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The AWS Certified Machine Learning Specialty certificate is intended for programmers, data scientists, and other candidates passionate about machine learning who want to learn how to use the benefits of artificial intelligence capabilities on the AWS platform. The training process necessary for obtaining this certification helps examinees develop the right skills to build, train, and deploy machine learning models using advanced AWS Cloud services. Candidates can achieve this certificate by obtaining the passing score in MLS-C01 exam. Even though this test doesn't have any mandatory requirements, the vendor recommends that candidates should have previous knowledge of certain topics. A successful applicant is one who has between 1 to 2 years of practical experience in developing, running and architecting ML and deep learning workloads on the AWS Cloud. Also, it would be helpful if the candidate would have prior experience performing basic hyperparameter optimization and know how to follow model-training and operational best practices.
| Section | Objectives |
|---|---|
Data Engineering - 20% | |
| 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) |
| 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) |
Exploratory Data Analysis - 24% | |
| 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) |
Modeling - 36% | |
| 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, etc. - Compute choice (GPU vs. CPU, distributed vs. non-distributed, platform [Spark vs. non-Spark]) - Model updates and retraining
|
| Perform hyperparameter optimization. | - Regularization
- Cross validation |
| 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) - Cross validation |
Machine Learning Implementation and Operations - 20% | |
| Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance. | - AWS environment logging and monitoring
- Multiple regions, Multiple AZs
- Load balancing |
| Recommend and implement the appropriate machine learning services and features for a given problem. | - ML on AWS (application services)
- AWS service limits
|
| Apply basic AWS security practices to machine 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
|
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AWS Certified Machine Learning – Specialty is a standard exam for candidates who want to excel in the development field and data science and verify their competence by earning certification. This test, coded MLS-C01, helps the individuals to measure their knowledge of the design, deployment, implementation, and maintenance of machine learning solutions.
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