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Certificazione Certified Machine Learning – Specialty

Corsi e Certificazioni Amazon AWS - Amazon Web Service - AWS Certification - Formazione AWS - Cloud Practtioner - Solution Architect - DevOps Engineer - Developer - SysOps Administrator - Aws Machine Learning - AWS Security - AWS Database - AWS Data Analytics - AWS Specialty

Certificazione Certified Machine Learning – Specialty

Panoramica | Svolgimento e Durata | Prerequisiti
Argomenti D’esame   |  Corsi di Preparazione

Panoramica   Svolgimento e Durata
Prerequisiti
Argomenti D’esame    Corsi di Preparazione

PANORAMICA

Certificazione AWS Certified Machine Learning - Specialty

Esame AWS Certified Machine Learning – Specialty;

 

The AWS Certified Machine Learning – Specialty (MLS-C01) exam is intended for individuals who perform an artificial intelligence/machine learning (AI/ML) development or data science role. The exam validates a candidate’s ability to design, build, deploy, optimize, train, tune, and maintain ML solutions for given business problems by using the AWS Cloud.

The exam also validates a candidate’s ability to complete the following tasks:

  • Select and justify the appropriate ML approach for a given business problem
  • Identify appropriate AWS services to implement ML solutions
  • Design and implement scalable, cost-optimized, reliable, and secure ML solutions

Per conseguire la Certificazione AWS Certified Machine Learning – Specialty è necessario sostenere con successo il seguente esame:
AWS MLS-C01;

Corsi propedeutici alla certificazione

Corsi di Preparazione:

  • Practical Data Science with Amazon SageMaker
  • The Machine Learning Pipeline on AWS
  • Deep Learning on AWS

Conttaci ora per ricevere tutti i dettagli e per richiedere, senza alcun impegno, di parlare direttamente con uno dei nostri Docenti CLICCA QUI.
Oppure chiamaci subito al nostro numero verde  800-177596.

 SVOLGIMENTO E DURATA

Esame AWS Certified Machine Learning – Specialty Durata 180 minuti circa 65 quesiti;

Negli esami sono presenti quesiti formulati in lingua inglese in forme differenti: Risposta Multipla; completamento di testo, collegamenti concettuali Drag and Drop; vere e proprie simulazioni laboratoriali.

 PREREQUISITI

Si consiglia la frequentazione dei seguenti corsi:

  • Practical Data Science with Amazon SageMaker
  • The Machine Learning Pipeline on AWS
  • Deep Learning on AWS
  • Exam Readiness: AWS Certified Machine Learning – Specialty

ARGOMENTI D’ESAME

Esame AWS Certified Machine Learning – Specialty – MLS-C01

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
  • Kinesis Analytics
  • Kinesis Firehose
  • EMR
  • 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 modeling.
  • 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 [Data labeling tools (Mechanical Turk, manual labor)])
  • 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 tuse/when not tuse 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
  • Batch vs. real-time/online
  • Perform hyperparameter optimization.
  • Regularization
  • Drop out
  • L1/L2
  • 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 ttrain a model, quality of model, engineering costs)
  • Cross validation

 

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
  • Build error monitoring
  • Multiple regions, Multiple AZs
  • AMI/golden image
  • Docker containers
  • AutScaling groups
  • Rightsizing
  • Instances
  • Provisioned IOPS
  • Volumes
  • Load balancing
  • AWS best practices
  • Recommend and implement the appropriate machine learning services and features for a given problem.
  • ML on AWS (application services)
  • Poly
  • Lex
  • Transcribe
  • AWS service limits
  • Build your own model vs. SageMaker built-in algorithms
  • Infrastructure: (spot, instance types), cost considerations
  • Using spot instances ttrain deep learning models using AWS Batch
  • Apply basic AWS security practices tmachine learning solutions.
  • IAM
  • S3 bucket policies
  • Security groups
  • VPC
  • Encryption/anonymization
  • Deploy and operationalize machine learning solutions.
  • Exposing endpoints and interacting with them
  • ML model versioning
  • A/B testing
  • Retrain pipelines
  • ML debugging/troubleshooting
  • Detect and mitigate drop in performance
  • Monitor performance of the model

 CORSI DI PREPARAZIONE

  • Practical Data Science with Amazon SageMaker
  • The Machine Learning Pipeline on AWS
  • Deep Learning on AWS
  • MLOps Engineering on AWS
  • Exam Readiness: AWS Certified Machine Learning – Specialty
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