Top 7 AWS Services for Machine Learning

Are you looking to build scalable and effective machine learning solutions? AWS offers a comprehensive suite of services designed to simplify every step of the ML lifecycle, from data collection to model monitoring. With purpose-built tools, AWS has positioned itself as a leader in the field, helping companies streamline their ML processes. In this article, we’ll dive into the top 7 AWS services that can accelerate your ML projects, making it easier to create, deploy, and manage machine learning models.

What is the Machine Learning Lifecycle?

The machine learning (ML) lifecycle is a continuous cycle that starts with identifying a business issue and ends when a solution is deployed in production. Unlike traditional software development, ML takes an empirical, data-driven approach, requiring unique processes and tools. Here are the primary stages:

1. Data Collection: Gather quality data from various sources to train the model.
2. Data Preparation: Clean, transform, and format data for model training.
3. Exploratory Data Analysis (EDA): Understand data relationships and outliers that may impact the model.
4. Model Building/Training: Develop and train algorithms, fine-tuning them for optimal results.
5. Model Evaluation: Assess model performance against business goals and unseen data.
6. Deployment: Put the model into production for real-world predictions.
7. Monitoring & Maintenance: Continuously evaluate and retrain the model to ensure relevance and effectiveness.

Importance of Automation and Scalability in the ML Lifecycle

As our ML projects scale up in complexity we see that manual processes break down. An automated lifecycle which in turn tends to do:.

• Faster iteration and experimentation
• Reproducible workflows
• Efficient resource utilization
• Consistent quality control
• Reduced Operational Overhead

Scalability is key as data volumes grow at the same time models have to handle more requests. Also we see that great ML systems which are well designed will scale to large data sets and at the same time will report high throughput inference without trade off in performance.

AWS Services by Machine Learning Lifecycle Stage

Data Collection

The primary service for the process of Data Collection can be served by Amazon S3. Amazon Simple Storage Service or Amazon S3 forms the building block upon which most ML workflows in AWS operate. Being a highly scalable, durable, and secure object storage system, it is more than capable of storing the gigantic datasets that ML model building would require.

 Key Features of Amazon S3  

• Virtually unlimited storage capacity with an exabyte-scale capability
• 99.99% data durability guarantee.
• Fine-grained access controls through IAM policies and bucket policies.
• Versioning and lifecycle management for data governance
• Integration with AWS analytics services for seamless processing.
• Cross-region replication for geographical redundancy.
• Event notifications trigger workflows when the data changes.
• Data encryption options for compliance and security.

Technical Capabilities of Amazon S3

• Supports objects up to 5TB in size.
• Performance-optimized through multipart uploads and parallel processing
• S3 Transfer Acceleration for fast upload over long distances.
• Intelligent Tiering storage class that moves data automatically between access tiers based on usage patterns
• S3 Select for server-side filtering to reduce data transfer costs and increase performance

Pricing Optimization of Amazon S3

While the Amazon S3 has a free tier for 12 Months, offering 5GB in the S3 Standard Storage class which provides 20,000 GET requests and 2000 Put, Copy, Post, or List requests as well. 

Other than this free tiers, it offers other packages for data storage that comes with more advanced features. You can pay for storing object in S3 buckets and the charge reasonably depends on your bucket size, duration of the object stored for, and the storage class.

• With lifecycle policies, objects can be automatically transitioned to cheaper storage tiers.
• Enabling the S3 Storage lens can identify any potential cost-saving avenues.
• Configure retention policies correctly so that unnecessary storage costs are not accrued.
• S3 Inventory is utilized to track objects and their metadata throughout your storage.

Alternative Services for Data Collection

• AWS Data Exchange: When you look for third party datasets Amazon Data Exchange has a catalog of which providers in many industries have put up their data. This service also includes the search out, subscription, and use of external datasets.
• Amazon Kinesis: In the field of real time data collection Amazon Kinesis allows you to collect, process, and analyze streaming data as it comes in. It does especially well with Machine Learning applications which require continuous input and learning from that input.
• Amazon Textract: If in documents your data is extracted by Textract which also includes hand written content from scanned documents and makes it available to the ML process.

Data Preparation

The data preparation is one of the most crucial processes in ML Lifecycle as it decides on what kind of ML model we’ll get at last and to service this, we can make use of immutable AWS Glue which offers ETL software that is convenient for analytics and ML data preparation.

Key Features of AWS Glue

• Serverless provides automatic scaling according to workload demand
• Visual job designer for ETL data transformations without coding
• Embedded data catalog for metadata management across AWS
• Support for Python and Scala scripts using user-defined libraries
• Scheme inference and discovery
• Batch and streaming ETL workflows
• Data Validation and Profiling
• Built-in job scheduling and monitoring
• Integration with AWS Lake Formation for fine-grained access control

Technical Capabilities of AWS Glue

• Supports multiple data sources such as S3, RDS, DynamoDB, and JDBC
• Runtime environment optimized for Apache Spark Processing
• Data Abstraction as dynamic frames for semi-structured data
• Custom transformation scripts in PySpark or Scala
• Built-in ML transforms for data preparation 
• Support collaborative development with Git Integration
• Incremental processing using job bookmarks

Performance Optimization of AWS Glue

• Partition data effectively to enable parallel processing
• Take advantage of Glue’s internal performance monitoring to locate bottlenecks
• Set the type and number of workers depending on the workload
• Designing a data partitioning strategy corresponding to query patterns
• Use push-down predicates wherever applicable to enable fewer scan processes

Pricing of AWS Glue

The costing of AWS Glue is very reasonable as you only have to pay for the time spent to extract, transform and load the job. You will be charged based on the hourly-rate on the number of Data Processing Units used to run your jobs. 

Alternative Services for Data Preparation

• Amazon SageMaker Data Wrangler: Data Science professionals prefer a visual interface and in Data Wrangler we have over 300 built in data transformations and data quality checks which do not require any code.
• AWS Lake Formation: In the design of a full scale data lake for ML we see that Lake formation puts in place a smooth workflow through the automation of what would be a large set of complex manual tasks which include data discovery, cataloging, and access control.
• Amazon Athena: In Athena SQL teams are able to perform freeform queries of S3 data which in turn easily generates insights and prepares smaller data sets for training.

Exploratory Data Analysis (EDA)

SageMaker Data Wrangler excels at visualizing EDA with built-in visualizations and provides over 300 data transformations for comprehensive data exploration.

Key Features

• Visual access to instant data insights without code.
• Built in we have histograms, scatter plots, and correlation matrices.
• Outlier identification and data quality evaluation.
• Interactive data profiling with statistical summaries
• Support of using large scale samples for efficient exploration.
• Data transformation recommendations according to data characteristics.
• Exporting too many formats for in depth analysis.
• Integration with feature engineering workflows
• One-click data transformation with visual feedback
• Support for many data sources which includes S3, Athena and Redshift.

Technical Capabilities

• Point and click for data exploration
• Automated creation of data quality reports and also put forth recommendations.
• Designing custom visualizations which fit analysis requirements.
• Jupyter notebook integration for advanced analyses
• Capable of working with large data sets through the use of smart sampling.
• Provision of built-in statistical analysis techniques
• Data lineage analyses for transformation workflows
• Export your transformed data to S3 or to the SageMaker Feature store.

Performance Optimization

• Reuse transformation workflows
• Use pre-built models which contain common analysis patterns.
• Use tools which report back to you automatically to speed up your analysis of the data.
• Export analysis results to stakeholders.
• Integrate insights with downstream ML workflows

Pricing of Amazon SageMaker Data Wrangler

The pricing of Amazon SageMaker Data Wrangler is primarily based on the compute resources allocated during the interactive session and processing job, as well as the corresponding storage. The state reports that for interactive data preparation in SageMaker Studio they charge by the hour which varies by instance type. There are also costs associated with storing the data in Amazon S3 and attached volumes during processing. 

For instance we see that the ml.m5.4xlarge instance goes for about $0.922 per hour. Also which types of processing jobs that run data transformation flows is a factor of the instance type and the duration of resource use. The same ml.m5.4xlarge instance would cost roughly $0.615 for a 40-minute job.  It is best to shut down idle instances as soon as practical and to use the right instance type for your work load to see cost savings.

For more pricing information, you can explore this link.

Alternative Services for EDA

• Amazon SageMaker Studio: Gives you a full featured IDE for machine learning, we have Jupyter Notebooks, real time collaboration, and also included are interactive data visualization tools.
• Amazon Athena: When you wish to perform ad hoc queries in SQL to explore your data, Athena is a serverless query service that runs your queries directly on data stored in S3.
• Amazon QuickSight: In the EDA phase for building BI dashboards, QuickSight provides interactive visualizations which help stakeholders to see data patterns.
• Amazon Redshift: Redshift for data warehousing provides quick access and analysis of large scale structured datasets.

Model Building and Training

AWS Deep Learning AMIs are pre-built EC2 instances that offer maximum flexibility and control over the training environment, preconfigured with Machine Learning tools.

Key Features

• Pre-installed ML Frameworks, optimized for TensorFlow, PyTorch, etc.
• Multiple versions of the Framework are available depending on the need for compatibility
• GPU-based configurations for superior training performance
• Root access for total customization of the environment
• Distributed training across multiple instances is supported
• Allow training through the use of spot instances, minimizing costs
• Pre-configured Jupyter Notebook servers for immediate use
• Conda environments for isolated package management
• Support for both CPU and GPU-based training workloads
• Regularly updated with the newest framework versions

Technical Capabilities

• Absolute control over training infrastructure and environment
• Installation and configuration of custom libraries
• Support for complex distributed training setups
• Ability to change system-level configurations
• AWS service integration through SDKs and CLI
• Support for custom Docker containers and orchestration
• Access to HPC instances
• Storage options are flexible, EBS/instance storage
• Network tuning for performance in multi-node training

Performance Optimization

• Profile the training workloads for bottleneck discovery
• Optimize the data loading and preprocessing pipelines
• Set the batch size properly concerning memory efficiency
• Perform mixed precision training wherever supported
• Apply gradient accumulation for adequately large batch training
• Consider model parallelism for extremely large models
• Optimize network configuration for distributed training

Pricing of AWS Deep Learning AMIs

AWS Deep Learning AMI are pre-built Amazon Machine Images configured for machine learning tasks with frameworks such as TensorFlow, PyTorch, and MXNet. However, there would be charges for the underlying EC2 instance type and for the time of use. 

For instance, an inf2.8xlarge instance would cost around $2.24 per hour, whereas a t3.micro instance is charged $0.07 per hour and is also eligible under the AWS Free tier. Instances of g4ad.4xlarge would see a price tag of about $1.12 per hour which is for in depth and large scale machine learning applications. Additional storage costs apply for EBS Volumes that go along with it.

Alternative Services for Model Building and Training

• Amazon SageMaker: Amazon’s flagship service to build, train, and deploy machine-learning models at scale, having built-in algorithms tuned for performance, automatic model-tuning capabilities, and an integrated development environment via SageMaker Studio.
• Amazon Bedrock: For generative AI applications, Bedrock acts as an access layer to foundation models from leading providers (Anthropic, AI21, Meta, etc.) via a simple API interface and with no infrastructure to deal with.
• EC2 Instances (P3, P4): For very IO-intensive deep learning workloads, come equipped with GPU-optimized instances, which can provide the highest performance for efficient model training.

Also Read: Top 10 Machine Learning Algorithms

Model Evaluation

The primary service for the Model Evaluation can be taken as Amazon CodeGuru. It executes program analysis and Machine Learning to assess ML code quality while searching for performance bottlenecks and recommending ways to improve them.

Key Features

• Automated code-quality assessment using ML-based insights
• Identification of performance issues and analysis of bottlenecks.
• Detecting security vulnerabilities in ML code
• Recommendations to reduce compute resource costs.
• Adding to popular development platforms and CI-CD processes.
• Monitoring application performance continuously in production
• Automated recommendations for code improvement
• Multi-language support, including Python
• Real-time anomaly detection based on performance
• Historical trend analysis of performance

Technical Capabilities of Amazon CodeGuru:

• Code review for potential issues.
• Runtime profiling for optimum performance
• Integration of our solution with AWS services for full scale monitoring.
• Automatic report generation which includes key insights.
• Custom metric tracking and alerting
• API Integration for programmatic access
• Support for containerized applications
• Integration of AWS Lambda and EC2 based applications.

Performance Optimization

• Offline and on-line evaluation strategies should be used.
• Cross validation should be used to determine the model stability.
• Testing out the model should include use of data which is different from that which was used for training.
• For evaluation we also look at business KPIs in addition to technical metrics.
• Explainability measures should be included with performance.
• For large model updates we may do an A/B test.
• Models transition into production based on defined criteria.

Pricing of Amazon CodeGuru

Amazon CodeGuru Reviewer offers a predictable repository size based pricing model. During the first 90 days, it offers a free tier, covering within a threshold of 100,000 loc, After 90 days, the monthly price is set for a standard rate of $10 USD per 100K lines for the first 100K lines and $30 USD for each next 100K lines on a per round-up basis.

An unlimited number of incremental reviews are included, along with two full scans per month, per repository. When more full scans are required, then you will be charged with the additional fees of $10 per 100K lines.Pricing done on the largest branch of each repository which does not include blank lines or lines with code comments. This model provides a straightforward mechanism for cost estimation and may save you 90% or more against the former pricing methods.

Alternative Services for Model Evaluation

• Amazon SageMaker Experiments: It provides tracking, comparing, and managing versions of models and experiments with parameters, metrics, and artifacts tracked automatically during training, along with visual comparison of model performance over multiple experiments.
• Amazon SageMaker Debugger: During training, Debugger monitors and debugs training jobs in real-time, capturing the state of the model at specified intervals and automatically detecting anomalies.

Deployment of ML Model

AWS Lambda supports serverless deployment of lightweight ML models and inherits the characteristics of automatic scaling and pay-per-use pricing, thereby making the service suited for unpredictable workloads.

Key Features

• Serverless for automatic scaling depending on load
• Pay-per-request price model allowing one to optimize costs
• Built-in high availability and fault tolerance
• Support of multiple runtime environments, including Python, Node.js, and Java
• Automatic load-balancing across multiple execution environments
• Works with API Gateway to create RESTful endpoints
• Accepts event-driven execution from a variety of AWS Services
• Built-in monitoring and logging via CloudWatch
• Supports containerized functions through Container Image
• VPC integration allows access to private resources in a secure manner

Technical Capabilities

• Cold start times of less than a second for the vast majority of runtime environments
• Concurrent execution scaling capacity with thousands of invocations
• Memory allocation from 128 MB to 10 GB, thus catering to the needs of varied workloads
• Timeout can reach a maximum of 15 minutes for every invocation
• Support for custom runtimes
• Trigger and destination integration with AWS Services
• Environment variables support for configuration
• Layers for sharing code and libraries across functions
• Provisioned concurrency to guarantee execution performance

Performance Optimization

• Decreasing the issue of cold starts by optimizing models.
• Provisioned concurrency is for when work is predictable.
• Load and cache models efficiently
• Optimize memory allocation concerning model constraints
• External services may benefit from connection reuse.
• Function performance should be profiled which in turn will identify bottlenecks.
• Optimize package size.

Pricing of Amazon SageMaker Hosting Services

Amazon SageMaker Hosting Services runs on pay-as-you-go provisioning, charging per second with extra fees for storage and transfer. For instance, it is around $0.115 per hour to host a model in an ml.m5.large, while almost $1.212 per hour for an ml.g5.xlarge instance. AWS allows SageMaker users to save money by committing to a certain amount of usage (dollar per hour) for one or three years.

Alternative Services for Deployment:

• Amazon SageMaker Hosting Services: This provides your fully managed solution for ML model deployments at scale for real-time inference, including auto-scaling capabilities, A/B testing through production variants, and multiple instance types.
• Amazon Elastic Kubernetes Service: When you have the need of higher control over your deployment infrastructure, EKS provides you with a managed Kubernetes service for container-based model deployments.
• Amazon Bedrock (API Deployment): For generative AI applications, Bedrock takes away the complexity of deployment by offering easy API access to foundation models without having to care about managing infrastructure.

Monitoring & Maintenance of ML Model

The process of Monitoring and maintaining an ML Model can be serviced by Amazon SageMaker Model Monitor services. It watches out for any change in the concepts of the deployed model by comparing its predictions to the training data and sounds an alarm whenever there is a deterioration in quality.

Key Features

• Automated data quality and concept drift detection
• Independent alert thresholds for different drift kinds
• Scheduled monitoring jobs with customizable frequency options
• Violation reports with comprehensive details and business use cases
• Good integration with CloudWatch metrics and alarms
• Allows both forms of monitoring- single and batch
• In-process change analysis for distribution changes
• Baseline creation from training datasets
• Drift metric visualization along a time axis
• Integration with SageMaker pipelines for automated retraining

Technical Capabilities

• Statistical tests for distribution shift detection
• Support for custom monitoring code and metrics
• Automatic constraint suggestion using training data
• Integration with Amazon SNS for alerting
• Data quality metric visualization
• Explainability monitoring for feature importance shifts
• Bias drift detection for fairness assessment
• Support for monitoring tabular data and unstructured data
• Integrating with AWS Security Hub for compliance monitoring

Performance Optimization of Amazon SageMaker Model Monitor

• Implement multi-tiered monitoring
• Define clear thresholds for interventions regarding drift magnitude
• Build a dashboard where stakeholders can get visibility on model health
• Develop playbooks for responding to different types of alerts
• Test model updates with a shadow mode
• Review performance regularly in addition to automated monitoring
• Track technical and business KPIs

Pricing of Amazon SageMaker Model Monitor

The pricing for the Amazon SageMaker Model monitor is variable, contingent on instance types and how long the jobs are monitored. For example, if you rent an ml.m5.large, the cost of $0.115 per hour for two monitoring jobs of 10 minutes each every day for the next 31 days, you will be roughly charged about $1.19. 

There may be additional charges incurred for compute and storage when baseline jobs are run to define monitoring parameters and when data capture for real-time endpoints or batch transform jobs are enabled. Choosing appropriate instance types in terms of cost and frequency would be key to managing and optimizing these costs

Alternative Services for Monitoring & Maintenance of ML Model:

• Amazon CloudWatch: It monitors the infrastructure and application-level metrics, offering a whole monitoring solution complete with custom dashboards and alerts.
• AWS CloudTrail: It records all API calls across your AWS infrastructure to track the usage and changes made to maintain security and compliance within your ML operations.

Summarization of AWS Services for ML:

Task	AWS Service	Reasoning
Data Collection	Amazon S3	Primary service mentioned for data collection – highly scalable, durable object storage that forms the building block for most ML workflows in AWS
Data Preparation	AWS Glue	Identified as the crucial service for data preparation, offers serverless ETL capabilities with visual job designer and automatic scaling for ML data preparation
Exploratory Data Analysis (EDA)	Amazon SageMaker Data Wrangler	Specifically mentioned for EDA – provides a visual interface with built-in visualizations, automatic outlier detection, and over 300 data transformations
Model Building/Training	AWS Deep Learning AMIs	Primary service highlighted for model building – pre-built EC2 instances with ML frameworks, offering maximum flexibility and control over the training environment
Model Evaluation	Amazon CodeGuru	Designated service for model evaluation – uses ML-based insights for code quality assessment, performance bottleneck identification, and improvement recommendations
Deployment	AWS Lambda	Featured service for ML model deployment – supports serverless deployment with automatic scaling, pay-per-use pricing, and built-in high availability
Monitoring & Maintenance	Amazon SageMaker Model Monitor	Specified service for monitoring deployed models – detects concept drift, data quality issues, and provides automated alerts for model performance degradation

Conclusion

AWS offers a robust suite of services that support the entire machine learning lifecycle, from development to deployment. Its scalable environment enables efficient engineering solutions while keeping pace with advances like generative AI, AutoML, and edge deployment. By leveraging AWS tools at each stage of the ML lifecycle, individuals and organizations can accelerate AI adoption, reduce complexity, and cut operational costs.

Whether you’re just starting out or optimizing existing workflows, AWS provides the infrastructure and tools to build impactful ML solutions that drive business value.




Gen AI Intern at Analytics Vidhya
Department of Computer Science, Vellore Institute of Technology, Vellore, India
I am currently working as a Gen AI Intern at Analytics Vidhya, where I contribute to innovative AI-driven solutions that empower businesses to leverage data effectively. As a final-year Computer Science student at Vellore Institute of Technology, I bring a solid foundation in software development, data analytics, and machine learning to my role.

Feel free to connect with me at [email protected]