Certificazione 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:
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.
Si consiglia la frequentazione dei seguenti corsi:
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 Analytics
- Kinesis Firehose
- 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.
- 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 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
- Provisioned IOPS
- Load balancing
- AWS best practices
- Recommend and implement the appropriate machine learning services and features for a given problem.
- ML on AWS (application services)
- 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.
- S3 bucket policies
- Security groups
- 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