6 AWS machine learning services you should be using

Your app just made a sale ... before the customer even realized they needed the product.

How? It analyzed browsing habits, predicted demand, and personalized the experience—all in real time.

That's not luck. That's machine learning (ML) in action.

Now, imagine that same intelligence detecting fraud, powering chatbots, or automating workflows. The possibilities are huge.

And that’s why ML isn’t just another tool—it’s the foundation of today’s smartest technology.

With businesses prioritizing real-time personalization, automation, and AI-driven decisions, ML is no longer optional for developers. If you want to build scalable, adaptive applications, now’s the time to dive in.

The good news is that getting into ML doesn’t have to be complicated. AWS has tools that let you fine-tune models or drop in prebuilt AI features—so you can focus on building smarter, faster, and more scalable apps without getting stuck in the weeds.

Here’s what we’ll cover in today’s newsletter:

• AWS ML stack explained: Tools for every stage, from plug-and-play to advanced frameworks.

• 6 AWS ML services you need to know: Like Rekognition, SageMaker, and Bedrock.

• Real-world use cases: See how ML can transform your apps and workflows.

Onward!

Overview: The AWS machine learning stack#

The AWS ML stack is structured into the following three levels, making it easy to find the right tools for different stages of the ML process.

• Applications: These are pretrained, ready-to-use ML models for tasks like image recognition, text analysis, and translation. Ideal for users with little to no ML expertise, these services make it easy to add AI capabilities to common use cases.

• Platforms: These tools simplify building, training, and deploying custom ML models. Designed for users with some ML experience, they streamline the model development life cycle, allowing users to focus on creating tailored ML solutions all within one environment.

• Frameworks and infrastructure: These are high-performance compute resources for advanced ML workloads. They are designed for experts who need granular control over specialized hardware to provide the flexibility and power required for large-scale training and deployment, enabling users to optimize performance while effectively managing costs.

AWS ML stack empowers everyone—from beginners integrating basic AI to experts scaling custom solutions—to unlock new opportunities for innovation and drive meaningful growth in their businesses.

Now let's explore some of the key ML services AWS offers—and what makes them worth knowing.

6 AWS ML services every dev should know#

AWS offers a wide range of machine learning services that suit a variety of applications, from natural language processing to big data analytics.

Let’s take a quick look at each of these popular services individually.

1. Amazon Rekognition#

Amazon Rekognition is an easy-to-use cloud service that adds image and video analysis to applications using deep learning technology. With Rekognition, users can detect and analyze objects, text, faces, and scenes without needing machine learning experience.

Rekognition integrates smoothly with other AWS services, making it scalable and secure for handling large volumes of visual data.

For example, social media platforms often face challenges monitoring and regulating the huge volume of user-generated content to meet community guidelines. Amazon Rekognition can automate this process by analyzing images and videos uploaded by users to detect inappropriate or prohibited content that violates platform policy.

2. Amazon Polly#

Amazon Polly is a cloud service that turns text into natural-sounding speech. Using advanced machine learning, it offers various voices in multiple languages, making it easy for developers to create engaging applications with fast speech output.

Polly is a valuable resource for developing an eLearning platform with a global reach. For example, eLearning platforms can offer audio versions of lectures, articles, and textbooks, allowing learners to listen to content on the go, benefiting from its multilingual support and high-quality speech synthesis.

3. Amazon Lex V2#

Amazon Lex V2 uses advanced speech recognition and natural language understanding to facilitate the building of intelligent, interactive voice- or text-based bots that integrate seamlessly across web, mobile, and messaging platforms.

Leveraging the same deep learning technologies as Amazon Alexa, Lex is an excellent tool for building chatbots and virtual assistants to enhance customer engagement.

For example, e-commerce companies can use a Lex-powered chatbot to help customers with orders, product details, and returns, engaging human agents only when necessary. This not only improves satisfaction but also reduces operational costs.

4. Amazon SageMaker#

Amazon SageMaker is a fully managed ML service that supports every phase of ML workflow, from data preparation to deployment. Its integrated IDE, SageMaker Studio, offers an end-to-end development experience with built-in support for distributed training, automatic model tuning, and model monitoring.

SageMaker Studio provides a wide range of tools to perform ML development steps:

• Data preparation: Use Data Wrangler to quickly clean, transform, and explore data sets for training.

• Model development: SageMaker provides fully managed notebooks for interactive code development. We can also leverage pretrained models using SageMaker JumpStart.

• Detect bias: SageMaker Clarify helps detect bias in the data and identify steps to remediate the bias.

• Model training: SageMaker Training service efficiently trains an ML model by handling infrastructure complexities and distributing workloads to achieve optimized cost and faster outcomes.

• Model evaluation and monitoring: We can use SageMaker Model Monitor for continuous performance tracking and automated alerts. Additionally, SageMaker Ground Truth allows one to incorporate human-in-the-loop feedback into the ML life cycle to improve model accuracy and relevancy.

SageMaker’s modular design and native support for popular ML frameworks and programming languages make it a preferred choice in enterprise and academic research for creating flexible and scalable ML solutions.

5. Amazon Bedrock#

Amazon Bedrock is a managed service that provides access to top foundation models through a single API, making it easy to build and customize generative AI applications without managing the infrastructure.

Bedrock is designed to accelerate the use of AI across industries, enabling organizations to integrate sophisticated language models without starting from scratch.

For instance, a marketing firm can utilize Bedrock to quickly build an AI model to generate personalized ads and social media posts, helping to engage specific audiences and improve customer interaction.

6. Amazon EMR#

Amazon EMR (Elastic MapReduce) is a big data service that allows users to process massive datasets using frameworks like Apache Spark and Hadoop. It is ideal for machine learning and data analytics projects that need to process data on a large scale because it offers a controlled environment that makes managing big data less complicated.

EMR also supports a serverless option, which allows data engineers and analysts to run applications developed with open-source big data frameworks without having to set up and maintain clusters.

Here's an example: Retail companies can leverage Amazon EMR to process and analyze large datasets to gain insights into purchasing trends and optimize stock levels. For example, they can identify peak shopping times and popular products by analyzing transaction data, leading to better staffing and inventory decisions.

2 functional AWS developer tools#

In addition to the full-fledged ML services AWS provides, the platform also offers 2 important developer tools that help create, train, and deploy ML models.

Let's take a look.

1. AWS Deep Learning AMIs#

AWS Deep Learning AMI (DLAMI) is preconfigured with popular frameworks like TensorFlow, PyTorch, Hugging Face, Apache MXNet, and more. These AMIs simplify launching deep learning models on Amazon EC2 instances, providing an efficient setup for experimentation and development.

DLAMIs are available for Ubuntu (16 and 18) and Amazon Linux (1 and 2), and you can select Conda AMIs with preinstalled frameworks or base AMIs for complete customization.

2. AWS Deep Learning Containers#

AWS Deep Learning Containers offer prebuilt, optimized Docker environments for various deep learning frameworks, providing seamless integration with AWS services such as SageMaker, ECS, EKS, and EC2. These containers are regularly updated with the latest framework versions and security patches and optimized for different AWS hardware, including GPU and CPU-based instances.

Deep Learning Containers are ideal for users looking to accelerate machine learning development without the need to manage dependencies and security patches. They are highly popular among data scientists and ML engineers for prototyping, scalable training, and inference workloads.

Get more out of AWS: Tools and tips for developers#

AWS offers a comprehensive suite of machine learning tools and infrastructure to help developers build smarter systems and scale faster. Here’s how to get the most out of it:

• Accelerate with prebuilt solutions: Use Amazon Rekognition, Polly, and Textract to quickly add ML capabilities to your applications.

• Scale with the right infrastructure: Utilize Amazon EMR and Deep Learning Containers to scale efficiently with SageMaker.

• ML model development: Use SageMaker Studio to accelerate AI development, streamline collaboration, and optimize model performance all in one integrated environment.

• Harness the power of generative AI: Use Amazon Bedrock to build and deploy scalable, tailored applications without the complexity of managing infrastructure.

With AWS ML services, you can build, train, and deploy models that solve real-world challenges and make smarter decisions—faster.

Build smarter with AWS ML tools#

AWS ML tools like SageMaker, Bedrock, Rekognition, and Polly don’t just simplify machine learning—they empower developers to solve real problems, faster.

Whether you’re scaling a system, enhancing user experiences, or building something entirely new, AWS provides the infrastructure to make it happen.

Machine learning is one skill that sets developers apart in today’s industry. Learning how to use AWS ML services is a great step towards leveling up your apps, workflows, and career.

Resources to develop your AWS skills#

Hands-on practice is essential to mastering AWS skills. Check out this hands-on Cloud Lab to learn and manage machine learning services.

There's no AWS sign-up or setup involved, so you don’t need to worry about service charges or bills. (Educative handles all that for you.)