knows what to do. and you give some sort of treat to your dog after successful execution of your command, that is reinforcement learning
Correct action rewarded
Generative AI models are self-supervised type learning
Unlabeled training data = The quick brown fox jumped over the lazy dog.
Remove word = The quick brown fox jumped over the [ ] dog.
Model predicts word = The quick brown fox jumped over the [excited] dog.
Compare to pseudo model >> excited != lazy => update model parameter and try again.
Terminology and concepts
| Statistical Definition | Everyday definition | |
| Label/Target | Dependent variable | what u r trying to predict |
| Feature | Independent variable | data that helps u make predictions (individual column name in spreadsheets) |
| Feature engineering | Data transformation | process of reshaping data to get more value out of it |
| Feature selection | Variable/subset selection | process of using the most valuable data. (helps to label) |
at -44:30 => note down ML process overview diagram
Business Problem
ML problem framing
Data phase Training phase Deployment phase
Amazon SageMaker => one stop solution for ML
prepare => build => train & tune => deploy & manage
Common Use Cases
Financial Fraud Detection
Healthcare Diagnostics
Autonomous Vehicles
Manufacturing Quality Control
Credit risk assessment
Cybersecurity threat detection
Predictive maintenance
Gen-AI models excel at tasks involving language processing, open-ended processing, and generation of human-like text or other modalities
Traditional ML and AI approaches are preferred in domains where interpretability, transparency, robustness, and strict compliance with regulations are critical requirements (a -32:30 Amazon ML stack)
Amazon SageMaker
Amazon Rekognition
Amazon Textract
Amazon Comprehend
Amazon Personalize
Amazon Kendra
Amazon Forecast
Amazon Fraud-detector