Is AWS Machine Learning Certification worth it?

The AWS Machine Learning Specialty (MLS-C01) certification has been developed to test your knowledge of applied machine and deep learning, both within the AWS environment, but also generally. Even the most highly experienced data scientists and machine learning developers consider the exam difficult without prior preparation. The AWS Machine Learning exam requires appropriate resources and hands-on experience to learn machine learning and artificial intelligence skills. Some people will only go as far as taking courses to help them pass the AWS Machine Learning exam. Let us start by knowing a little more about the exam.

AWS Machine Learning Specialty (MLS-C01)

The AWS Certified Machine Learning – Specialty certification is a professional certification that demonstrates a deep understanding of Amazon Web Services (AWS) machine learning tools and techniques. It certifies that the individual has the skills and knowledge necessary to develop, deploy, and maintain machine learning models on the AWS platform.

The certification exam covers topics such as:

1. AWS machine learning services and tools
2. Designing, deploying, and maintaining machine learning models
3. Data preparation, processing, and management
4. Machine learning algorithms and techniques
5. Model optimization and evaluation
6. Security and compliance in machine learning

The certification is intended for individuals who have a strong background in machine learning, data science, and cloud computing, and who want to demonstrate their expertise in these areas. Obtaining the AWS Certified Machine Learning – Specialty certification can help individuals differentiate themselves in the job market and increase their marketability to potential employers.

Exam overview

The AWS Machine Learning Specialist Certification exam is made up of 65 scenario-based questions that assess a candidate’s ability to solve various business problems. This is a speciality exam with a time limit of 170 minutes. The AWS Machine Learning Certification Cost is $300, though prices may vary depending on location. The exam can be scheduled through Pearson VUE or PSI.

The questions are of the multiple-choice and multiple-response variety. The AWS machine learning speciality exam is graded on a scale of 1 to 1000, with 750 being the passing score. The Amazon Web Services machine learning speciality exam is available in English, Japanese, Korean, and Simplified Chinese.

Who should take the exam?

Amazon suggests that a candidate taking the exam have the following knowledge and experience:

• To begin, you should have at least 1-2 years of experience developing, architecting, or running ML/deep learning workloads on the AWS Cloud.
• As a result, the ability to express the intuition underlying basic ML algorithms.
• Also, you should have some experience with basic hyperparameter optimization.
• Experience with machine learning and deep learning frameworks is also required.
• In addition, the ability to adhere to model-training best practices.
• Finally, you must be able to adhere to deployment and operational best practices.

Let us now move to the main point of the article –

Is AWS Machine Learning Certification worth it?

The ecosystem is thriving and expanding, and traditional educational pathways are struggling to keep up. Earning and displaying the AWS Machine Learning certification on your resume represents deep technical knowledge and critical thinking ability. Employers and managers recognize that it denotes a thorough understanding of algorithms, frameworks, and best practices, and the ability to apply that knowledge to real-world solutions on AWS.

Only Carnegie Mellon University in the United States offers a bachelor’s level machine learning program, despite the industry’s dire need. All other programs are at the master’s or doctoral level, which means massive amounts of student debt. In comparison, the AWS Certified Machine Learning – Specialty exam costs $300 once, and the practice exam costs $40. The test takes 180 minutes to complete, and preparation typically takes 40+ hours.

Here are some factors to consider:

1. Market demand: The demand for professionals with AWS machine learning skills is growing as organizations increasingly adopt cloud-based machine learning solutions. If there is high demand in your area, obtaining the certification could make you more competitive in the job market.
2. Career advancement: The certification can demonstrate to employers that you have a deep understanding of AWS machine learning and can help you advance in your career or open up new job opportunities.
3. Learning opportunities: The certification process provides an opportunity to gain hands-on experience with AWS machine learning and deepen your knowledge of this field.
4. Marketability: The certification can increase your marketability and credibility as a professional who is capable of developing, deploying, and maintaining machine learning models on the AWS platform.
5. Career goals: If your career goals include working with machine learning and AWS, the certification can help you achieve these goals and demonstrate your expertise.

Let us now move to the course outline to know more about the exam –

Syllabus outline

The Amazon AWS Machine Learning Certification exam will put you through your paces in the following areas. The domain compositions are also fixed. Consider the AWS Machine Learning Certification Course Outline –

Domain 1: Data Engineering

1.1 Create data repositories for machine learning.

• Identify data sources (e.g., content and location, primary sources such as user data) (AWS Documentation: Supported data sources)
• Determine storage mediums (e.g., DB, Data Lake, S3, EFS, EBS) (AWS Documentation: Using Amazon S3 with Amazon ML, Creating a Datasource with Amazon Redshift Data, Using Data from an Amazon RDS Database, Host instance storage volumes, Amazon Machine Learning and Amazon Elastic File System)

1.2 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 (AWS Documentation: Amazon Kinesis Data Streams)
  • Kinesis Analytics (AWS Documentation: Amazon Kinesis Data Analytics)
  • Kinesis Firehose (AWS Documentation: Build seamless data streaming pipelines)
  • EMR (AWS Documentation: Process Data Using Amazon EMR with Hadoop Streaming, Optimize downstream data processing)
  • Glue (AWS Documentation: Simplify data pipelines, AWS Glue)
• Job scheduling (AWS Documentation: Job scheduling, Time-based schedules for jobs and crawlers)

1.3 Identify and implement a data transformation solution.

• Transforming data transit (ETL: Glue, EMR, AWS Batch) (AWS Documentation: extract, transform, and load data for analytic processing using AWS Glue)
• Handle ML-specific data using map reduce (Hadoop, Spark, Hive) (AWS Documentation: Large-Scale Machine Learning with Spark on Amazon EMR, Apache Hive on Amazon EMR, Apache Spark on Amazon EMR, Use Apache Spark with Amazon SageMaker, Perform interactive data engineering and data science workflows)

Domain 2: Exploratory Data Analysis

2.1 Sanitize and prepare data for modeling.

• Identify and handle missing data, corrupt data, stop words, etc. (AWS Documentation: Managing missing values in your target and related datasets, Amazon SageMaker DeepAR now supports missing values, Configuring Text Analysis Schemes)
• Formatting, normalizing, augmenting, and scaling data (AWS Documentation: Understanding the Data Format for Amazon ML, Common Data Formats for Training, Data Transformations Reference, AWS Glue DataBrew, Easily train models using datasets, Visualizing Amazon SageMaker machine learning predictions)
• Labeled data (recognizing when you have enough labeled data and identifying mitigation strategies [Data labeling tools (Mechanical Turk, manual labor)]) (AWS Documentation: data labeling for machine learning, Amazon Mechanical Turk, Use Amazon Mechanical Turk with Amazon SageMaker)

2.2 Perform feature engineering.

• Identify and extract features from data sets, including from data sources such as text, speech, image, public datasets, etc. (AWS Documentation: Feature Processing, Feature engineering, Amazon Textract, Amazon Textract features)
• Analyze/evaluate feature engineering concepts (binning, tokenization, outliers, synthetic features, 1 hot encoding, reducing dimensionality of data) (AWS Documentation: Data Transformations Reference, Building a serverless tokenization solution to mask sensitive data, ML-powered anomaly detection for outliers, ONE_HOT_ENCODING, Running Principal Component Analysis, Perform a large-scale principal component analysis)

2.3 Analyze and visualize data for machine learning.

• Graphing (scatter plot, time series, histogram, box plot) (AWS Documentation: Using scatter plots, Run a query that produces a time series visualization, Using histograms, Using box plots)
• Interpreting descriptive statistics (correlation, summary statistics, p value)
• Clustering (hierarchical, diagnosing, elbow plot, cluster size)

Domain 3: Modeling

3.1 Frame business problems as machine learning problems.

• Determine when to use/when not to use ML (AWS Documentation: When to Use Machine Learning)
• Know the difference between supervised and unsupervised learning
• Selecting from among classification, regression, forecasting, clustering, recommendation, etc. (AWS Documentation: K-means clustering with Amazon SageMaker, Building a customized recommender system in Amazon SageMaker)

3.2 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 (AWS Documentation: XGBoost Algorithm, K-means clustering with Amazon SageMaker, Forecasting financial time series, Amazon Forecast can now use Convolutional Neural Networks, Detecting hidden but non-trivial problems in transfer learning models)
• Express intuition behind models

3.3 Train machine learning models.

• Train validation test split, cross-validation (AWS Documentation: Train a Model, Incremental Training, Managed Spot Training, Validate a Machine Learning Model, Cross-Validation, Model support, metrics, and validation, Splitting Your Data)
• 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]) (AWS Documentation: Introduction to Apache Spark)
• Model updates and retraining (AWS Documentation: Retraining Models on New Data, Automating model retraining and deployment)
  • Batch vs. real-time/online

3.4 Perform hyperparameter optimization.

• Regularization (AWS Documentation: Training Parameters)
  • Drop out
  • L1/L2
• Cross validation (AWS Documentation: Cross-Validation)
• Model initialization
• Neural network architecture (layers/nodes), learning rate, activation functions
• Tree-based models (# of trees, # of levels)
• Linear models (learning rate)

3.5 Evaluate machine learning models.

• Avoid overfitting/underfitting (detect and handle bias and variance) (AWS Documentation: Underfitting vs. Overfitting, Amazon SageMaker Clarify Detects Bias and Increases the Transparency, Amazon SageMaker Clarify)
• Metrics (AUC-ROC, accuracy, precision, recall, RMSE, F1 score)
• Confusion matrix (AWS Documentation: Custom classifier metrics)
• Offline and online model evaluation, A/B testing (AWS Documentation: Validate a Machine Learning Model, Machine Learning Lens)
• Compare models using metrics (time to train a model, quality of model, engineering costs) (AWS Documentation: Easily monitor and visualize metrics while training models, Model Quality Metrics, Monitor model quality)
• Cross validation (AWS Documentation: Cross-Validation, Model support, metrics, and validation)

Domain 4: Machine Learning Implementation and Operations

4.1 Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance. (AWS Documentation: Review the ML Model’s Predictive Performance, Best practices, Resilience in Amazon SageMaker)

• AWS environment logging and monitoring (AWS Documentation: Logging and Monitoring)
  • CloudTrail and CloudWatch (AWS Documentation: Logging Amazon ML API Calls with AWS CloudTrail, Log Amazon SageMaker API Calls, Monitoring Amazon ML, Monitor Amazon SageMaker)
  • Build error monitoring (AWS Documentation: ML Platform Monitoring)
• Multiple regions, Multiple AZs (AWS Documentation: Regions and Endpoints, Best practices)
• AMI/golden image (AWS Documentation: AWS Deep Learning AMI)
• Docker containers (AWS Documentation: Why use Docker containers for machine learning development, Using Docker containers with SageMaker)
• Auto Scaling groups (AWS Documentation: Automatically Scale Amazon SageMaker Models, Configuring autoscaling inference endpoints)
• Rightsizing
  • Instances (AWS Documentation: Ensure efficient compute resources on Amazon SageMaker)
  • Provisioned IOPS (AWS Documentation: Optimizing I/O for GPU performance tuning of deep learning)
  • Volumes (AWS Documentation: Customize your notebook volume size, up to 16 TB)
• Load balancing (AWS Documentation: Managing your machine learning lifecycle)
• AWS best practices (AWS Documentation: Machine learning best practices in financial services)

4.2 Recommend and implement the appropriate machine learning services and features for a given problem.

• ML on AWS (application services)
  • Poly (AWS Documentation: Amazon Polly, Build a unique Brand Voice with Amazon Polly)
  • Lex (AWS Documentation: Amazon Lex, Build more effective conversations on Amazon Lex)
  • Transcribe (AWS Documentation: Amazon Transcribe, Transcribe speech to text in real time)
• AWS service limits (AWS Documentation: Amazon SageMaker endpoints and quotas, Amazon Machine Learning endpoints and quotas, System Limits)
• Build your own model vs. SageMaker built-in algorithms (AWS Documentation: Use Amazon SageMaker Built-in Algorithms or Pre-trained Models)
• Infrastructure: (spot, instance types), cost considerations (AWS Documentation: Instance Types for Built-in Algorithms)
  • Using spot instances to train deep learning models using AWS Batch (AWS Documentation: Train Deep Learning Models on GPUs)

4.3 Apply basic AWS security practices to machine learning solutions.

• IAM (AWS Documentation: Controlling Access to Amazon ML Resources, Identity and Access Management in AWS Deep Learning Containers)
• S3 bucket policies (AWS Documentation: Using Amazon S3 with Amazon ML, Granting Amazon ML Permissions to Read Your Data from Amazon S3)
• Security groups (AWS Documentation: Secure multi-account model deployment with Amazon SageMaker, Use an AWS Deep Learning AMI)
• VPC (AWS Documentation: Securing Amazon SageMaker Studio connectivity, Direct access to Amazon SageMaker notebooks, Building secure machine learning environments)
• Encryption/anonymization (AWS Documentation: Protect Data at Rest Using Encryption, Protecting Data in Transit with Encryption, Anonymize and manage data in your data lake)

4.4 Deploy and operationalize machine learning solutions.

• Exposing endpoints and interacting with them (AWS Documentation: Creating a machine learning-powered REST API, Call an Amazon SageMaker model endpoint)
• ML model versioning (AWS Documentation: Model versioning, Register a Model Version)
• A/B testing (AWS Documentation: A/B Testing ML models in production, Dynamic A/B testing for machine learning models)
• Retrain pipelines (AWS Documentation: Automating model retraining and deployment, Machine Learning Lens)
• ML debugging/troubleshooting (AWS Documentation: Debug Your Machine Learning Models, Analyzing open-source ML pipeline models in real time, Troubleshoot Amazon SageMaker model deployments)
  • Detect and mitigate drop in performance (AWS Documentation: Identify bottlenecks, improve resource utilization, and reduce ML training costs, Optimizing I/O for GPU performance tuning of deep learning training)
  • Monitor performance of the model (AWS Documentation: Monitor models for data and model quality, bias, and explainability, Monitoring in-production ML models at large scale)

Now, we will be looking at some of the resources that help you ace the exam in one go!

The Official learning path by Amazon

Amazon’s official website suggests combining hands-on experience with online training and sample papers in order to ace the exam. Always make a point of visiting the official website to learn more about each aspect of the exam. The official site contains information about the exam’s technical aspects as well as the most recent updates. There are numerous official resources for the exam available on Amazon. Amazon is also offering free webinars to help spread awareness of the exam. Amazon also offers a variety of classroom sessions and expert-led courses, as listed below:

Recommended Progression

• Machine Learning Exam Basics
• Also, Process Model: CRISP-DM on the AWS Stack
• furthermore, The Elements of Data Science
• moreover, Storage Deep Dive Learning Path
• also, Machine Learning Security
• moreover, Developing Machine Learning Applications
• Types of Machine Learning Solutions

Branching content areas

• Communicating with Chat Bots
• Speaking of: Machine Translation and NLP
• Seeing Clearly: Computer Vision Theory

Optional training

• Big Data on AWS

For more training options, you can visit Training Library by Amazon for machine learning.

Study groups and discussions

You can join a variety of study groups to help you improve your preparations and pool different resources. Discussions allow you to put your knowledge to the test. Try to form groups with more interactive people, as this will help you get answers faster. This will help you develop a competitive spirit and improve your performance.

Practice papers and test series

The only way to pass the exam with a good score is to take the Practice Exam. The more you practice, the clearer your ideas will become. Always practice sample papers and take as many test series as you can. This will aid in the discovery of loopholes and the identification of weak points. You will find the areas where you need to work harder and the areas where you are completely prepared for the exam. This is the most important step in the preparation process. Try a free practice test now!

Some Basic Exam Tips:

1. Familiarize yourself with the AWS Machine Learning services and tools, including Amazon SageMaker, Amazon Comprehend, Amazon Rekognition, and others.
2. Study the official AWS Certified Machine Learning – Specialty exam objectives and syllabus.
3. Practice using AWS Machine Learning services and tools to develop, deploy, and maintain machine learning models.
4. Familiarize yourself with machine learning algorithms and techniques, including supervised and unsupervised learning, deep learning, and others.
5. Learn about data preparation, processing, and management, including data storage, retrieval, and cleaning.
6. Study best practices for model optimization and evaluation, including model selection, training, and testing.
7. Get hands-on experience with security and compliance in machine learning, including data protection and privacy.
8. Take advantage of AWS’s training resources and sample exams to supplement your self-study.
9. Join online forums and discussion groups to connect with other AWS professionals and learn from their experiences.
10. Make sure you are comfortable with the examination format, timing, and delivery platform before taking the certification exam.

Conclusion

Whether the AWS Machine Learning certification is worth it depends on your career goals and the demands of the job market. The certification can demonstrate to employers and clients that you have a deep understanding of Amazon Web Services (AWS) machine learning tools and techniques and that you are capable of using them to develop, deploy, and maintain machine learning models. This can increase your marketability and potentially lead to career advancement or higher pay.

On the other hand, if your current role and responsibilities do not require knowledge of AWS machine learning or if the job market in your area does not place a high value on this certification, obtaining the certification may not be as beneficial.

Ultimately, the value of the AWS Machine Learning certification will depend on your specific circumstances, so it is important to carefully consider your career goals and the demands of the job market before making a decision.