My Cheat-sheet

AWS Infra

AWS Services

• Polly --> Text to speech
• Comprehend --> NLP --> extract relationships and metadata
• lex --> Chatbots, speech to text, text to text (does not speak!)
• Transcribe --> Speech to text (not for chatbots; does not recognize intend)
• Translate --> Language translation
• Textract --> OCR
• Rekognition --> Face Sentiment, Face Search in photos or Video, Searchable Video Lib, Moderation
• Forecast --> Managed Service for time Series forecasting
• Personalise --> Creates high-quality recommendations for your websites and applications

SageMaker

• SageMaker --> A fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly.
• Hyperparameter Tuning:
  • Define Metrics
  • Define Hyperparameter Ranges
  • Run Random or Bayesian Tuning
  • API --> HyperparameterTuner()
• Batch Transform:
  • Pre-process datasets to remove noise or bias that interferes with training or inference from your dataset.
  • Get inferences from large datasets.
  • Run inference when you don't need a persistent endpoint.
• Neo --> Enables machine learning models to train once and run anywhere in the cloud and at the edge
• Inference Pipelines --> An Amazon SageMaker model that is composed of a linear sequence of two to five containers that process requests for inferences on data
• Elastic Inference -->
  • Speed up the throughput and decrease the latency of getting real-time inferences from your deep learning models
  • Use a private link end-point service
• Standard Scaler --> Normalise Columns
• Nomaliser --> Normalise Rows
• DescribeJob API --> To check what went wrong
• Deploying a model:
  • Create a model
  • Create an endpoint configuration for an HTTPS endpoint
  • Create an HTTPS endpoint

Streaming Services

• Kinesis Data Stream --> Stream large volumes fo data for processing (EMR, Lambda, KDA)
• Kinesis Video Stream --> Stream Videos for Analytics, ML or video processing
• Kinesis Firehose --> Stream data into an end point (S3, ES, Splunk)
• Kinesis Data Analytics (KDA) --> Apply analytics on Data (Java libs (Flink), SQL)

Other Services

• Amazon Fsx --> High-performance file system (cost effective too).
• S3 --> Simple Storage Service: object storage service (structure or unstructured) that offers scalability, data availability, security, and performance (at low cost).
• Glue --> A serverless ETL service that crawls your data, builds a data catalog, performs data preparation, data transformation, and data ingestion
• CloudWatch --> A monitoring and observability service that provides you with data and actionable insights to monitor your applications (logs).
• CloudTrail --> Enables governance, compliance, operational auditing, and risk auditing of your AWS account (track API calls).
• Athena --> Interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL (needs AWS Glue)
• Amazon EMR --> Hadoop Env for Spark, Hive etc (not serverless)
• Lambda --> Run code without provisioning or managing servers (15 mins life)
• Step-Functions --> Lets you coordinate multiple AWS services into serverless workflows (like state machines)
• AWS Batch

ML & Data Science

ML Lifecycle:

• Data Ingestion
• Data Cleaning
• Feature Selection/Engineering
• Model Training & Optmisation
• Model Performance
• Model Servicing

Data Processing: Ingestion, Cleaning, Feature Selection & Engineering

• Pipe\RecordIO --> Stream data into your ML processing pipeline (faster, due to less I/O)
• Feature Selection: Algorithm runs quicker (speed up) and is more effective (accuracy).
  • Remove data that has no bearing on the target label:
    • Look at the correlation of a feature and the target label
    • Look at the feature's variance!
    • Look at percentages of missing data
• Dealing with Missing or imbalanced Data:
  • Imputing new values:
    • Mean of all values (if not too many rows are missing... else remove that row)
    • If a feature has a vary low variance or has many values missing, it can also be removed
  • Class Imbalance:
    • Source more data or synthesise more data:
      • SMOTE --> Generate mock data using means across existing datapoints (depends on how many you choose)
    • Under-sampling --> Match the records of your smallest class (ignore what's left)
    • Over-sampling --> Replicate minority class to increase it as close to other classes (no variation though...).
    • Try different algorithms!
• Feature Engineering: Engineer new features:
  • E.g. Multiply age with height!
  • Datetime --> Hour of the day
  • PCA --> Dimensionality Reduction:
    • Identify centroid (mean value of all points) and move centroid to the center of your axes
    • Draw a minimum bounding box encapsulating all data points
    • The length of the sides of these boxes is a PC and we have as many as the number of features we have
    • The length of each side (PC) defines the compoenent's variance. We can drop the lowest ones.
  • T-SNE --> Dimensionality Reduction: "Stochastic Neighbor Embedding":
    • It models each high-dimensional object by a two-or three-dimensional point in such a way that similar objects are modeled by nearby points and dissimilar objects are modeled by distant points with high probability.
  • Label encoding/one-hot encoding!
    • For catecorigal features that have no ordinal relationships.
• Splitting and Randomisation:
  • Work so that the distribution of target labels is balanced between testing and training data

Model Training & Optmisation

• Gradient Descent: Used for linear regression, logistic regression and SVMs
  • Defined by:
    • a loss Function
    • a Learning Rate (or step)
  • Optimisers:
    • Adam --> Good with escaping Local Minimals
    • Adagrad --> Optimise learning Rate
    • RMSProp --> Uses Moving Average Gradients
  • Deep Leanring:
    • Backward and forward propagation!
• Genetic Algorithms:
  • Fitness Functions
• Regularisation: Apply it when our model overfits!
  • Reduce the model's sensitivity to certain features (e.g. height).
  • Can be done through regression (L1 and L2).
• Hyperparameters: External parameters to the training job (e.g. learning rate, epochs, batch size, tree depth, )
  • Hyperparameter Tuning: E.g. Random Bayes (trial and error)!
• Cross Validation:
  • Validation of data can be used to tweak hyperparameters
  • To not lose any training data we use cross-validation: k-fold crioss validation
  • Also useful for comparing algorithms!

Model Performance

• Confusion Matrix: Used for model performance evaluation (to compare the performance across many algorithms)
  • Recall/Sensitivity (True Positives Rate): $\frac{TP}{TP+FN}$ --> The higher it is the fewer FN we have! (Use-case: Catching Fraud, FN are unacceptable!)
  • Specificity (True Negatives Rate): $\frac{TN}{TN+FP}$ --> The higher it is the fewer FP we have! (Use-case: Content Moderation, FP are unacceptable!)
  • Precision: $\frac{TP}{TP+FP}$ --> True positives proportion that where correctly classified.
  • Accuracy: $\frac{TP + TN}{ALL}$ --> May imply overfitting if too high!
  • F1 = $\frac{2RecallPrecision}{Recall + Precision}$
  • ROC: Receiver Operator Curve: Helps with identifying max-specificity and max-sensitivity cut-off points (models).
  • AUC: Area Under the Curve: Characterises the overal performance of a model! The larger it is the better!
• Entropy/Gini: Information gain
• Variance: root of squared distances of all points from the mean.
  • Used in evaluating cluster representations
  • Used in PCA

Some Algorithms:

• Logistical Regression
• Linear Regression
• Support Vector Machines
• Decision Trees
• Random Forests
• K-Means
• K-Nearest Neighbour
• Latent Dirichlet Allocation (LDA) Algorithm

Image Processing (Algorithms/Models)

• Semantic Segmentation --> like Image Classification on Pixels (pixel Colour labelling)
• Instance Segmentation --> Identify Objects in a picture or video (people, cars, trees)
• Image Localisation --> Identify Main Instance in an Image
• Image Classification (CNN) --> Label Images

NLP (Algorithm/Models)

• Remember bag-of-words, n-grams and padding, lemmatisation, tokenisation, stop-words
• Word2Vec --> Maps words to high-quality distributed vectors. Good for sentiment analysis, named entity recognition, machine translation
• Object2Vec --> A general-purpose neural embedding algorithm. generalises Word2Vec.
• BlazingText --> Similar to Word2vec, it provides the Skip-gram and continuous bag-of-words (CBOW) training architectures. Very Scalable though compared to Word2Vec.

Time Series/Anomaly Detection and more (Algorithms/Models)

• DeepAR --> RNN for scalar time series data
• Random Cut Forests --> Decition Trees for Anomaly detection (patterns)
• XGBoost --> Extrem Gradient boosting applied on Decision Trees (very effective not accounting for deep learning)
• Factorization Machines Algorithm (FMA) --> Works with highly dimensional and sparse data inputs