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IA Machine and Deep Learning AWS Services Fundamentals

Il corso esplora gli AI Amazon Web Services specificatamente dedicati per soluzioni ML , DP ed Analytics correlati. la Reference Architecture è attentamente delineata al fine della acquisizione delle competenze e della Knowledge per le "Features" e  "Capabilities" che costituisco i "building block" Component per la varie implementazioni adottabili. 
il corso esplora le principali metodologie e tecnologie in materia di Tools, Platform dei Servizi AWS dedicati al capitolo "Intelligenza Artificiale".

DURATA

min 10 gg piano formativo personalizzabile

ATTESTATI

Attestato di partecipazione First Consulting 

CERTIFICAZIONI

Propedeutico
Path Certificazione AWS 

KEY POINT

Intelligenza Artificiale 
Cloud AWS ML e DP
Amazon Web Service

Programma

Domain 1 Data Engineering

Create data repositories for machine learning.

  • Identify data sources (e.g., content and location, primary sources such as user data)

  • Determine storage mediums (e.g., DB, Data Lake, S3, EFS, EBS)


Identify and implement a data ingestion solution.

  • Data job styles/types (batch load, streaming)

  • Data ingestion pipelines (Batch-based ML workloads and streaming-based ML workloads)Kinesis Analytics

  • Kinesis Firehose

  • EMR o Glue

  • Job scheduling
     

Identify and implement a data transformation solution.

  • Transforming data transit (ETL: Glue, EMR, AWS Batch)

  • Handle ML-specific data using map reduce (Hadoop, Spark, Hive)
     

Domain 2: Exploratory Data Analysis

Sanitize and prepare data for modelling.

  • Identify and handle missing data, corrupt data, stop words, etc.

  • Formatting, normalizing, augmenting, and scaling data

  • Labeled data (recognizing when you have enough

  • labeled data and identifying mitigation strategies
     

Perform feature engineering.

  • Identify and extract features from data sets, including from data sources such as text, speech, image, public datasets, etc.

  • Analyze/evaluate feature engineering concepts (binning, tokenization, outliers, synthetic features, 1 hot encoding, reducing dimensionality of data)
     

Analyze and visualize data for machine learning.

  • Graphing (scatter plot, time series, histogram, box plot)

  • Interpreting descriptive statistics (correlation, summary statistics, p value)

  • Clustering (hierarchical, diagnosing, elbow plot, cluster size)

 

Domain 3 Modeling

 Frame business problems as machine learning problems.

  • Determine when to use/when not to use ML

  • Know the difference between supervised and unsupervised learning

  • Selecting from among classification, regression, forecasting, clustering, recommendation, etc.
     

Select the appropriate model(s) for a given machine learning problem.

  • Xgboost, logistic regression, K-means, linear regression, decision trees, random forests, RNN, CNN, Ensemble, Transfer learning

  • Express intuition behind models

 Train machine learning models.

  • Train validation test split, cross-validation

  • Optimizer, gradient descent, loss functions, local minima, convergence, batches, probability

  • Compute choice (GPU vs. CPU, distributed vs. non-distributed, platform [Spark vs. non-Spark])

  • Model updates and retraining o Batch vs. real-time/online
     

 Perform hyperparameter optimization.

  • Regularization Drop out

  • Cross validation

  • Model initialization

  • Neural network architecture (layers/nodes), learning rate, activation functions

  • Tree-based models (# of trees, # of levels)

  • Linear models (learning rate)
     

 Evaluate machine learning models.

  • Avoid overfitting/underfitting (detect and handle bias and variance)

  • Metrics (AUC-ROC, accuracy, precision, recall, RMSE, F1 score)

  • Confusion matrix

  • Offline and online model evaluation, A/B testing

  • Compare models using metrics (time to train a model, quality of model, engineering costs)

Domain 4: Machine Learning Implementation and Operations

 

Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance.

  • AWS environment logging and monitoring CloudTrail and CloudWatch o Build error monitoring

  • Multiple regions, Multiple AZs

  • AMI/golden image

  • Docker containers

  • Auto Scaling groups

  • Rightsizing o Instances o Provisioned IOPS o Volumes

  • Load balancing

  • AWS best practices
     

Recommend and implement the appropriate machine learning services and features

  • ML on AWS (application services) and Transcribe

  • AWS service Limits

  • Build your own model vs. Sage Maker built-in algorithms

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