Increasingly, people are opting to utilize the SageMaker Suite for custom models and internal development purposes. Join Caylent’s Randall Hunt as he breaks down the different services that make up SageMaker Suite
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As Caylent's VP of Customer Solutions, Mark leads a team that's entrusted with envisioning and proposing solutions to an infinite variety of client needs. He's passionate about helping clients transform and leverage AWS services to accelerate their objectives. He applies curiosity and a systems thinking mindset to find the optimal balance among technical and business requirements and constraints. His 20+ years of experience spans team leadership, technical sales, consulting, product development, cloud adoption, cloud native development, and enterprise-wide as well as line of business solution architecture and software development from Fortune 500s to startups. He recharges outdoors - you might find him and his wife climbing a rock, backpacking, hiking, or riding a bike up a road or down a mountain.
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Randall Hunt, VP of Cloud Strategy and Innovation at Caylent, is a technology leader, investor, and hands-on-keyboard coder based in Los Angeles, CA. Previously, Randall led software and developer relations teams at Facebook, SpaceX, AWS, MongoDB, and NASA. Randall spends most of his time listening to customers, building demos, writing blog posts, and mentoring junior engineers. Python and C++ are his favorite programming languages, but he begrudgingly admits that Javascript rules the world. Outside of work, Randall loves to read science fiction, advise startups, travel, and ski.
View Randall's articlesAs AI & ML adoption grows, builders are increasingly opting to utilize the Amazon SageMaker Suite to create custom models and accelerate internal development. The SageMaker Suite is more than just one service, it consists of a number of products that help you address everything from ideation and initial development all the way through production. It’s approachable from several different personas, from a low-code environment for business analysts to people who have some familiarity with the data science without experience with building model training inference pipelines.
Let’s break down some of the different services that make up the Amazon SageMaker Suite.
SageMaker Canvas is a no-code environment that uses an AutoML approach to accelerate initial model exploration and expand access to AI beyond data scientists. Many of our customers have seen success with this service as users with business domain knowledge are able to leverage Canvas to generate valuable insights quickly without requiring technical model expertise.
SageMaker Studio is a full integrated development environment (IDE) for ML. Within Studio, you have managed Jupyter Notebooks and the traditional machine learning data science tools that you would expect.
SageMaker Ground Truth allows you to create high quality data sets for model training by managing workflows with humans in the loop to label your data. Amazon SageMaker Ground Truth Synthetic Data goes beyond labeling existing data with its capacity for creating synthetic labeled data for computer vision models.
Edge Manager and SageMaker Neo will take models and quantize and recompile them for edge devices, increasing compute efficiency and expanding deployment options beyond the cloud.
SageMaker Model Governance and Model Cards
SageMaker Model Governance and Model Cards provide numerous capabilities for data governance and access control to help you ensure that your organization is using AI and ML responsibly and transparently.
SageMaker JumpStart accelerates machine learning projects with pre-trained models, templates, and workflows, including open-source models for different problem types. It offers a library of pre-built ML solutions for various industries, along with pre-built notebooks for tasks like fine-tuning and deployment, streamlining model development and deployment.
SageMaker Pipelines automates and organizes ML workflows, offering workflow automation for data preprocessing, model training, deployment and inference. It also provides a user-friendly visual interface, ensures reproducibility, and seamlessly integrates with AWS services, making it an ideal choice for organizations looking to scale ML projects with precision and reliability.
SageMaker is a broad suite of services that customers take advantage of in various ways. Different customers use different parts of SageMaker based on their unique business requirements. Most customers incrementally adopt individual services that align with their needs, within a modular architecture that avoids any lock-in.
The SageMaker Suite greatly reduces the amount of undifferentiated work for not just data scientists and machine learning architects, but also for business users seeking to generate insights.
We hope this provides you some insight into the different services within the Amazon SageMaker Suite. Are you exploring ways to take advantage of Analytical or Generative AI in your organization? Partnered with AWS, Caylent's data engineers have been implementing AI solutions extensively and are also helping businesses develop AI strategies that will generate real ROI. For some examples, take a look at our Generative AI offerings.
Mark Olson
Randall Hunt