It’s a wrap for Amazon SageMaker Month, 30 days of content, discussions, and news
Did you miss SageMaker Month? Don’t look any further than this round-up post to get caught up. In this post, we share key highlights and learning materials to accelerate your machine learning (ML) innovation.
On April 20, 2021, we launched the first ever Amazon SageMaker Month, 30 days of hands-on workshops, tech talks, Twitch sessions, blog posts, and playbooks. Our goal with SageMaker Month was to connect you with AWS experts, getting started resources, workshops, and learning content to be successful with ML. The following is a summary of what you can access on-demand to get started on your ML journey with Amazon SageMaker.
Introducing SageMaker Savings Plans
To kick off SageMaker month, we introduced Amazon SageMaker Savings Plans, a flexible, usage-based pricing model for SageMaker. The goal of SageMaker Savings Plans is to offer you the flexibility to save up to 64% on SageMaker ML instance usage in exchange for a commitment of consistent usage for a 1-year or 3-year term. In addition, to help you save even more, we announced a price drop on SageMaker CPU and GPU instances.
To enable customers to save more on SageMaker, we hosted a SageMaker Friday Twitch session with Greg Coquillo, the second-most influential speaker according to LinkedIn Top Voices 2020: Data Science & AI, along with Julien Simon and Segolene Dessertine-Panhard outlining cost-optimization techniques using SageMaker and SageMaker Savings Plans.
SageMaker Savings Plans enhance the productivity and cost-optimizing capabilities already available in Amazon SageMaker Studio, which can improve your data science team’s productivity up to 10 times. Studio provides a single visual interface where you can perform all your ML development steps. Studio also gives you complete access, control, and visibility into each step required to build, train, and deploy models. To enable your teams to move faster and boost productivity, learn how to customize your Studio notebooks.
Getting started with ML
SageMaker is the most comprehensive ML service, purpose-built for every step of the ML development lifecycle. SageMaker provides all the components used for ML in a single service, so you can prepare data and build, train, and deploy models.
Data preparation is the first step of building an ML model. It’s a time-consuming and involved process that is largely undifferentiated. We hear from our customers that it constitutes up to 80% of their time during ML development. Data preparation has always been considered tedious and resource intensive, due to the inherent nature of data being “dirty” and not ready for ML in its raw form. “Dirty” data could include missing or erroneous values, outliers, and more. Feature engineering is often needed to transform the inputs to deliver more accurate and efficient ML models. To help with feature engineering, Amazon SageMaker Feature Store offers a purpose-built repository to store, update, retrieve, and share ML features within development teams.
Another challenge with data preparation is that it often requires multiple steps. Although most standalone data preparation tools provide data transformation, feature engineering, and visualization, few tools provide built-in model validation. And all of these data preparation steps are considered separate from ML. What’s needed is a framework that provides all these capabilities in one place and is tightly integrated with the rest of the ML pipeline. Most standalone tools for data preparation treat it as an extract, transform, and load (ETL) workload, making it tedious to iteratively prepare data, validate the model on test datasets, deploy it in production, and go back to ingesting new data sources and performing additional feature engineering. Most iterative data preparation is divorced from deployment. Therefore, data preparation modules need curation and integration before they’re deployed in production. These practices in ML are sometimes referred to as MLOps.
To help you overcome these challenges, you can use Amazon SageMaker Data Wrangler, a capability to simplify the process of data preparation, feature engineering, and each step of the data preparation workflow, including data selection, cleansing, and exploration on a single visual interface. As part of SageMaker Month, we created a step-by-step tutorial on how you can prepare data for ML with Data Wrangler. In addition, you can learn how financial customers use SageMaker every day to predict credit risk and approve loans. This example uses Data Wrangler and Amazon SageMaker Clarify to detect bias during the data preparation stage.
Another part of the data preparation stage is labeling data. Data labeling is the task of identifying objects in raw data, such as text, images, and videos, and tagging them with labels that help your ML model make accurate predictions and estimations. For example, in an autonomous vehicle use case, Light Detection and Ranging (LIDAR) devices are commonly used to capture and generate a three-dimensional point cloud data, which is an understanding of the physical space at a single point in time. For this use case, you need to label your data captured both in 2D and 3D spaces to produce highly accurate predictions of vehicles, lanes, and pedestrians. Amazon SageMaker Ground Truth, a fully managed data labeling service, makes it easy to build highly accurate training datasets for ML in 2D and 3D spaces using custom or built-in data labeling workflows. To help you label your data, we created how-to blog posts to showcase how to annotate 3D point cloud data and automate data labeling workflows for an autonomous vehicle use case with Ground Truth.
After you built your ML model, you must train and tune it to achieve the highest accuracy. Improving a model’s performance is an experimental and iterative process. For SageMaker Month, we consolidated a few techniques and best practices on how to train and tune high-quality deep learning models with complete visibility using SageMaker.
When you’re satisfied with your model’s accuracy, understanding how to deploy and manage models at scale is key. For model deployment and management, we showcase an example where an application developer is using SageMaker multi-model endpoints to host thousands of models and pipelines to automate retraining to improve recommendations across different US cities.
When it’s time to deploy your model and make predictions, a process called inference, you can use SageMaker for inference in the cloud or on edge devices. Amazon SageMaker Neo automatically compiles ML models for any ML framework and any target hardware. A Neo compiled model can speed up YOLOv4 inference to twice as fast. You can also reduce ML inference costs on SageMaker with hardware and software acceleration.
As part of SageMaker Month, we also launched an example use case that shows how you can use Amazon SageMaker Edge Manager, a capability to optimize, secure, monitor, and maintain ML models on fleets of smart cameras, robots, personal computers, and mobile devices. This blog outlines how to manage and monitor models on edge devices such as wind turbines.
Finally, to bring all our SageMaker capabilities together and help you move from model ideation to production, we created an on-demand introduction to SageMaker workshop similar to the virtual hands-on workshops we conducted live and during recent AWS Summits. It includes everything you need to get started with SageMaker at your own pace.
ML through our Partners
As part of SageMaker Month, we partnered with Tableau and DOMO to empower data and business analysts with ML-powered insights without needing any ML expertise. With the right data readily available, you can use ML and business intelligence (BI) tools to help make predictions needed to automate and speed up critical business processes and workflows.
We partnered with DOMO to enable ML for everyone with SageMaker. Domo AutoML, powered by Amazon SageMaker Autopilot, provides insights to complex business problems and automates the end-to-end decision-making process. This helps organizations improve decision-making and adapt faster to business changes.
We also partnered with Tableau to create a blog post and tech talk that showcases an end-to-end demo and new Quick Start solution that makes it easy for data analysts to use ML models deployed on SageMaker directly in their Tableau dashboards without writing any custom integration code.
SageMaker Month focused on cost savings and optimization, getting started with ML, and learning content to accelerate ML innovation. As we wrap up SageMaker Month, we’re excited to share the upcoming and first ever virtual AWS Machine Learning Summit on June 2, 2021. The summit brings together industry-leading scientists, AWS customers, and experts to dive deep into the art, science, and impact of ML. Attend for free, learn about features over 30 sessions, and interact with leaders in a live Q&A.
About the Author
Shashank Murthy is a Senior Product Marketing Manager with AWS Machine Learning. His goal is to make it easy for customers to build, train, and deploy machine learning models using Amazon SageMaker. For fun outside work, Shashank likes to hike the Pacific Northwest, play soccer, and run obstacle course races.