AWS SageMaker is a fully-managed service that allows developers and data scientists to build, train, and deploy machine learning models. The claims made about the platform are impressive: a ten times increase in team productivity, a 54% lower TCO, a 40% reduction in data labeling costs, and the ability to train models up to 50% faster through more efficient use of GPUs, not to mention the ability to make over 1 trillion predictions per month.
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Does AWS SageMaker really deliver on these promises? We decided to do some research and see if the hype was justified.
While it’s hard to verify the exact numbers quoted above, it does seem like SageMaker has the potential to be a game-changer for machine learning teams. The fully-managed service takes care of a lot of the tedious work that can bog down data scientists, such as infrastructure management and scaling, allowing you to focus on more critical tasks. So is it worth trying out SageMaker? Let’s find out!
6 Must-know facts about AWS SageMaker
What Is AWS SageMaker: Explained
Some everyday use cases for AWS SageMaker include things like predictive modeling. In other words, SageMaker can help you use historical data to make predictions about future events.
This allows you to create exciting models to personalize user recommendations based on past interactions. For example, a streaming service might use AWS SageMaker to build a recommendation system that suggests new movies or TV shows to users based on their viewing history.
Given SageMaker’s integration with other AWS services, you can also use NLP, or natural language processing, to analyze and process text data. You can build complex classification models for use in web apps. This can be valuable for building models that classify inputs from clients or users.
As a fully-managed machine learning service, SageMaker makes it a breeze for data scientists and developers to construct and fine-tune models and then effortlessly deploy them into a production-ready environment. Plus, with a built-in Jupyter notebook for easy access to data sources and analysis, you can dive into your projects without any hassle.
SageMaker also has optimized, standard machine-learning algorithms that can handle vast amounts of data and run smoothly in a distributed setting. You can deploy your model in a secure, scalable space without hassle via the SageMaker Studio or console. Let’s go over some of SageMaker’s primary components and explore why you might want to spend time on each one.
SageMaker Studio
SageMaker Studio is an integrated machine-learning environment that allows you to build, train, deploy, and analyze your models all in the same application. It provides a single, web-based UI for working with your SageMaker resources, including notebooks, models, and data sets.
SageMaker Studio allows you to write and run code using Jupyter notebooks. These are interactive documents that mix code, text, and other media. SageMaker Experiments and SageMaker Debugger provide additional tools for visualizing and analyzing data. Monitoring your models in real-time with Debugger is particularly valuable for spotting issues before they become problematic.
You’ll also find many machine learning algorithms and frameworks, such as TensorFlow, PyTorch, and sci-kit-learn. SageMaker supports all of the most popular frameworks out of the box. This way, you can start on your project quickly without reinventing your application’s fundamental building blocks.
SageMaker Autopilot
AWS SageMaker Autopilot is an automated machine-learning service that allows users to build and deploy machine-learning models. All without the need for coding or data science expertise. It uses a simple, drag-and-drop interface called SageMaker Studio Canvas to make it easy for users to create models and make predictions.
SageMaker Autopilot trains and tunes a range of machine-learning models on your data and selects the best-performing model based on your evaluation metric. Once you have a trained and tuned model, you can deploy it to a production environment and use it to make predictions.
SageMaker Autopilot is a good option for users who want to build machine learning models but don’t have coding or data science expertise. As a “low code” solution, you still need a bit of technical expertise to put everything together. However, the friendlier interface is more welcoming to newcomers.
SageMaker Data Wrangler
AWS SageMaker Data Wrangler is another similar feature that allows you to import, analyze, prepare, and “featurize” data for machine learning. It provides a simple visual interface that allows you to perform everyday data preparation tasks without writing code and also allows you to integrate custom Python scripts and transformations to customize your data prep workflow.
Finally, Data Wrangler lets you prepare your data for machine learning by cleaning and transforming it. On top of that, it will handle missing values and outliers, and generate a handy quality report to show you the results.
How to Use AWS SageMaker
You might be thinking: SageMaker sounds incredible! So how do you use it? Let’s break down the basics.
To use AWS SageMaker, you’ll need to create an AWS account. This is the easy part. You can create an account by visiting the AWS website and following the prompts. Once you have an AWS account, you can set up a SageMaker environment. This process involves creating an IAM role and a SageMaker notebook instance.
Start exploring the SageMaker interface after setting up your SageMaker environment. You can manage SageMaker resources through SageMaker Studio and run code with a notebook instance using Jupyter.
Preparing your data is the first step before you train a model. This process involves collecting and cleaning your data and storing it in a SageMaker-compatible format.
Once your data is prepared, you can train a model using SageMaker. This involves selecting an algorithm or framework, configuring your training parameters, and launching the training job.
After training your model, you’ll want to evaluate its performance to ensure that it is accurate and effective. SageMaker provides a range of tools and metrics for evaluating models.
When satisfied with your model’s performance, you can deploy it to a production environment where it can be used to make predictions or take other actions. After deploying, you’ll want to monitor your model’s performance.
Make updates as needed to ensure it continues to perform well. As mentioned earlier, SageMaker provides plenty of tools for monitoring deployed models and updating things.
How to Learn AWS SageMaker
Using SageMaker probably won’t come naturally to everyone, but it will be a breeze if you have a technical background. Since SageMaker packs features that take advantage of a vast range of technologies, you would have to spend a lot of time studying to explore everything. But it’s easy to jump in and start tinkering if you’re just looking to dip your toes in the water.
The AWS SageMaker documentation is a comprehensive resource that covers all aspects of using SageMaker. It includes detailed instructions, tutorials, and code examples. You’ll find hundreds of pages to dig into for juicy information. However, tackling the documentation head-on is often not the best way to learn something new.
Instead, you should focus on building your project and consulting the documentation when you need help. Follow a video or text-based tutorial to help you get off on the right foot. Once you have a solid framework in place, you can search for specific bugs or issues in the documentation or on websites like Stack Overflow.
AWS SageMaker: When Is it Not the Best Choice?
While SageMaker deserves a badge of honor for giving its users total control over their machine-learning models, it might not be the best choice in every situation. The elephant in the room is flexibility. SageMaker falls flat when it comes to flexibility since you are constrained to the AWS ecosystem. In other words, you cannot use SageMaker separately from other platforms.
If your team or organization already uses AWS, SageMaker is an obvious choice. But it might not be worth setting up an AWS strictly for SageMaker’s capabilities due to the cost and restricted environment.
For those looking for alternative services, you’ll find wildly varying options. It’s hard to beat SageMaker, as it offers a unique blend of features and integration support, but you can still find competitive alternatives. Let’s look at two of the most popular alternatives to SageMaker to give you an idea.
Kubeflow
Kubeflow is designed to be portable and run on any infrastructure, including on-premises, cloud, and hybrid environments. This can be useful if you want to use the same machine-learning pipeline across different environments or if you want to avoid vendor lock-in.
Like SageMaker, Kubeflow allows you to customize your machine-learning workflow using open-source tools and frameworks, such as TensorFlow, PyTorch, and others. The most significant advantage is that Kubeflow is an open-source project. This means you can access the source code and contribute to the platform’s development.
MLflow
Like Kubeflow, MLflow is fully open-source and portable. You can run it alongside many popular tools and frameworks. Popular programming languages like Python, R, Java, and others can run alongside your machine-learning library of choice. As a result, MLflow is an excellent choice if you want a versatile service that won’t keep you tied down.
Despite being open-source, MLflow can still scale to support large organizations. With companies like Microsoft, DataBricks, R Studio, and the University of Washington contributing to the project, it has a solid foundation and support network.
