AWS certification > AWS Certified Machine Learning Specialty
AWS Certified Machine Learning
AWS Certified Machine Learning
Specialty
| Category | Specialty |
|---|---|
| Exam duration | 180 minutes to complete the exam |
| Exam format | 65 questions either multiple choice or multiple response |
| Cost | 300 USD. Visit Exam pricing for additional cost information, including foreign exchange rates |
| Delivery method | Pearson VUE testing center or online proctored exam. |
The AWS Certified Machine Learning - Specialty is intended for individuals who perform a development or data science role and have more than one year of experience developing, architecting, or running machine learning/deep learning workloads in the AWS Cloud. At least two years of hands-on experience developing, architecting, and running ML or deep learning workloads in the AWS Cloud
Course Details
1. Create data repositories for ML
- Identify data sources (for example, content and location, primary sources such as user data).
- Determine storage mediums (for example, databases, Amazon S3, Amazon Elastic File System [Amazon EFS], Amazon Elastic Block Store [Amazon EBS])
2. Identify and implement a data ingestion solution
- Identify data job styles and job types (for example, batch load, streaming).
- Orchestrate data ingestion pipelines (batch-based ML workloads and streaming-based ML workloads).
- Amazon Kinesis
- Amazon Kinesis Data Firehose
- Amazon EMR
- AWS Glue
- Amazon Managed Service for Apache Flink
- Schedule jobs
3. Identify and implement a data transformation solution
- Transform data in transit (ETL, AWS Glue, Amazon EMR, AWS Batch).
- Handle ML-specific data by using MapReduce (for example, Apache Hadoop, Apache Spark, Apache Hive).
4. Sanitize and prepare data for modelling
- Identify and handle missing data, corrupt data, and stop words.
- Format, normalize, augment, and scale data.
- Determine whether there is sufficient labelled data.
- Identify mitigation strategies.
- Use data labelling tools (for example, Amazon Mechanical Turk).
5. Perform feature engineering
- Identify and extract features from datasets, including from data sources such as text, speech, image, public datasets.
- Analyze and evaluate feature engineering concepts (for example, binning, tokenization, outliers, synthetic features, one-hot encoding, reducing dimensionality of data)
6. Analyze and visualize data for ML
- Create graphs (for example, scatter plots, time series, histograms, box plots).
- Interpret descriptive statistics (for example, correlation, summary statistics, p-value).
- Perform cluster analysis (for example, hierarchical, diagnosis, elbow plot, Cluster size)
7. Frame business problems as ML problems
- Determine when to use and when not to use ML.
- Know the difference between supervised and unsupervised learning.
- Select from among classification, regression, forecasting, clustering, and recommendation models
8. Select the appropriate model(s) for a given ML problem
- XGBoost, logistic regression, k-means, linear regression, decision trees, random forests, RNN, CNN, ensemble, transfer learning
- Express the intuition behind models
9. Train ML models
- Split data between training and validation (for example, cross validation).
- Understand optimization techniques for ML training (for example, gradient decent, loss functions, convergence).
- Choose appropriate compute resources (for example GPU or CPU, distributed or non-distributed).
- Choose appropriate compute platforms (Spark or non-Spark).
- Update and retrain models.
- Batch or real-time/online
10. Perform hyperparameter optimization
- Perform regularization.
- Drop out
- L1/L2
- Perform cross validation.
- Initialize models.
- Understand neural network architecture (layers and nodes), learning rate, and activation functions.
- Understand tree-based models (number of trees, number of levels).
- Understand linear models (learning rate)
11. Evaluate ML models
- Avoid overfitting or underfitting.
- Detect and handle bias and variance.
- Evaluate metrics (area under curve [AUC]-receiver operating characteristics [ROC], accuracy, precision, recall, Root Mean Square Error [RMSE], F1 score).
- Interpret confusion matrices.
- Perform offline and online model evaluation (A/B testing).
- Compare models by using metrics (for example, time to train a model, quality of model, engineering costs).
- Perform cross validation.
12. Build ML solutions for performance, availability, scalability, resiliency, and Fault Tolerance
- Log and monitor AWS environments.
- AWS CloudTrail and Amazon CloudWatch
- Build error monitoring solutions.
- Deploy to multiple AWS Regions and multiple Availability Zones.
- Create AMIs and golden images.
- Create Docker containers.
- Deploy Auto Scaling groups.
- Right size resources (for example, instances, Provisioned IOPS, volumes).
- Perform load balancing. 4
- Follow AWS best practices
13. Recommend and implement the appropriate ML services and features for a given problem
- ML on AWS (application services)
- Amazon Polly
- Amazon Lex
- Amazon Transcribe
- Understand AWS service quotas.
- Determine when to build custom models and when to use Amazon Sage Maker built-in algorithms.
- Understand AWS infrastructure (for example, instance types) and cost considerations.
- Use Spot Instances to train deep learning models by using AWS Batch.
14. Apply basic AWS security practices to ML solutions
- AWS Identity and Access Management (IAM)
- S3 bucket policies
- Security groups
- VPCs
- Encryption and anonymization
15. Deploy and operationalize ML solutions
- Expose endpoints and interact with them.
- Understand ML models.
- Perform A/B testing.
- Retrain pipelines.
- Debug and troubleshoot ML models.
- Detect and mitigate drops in performance.
- Monitor performance of the model
| Fees Structure : | 12500 INR / 150 USD |
| Total No of Class : | 78 Video Class |
| Class Duration : | 32:30 Working Hours |
| Download Feature : | Download Avalable |
| Technical Support : | Call / Whatsapp : +91 8680961847 |
| Working Hours : | Monday to Firday 9 AM to 6 PM |
| Payment Mode : | Credit Card / Debit Card / NetBanking / Wallet (Gpay/Phonepay/Paytm/WhatsApp Pay) |
| Fees Structure : | 22500 INR / 270 USD |
| Class Duration : | 45 Days |
| Class Recording : | Live Class Recording available |
| Class Time : | Monday to Firday 1.5 hours per day / Weekend 3 Hours per day |
| Technical Support : | Call / Whatsapp : +91 8680961847 |
| Working Hours : | Monday to Firday 9 AM to 6 PM |
| Payment Mode : | Credit Card / Debit Card / NetBanking / Wallet (Gpay/Phonepay/Paytm/WhatsApp Pay) |
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