Discover the various AWS Machine Learning Services and see how each one can help with your machine learning operations. Read more in SquareOps’ blog!
Have you ever wondered whether AWS is good for machine learning services?
AWS has been the standard for cloud computing for multiple years now, but it also comes with a comprehensive suite of ML tools and services. Developers have access to robust ML infrastructure, pre-trained models, and APIs.
AWS, therefore, equips developers and data scientists to build and deploy sophisticated ML applications without the complexities of managing underlying hardware and software.
Let’s take a look at the various AWS machine learning services that are available to scale up your operations rapidly. We’ll start with one of the core platforms that AWS offers, SageMaker.
SageMaker is Amazon’s fully managed ML platform that simplifies the entire ML development lifecycle. It simplifies interactive development environments for data exploration and model training and includes scalable training of ML models and the deployment of models as real-time or batch endpoints. SageMaker also offers centralized storage and management of ML models, as well as continuous tracking of model performance and drift detection.
Some of the other features include:
By taking away the complexities of infrastructure management, SageMaker enables developers to focus on building and refining ML models.
Next, let’s take a look at some of the text and image processing utilities in AWS machine learning services.
Amazon Comprehend is a sophisticated natural language processing (NLP) service designed to extract meaningful insights from text data.
Here are some of the key tasks it performs, along with real-world examples:
Comprehend can determine whether the sentiment in a text is positive, negative, or neutral.
Here’s the use case: Businesses use it to analyze customer reviews or social media posts to understand public perception of their products or services.
This helps identify key themes within large volumes of text. For instance, news agencies use Comprehend to automatically categorize articles into topics like politics, sports, or entertainment, making it easier to organize content.
Comprehend recognizes and classifies named entities like people, organizations, and locations in the text.
Example: a customer service company might use this to extract relevant details from emails or support tickets, such as customer names and locations, to improve service efficiency.
It extracts important keywords or phrases from text, helping to summarize documents or improve SEO.
For example, an e-commerce site can use this feature to identify key product features in customer reviews to improve search results and product descriptions.
Amazon Rekognition is a powerful computer vision service that uses advanced deep learning techniques to analyze images and videos.
By applying sophisticated algorithms to visual data, Rekognition can perform a wide range of tasks, including:
Uses: It’s used in visual search engines or for content moderation to ensure inappropriate images are flagged.
Uses: This is commonly used in security systems for access control or surveillance to compare and recognize faces in real-time.
Uses: This feature is used in document scanning, converting handwritten notes into editable text, or extracting information from images for further processing.
So, ML applications like the ones listed above can take up a significant amount of compute power on your instances and can be tricky to configure.
Why not use a virtual machine to help with some of the load? Learn about Amazon’s ML-oriented AMIs (Amazon Machine Images) below.
Amazon Deep Learning AMIs provide pre-configured virtual machine images optimized for deep learning workloads. These AMIs come pre-installed with popular deep learning frameworks like TensorFlow, PyTorch, MXNet, and Apache MXNet, along with the necessary libraries and tools.
The ultimate benefit? It eliminates the need for manual setup and configuration, allowing data scientists and ML engineers to quickly start building and training deep learning models.
A few key features of Deep Learning AMIs include:
This is a robust list of tools that are developed by AWS, but there are also some external ML utilities that come included with AWS.
Let’s take a look at a few of these now!
Apache MXNet on AWS is a highly scalable and flexible deep learning framework that supports a wide range of deep learning models, including:
It is known for its efficiency, scalability, and ease of use. Some features of Apache MXNet include:
PyTorch is a popular open-source ML framework primarily used for deep learning applications. It’s notable for its flexibility, ease of use, and strong community support. PyTorch can be run on AWS to simplify the process of building, training, and deploying ML models. It provides a seamless integration with the AWS ecosystem, offering a range of features and benefits for ML practitioners.
This is especially beneficial for a few reasons:
To use these utilities mentioned above, consider using AWS Deep Learning Containers that make short work of your containerization.
Similar to the Deep Learning AMI, AWS Deep Learning Containers provide pre-built Docker images with popular deep learning frameworks. These containers come pre-installed with the latest versions of frameworks, libraries, and tools, saving you time and effort in setting up development environments.
There are some advantages to using these:
Containers are designed for high performance, often leveraging hardware acceleration with GPUs or specialized hardware like TPUs.
For example, a healthcare company used AWS Deep Learning Containers to speed up their medical image processing, achieving faster training times for deep learning models that detect diseases from X-rays.
Deep Learning Containers ensure a consistent development and deployment environment, reducing the risk of compatibility issues. Teams can be confident that the code runs the same way in both development and production environments.
These containers can be easily deployed across multiple platforms, including cloud environments like AWS, GCP, and Azure. This flexibility allows you to quickly move applications between different cloud providers based on your needs or cost preferences.
The bottom line: AWS offers a wide range of machine learning services that cater to different use cases, from training models to deploying scalable applications. Let’s wrap it up.
As you can see, the intersection of ML and AWS comes with many resources and reasons to migrate your operations to the cloud.
Of course, if you need a helping hand in migrating your cloud operations, SquareOps has the expertise to ensure a smooth transition. We are an AWS-certified partner with over 20 certifications to date and ensure 24/7 uptime during your migration.
If you want to kickstart your machine learning services on the cloud, just schedule a demo with us, and we’ll take it from there!
Amazon uses various machine learning models across different services, including Deep Learning, Reinforcement Learning, and Natural Language Processing (NLP) models like BERT for language understanding and Convolutional Neural Networks (CNNs) for image recognition.
Amazon SageMaker is the primary AWS service for machine learning. It helps you build, train, and deploy machine learning models at scale.
Amazon SageMaker is a fully managed service that helps developers and data scientists build, train, and deploy machine learning models at scale. It simplifies the ML workflow with pre-built algorithms, tools for data preparation, and model deployment features.
Machine Learning (ML) in AWS refers to the use of AWS services and tools to build, train, and deploy machine learning models for a variety of use cases, such as predictive analytics, computer vision, and natural language processing.
ML as a Service (MLaaS) refers to cloud-based platforms that offer ready-to-use machine learning tools and services, allowing businesses to use machine learning without the need for specialized infrastructure or expertise.
AWS offers several MLaaS products like Amazon SageMaker, which helps users develop, train, and deploy models without managing the underlying infrastructure.
There are many types of machine learning models, but the main categories include: