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AWS Certified Machine Learning – Specialty
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Course Description
Overview
The AWS Certified Machine Learning – Specialty 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
Objectives
Audience
Prerequisites
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Before you take this exam, we recommend you have:
- At least two years of hands-on experience developing, architecting, and running ML or deep learning workloads in the AWS Cloud
- Ability to express the intuition behind basic ML algorithms
- Experience performing basic hyperparameter optimization
- Experience with ML and deep learning frameworks
- Ability to follow model-training, deployment, and operational best practices
- The ability to express the intuition behind basic ML algorithms
- Experience performing basic hyperparameter optimization
- Experience with ML and deep learning frameworks
- The ability to follow model-training best practices
- The ability to follow deployment best practices
- The ability to follow operational best practices
Topics
- 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)
- 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
- Transforming data transit (ETL: Glue, EMR, AWS Batch)
- Handle ML-specific data using map reduce (Hadoop, Spark, Hive)
- 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)])
- 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)
- Graphing (scatter plot, time series, histogram, box plot)
- Interpreting descriptive statistics (correlation, summary statistics, p value)
- Clustering (hierarchical, diagnosing, elbow plot, cluster size)
- 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.
- Xgboost, logistic regression, K-means, linear regression, decision trees, random forests, RNN, CNN, Ensemble, Transfer learning
- Express intuition behind 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
- Batch vs. real-time/online
- 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)
- 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
- AWS environment logging and monitoring: CloudTrail and CloudWatch, Build error monitoring
- Multiple regions, Multiple AZs
- AMI/golden image
- Docker containers
- Auto Scaling groups
- Rightsizing: Instances, Provisioned IOPS, Volumes
- Load balancing
- AWS best practices
- 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 to train deep learning models using AWS Batch
- IAM
- S3 bucket policies
- Security groups
- VPC
- Encryption/anonymization
- 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
Related Courses
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Practical Data Science with Amazon SageMaker
AWS-175- Duration: 1 Day
- Delivery Format: Classroom Training, Online Training
- Price: 675.00 USD
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MLOps Engineering on AWS
AWS-245- Duration: 3 Days
- Delivery Format: Classroom Training, Online Training
- Price: 2,025.00 USD
Self-Paced Training Info
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- Same in-demand topics as instructor-led public and private classes.
- Standalone learning or supplemental reinforcement.
- e-Learning content varies by course and technology.
- View the Self-Paced version of this outline and what is included in the SPVC course.
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Self-Paced Training Terms & Conditions
THIS IS A SELF-PACED VIRTUAL CLASS. AFTER YOU REGISTER, YOU HAVE 30 DAYS TO COMPLETE THE COURSE.
Before you enroll, review the system requirements to ensure that your system meets the minimum requirements for this course. AFTER YOU ARE ENROLLED IN THIS COURSE, YOU WILL NOT BE ABLE TO CANCEL YOUR ENROLLMENT. You are billed for the course when you submit the enrollment form. Self-Paced Virtual Classes are non-refundable. Once you purchase a Self-Paced Virtual Class, you will be charged the full price.
After you receive confirmation that you are enrolled, you will be sent further instructions to access your course material and remote labs. A confirmation email will contain your online link, your ID and password, and additional instructions for starting the course.
You can start the course at any time within 12 months of enrolling for the course. After you register/start the course, you have 30 days to complete your course. Within this 30 days, the self-paced format gives you the opportunity to complete the course at your convenience, at any location, and at your own pace. The course is available 24 hours a day.
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