AWS foundations: How Amazon SageMaker can help?

Key takeaways:

  • Amazon SageMaker is a fully managed service that helps build, train, and deploy machine learning models.

  • SageMaker Studio offers a visual interface with pre-built models (e.g., Hugging Face, TensorFlow) and MLOps tools to streamline ML processes.

  • SageMaker's data preparation tools like SageMaker Data Wrangler, EMR Clusters, and SageMaker Feature Store simplify data preprocessing and feature engineering.

  • Model training supports built-in tools like AutoML, Jumpstart, SageMaker Canvas, and SageMaker Pipelines allow easy model building, fine-tuning, and automation of model development.

  • SageMaker's Model deployment and management features like endpoints, model versioning, and Model Monitor facilitate real-time inference, version control, and monitoring of models.

Amazon SageMaker is a fully managed machine learning service offered by AWS. It allows us to build, train, and deploy machine learning models using tools such as notebooks, debuggers, profilers, CI/CD, and more, all in one place.

Machine learning engineers, data scientists, and business analysts commonly use Amazon SageMaker for research, development, and predictions. This Answer will discuss some of the ways SageMaker helps developers and scientists.

Machine Learning is the last invention that humanity will ever need to make. —Nick Bostrom.

Let’s understand the features of ML features that SageMaker provides.

Data preparation with Amazon SageMaker

Often, the data used for machine learning is in raw format, which requires preprocessing and preparation to be utilized for machine learning. SageMaker allows us to easily load data stored on multiple AWS services such as S3 buckets, DynamoDB tables, Redshift clusters, and more. Also, it offers various features to efficiently process the data.

Loading data from various AWS services
Loading data from various AWS services

Here are some of the ways SageMaker helps in data preparation and processing:

  • SageMaker Data Wrangler: It offers simplified data processing and feature engineering and efficiently combines multiple features to get data insights for preparation. Furthermore, it helps detect anomalies in data. Data Wrangler helps us explore, cleanse, and visualize data on a single web interface.

  • SageMaker Feature Store: Features are inputs for the machine learning algorithms. SageMaker offers a fully managed feature repository to store and manage features for machine learning models. Through Data Wrangler, we can directly store features on the feature store. However, they can be loaded from other AWS services such as AWS Lake Formation, Snowflake, S3, and more.

  • GeoSpatial ML with SageMaker: It is a tool for preparing geospatial data like satellite images, GPS data, and spatial datasets for machine learning. It provides access to multiple open-access geospatial data sources like Landsat and Sentinel-2. It also provides several geospatial data operations, making it easy to transform our custom geospatial dataset, like correcting inaccurate GPS data to actual streets and roads with a map-matching feature. Moreover, it provides data visualization tools that can be used to analyze ML predictions.

Model training with Amazon SageMaker

Amazon SageMaker offers an integration development environment for running, debugging, and iterating through the code. Furthermore, it provides a variety of built-in machine learning algorithms, such as linear learner, XGBoost, and more. These algorithms can be used to perform basic tasks or serve as building blocks for more complex algorithms.

Some features helpful in model building are:

  • SageMaker Notebooks: It provides a fully managed, scalable, and integrated IDE for writing codes, accessing SageMaker’s built-in ML models, and accessing data in other AWS services.

  • SageMaker Jumpstart: It offers the most commonly used pertained algorithms to start with machine learning. Developers can build upon these models, fine-tune them, or use these for evaluation and inference in simple use cases.

  • SageMaker Studio Lab: Amazon SageMaker Studio Lab is a free web application for learning and experimenting with data science and machine learning. It supports multiple tools, including Jupyter Notebooks, Python, R, data visualization, Git, machine learning frameworks, and other open-source packages.

  • SageMaker model training: Amazon SageMaker Model Training offers scalable, time and cost-efficient training and tuning of machine learning (ML) models at scale without the need to manage infrastructure. It provides the highest-performing ML computing infrastructure currently available, and since we pay only for what we use, it can be very cost-effective.

Model deployment and management

SageMaker helps in model deployment and management in many ways. Let’s discuss some of these.

  • Endpoints: SageMaker offers an option to host a model over an endpoint. This endpoint can be used for real-time inference using the trained machine-learning models.

  • Model versioning: SageMaker keeps track of model versions, allowing you to deploy multiple model versions simultaneously. This enables A/B testing, canary deployment, and rolling updates without disrupting the prediction serving.

  • Model Monitor: SageMaker Model Monitor allows us to monitor real-time endpoints and batch transform jobs. Additionally, we can set up notifications for any irregular behaviors and take action. Model monitors offer monitoring of data quality, model quality, bias in the model’s predictions, and any drift in feature attribution.

  • Model management: SageMaker provides a centralized location to manage all aspects of your machine learning models, including training data, model artifacts, and deployment configurations. This simplifies model governance and allows for easy collaboration among team members.

End-to-end ML with Amazon SageMaker

End-to-end machine learning (ML) with Amazon SageMaker involves the full life cycle of an ML project, from data preparation to model deployment, all within a fully managed environment. Let’s discuss some tools that provide end-to-end ML support with Amazon SageMaker.

  • SageMaker Canvas: It is another service that allows us to utilize pretrained models for inference without writing code. We can also build and use our model on custom datasets in SageMaker Canvas.

    • SageMaker AutoPilot: It picks up the finest algorithms and tunes them based on our data with complete visibility of the progress. We can pick up the best-performing models to deploy in one click and boost our productivity.

Inference using SageMaker Canvas
Inference using SageMaker Canvas
  • SageMaker Studio is a complete integrated development environment that provides a visual web-based interface to access various tools for preparing datasets, building and training models, and deploying them. It offers multiple IDEs such as Visual Studio Code, Rstudio, Jupyter Notebooks, and more. With SageMaker, we can quickly upload data sets, tune models, experiment, collaborate, and deploy machine learning models.

It provides us access to prebuilt popular machine learning models such as Hugging FaceIt is a popular platform and open-source library for natural language processing (NLP) tasks, particularly known for its extensive collection of pre-trained machine learning models., TensorFlowTensorFlow is an open-source machine learning framework developed by Google Brain for building and training various types of machine learning models, including deep learning models. , and Stability AIStability Ai offers open source models in generative AI for video generation. . Additionally, it offers purposeful tools for multiple ML operations to help us automate and standardize processes. Using MLOps tools, we can easily train, test, troubleshoot, deploy, and govern our machine learning models. It also supports automated resource tagging, which helps administrators track machine learning costs. SageMaker Studio is integrated with EMR Clusters by default. Therefore, developers can perform large-scale data preparation and training from their notebooks. Moreover, SageMaker Studio allows us to visualize the EMR jobs using Spark UI.

  • SageMaker Pipelines: They can automate the entire development process of a machine learning model, from data preprocessing to model and management. This is particularly helpful in managing and standardizing work practices among individuals across an organization.

Take the first step toward deploying an ML model in AWS SageMaker—explore the “Deploying a Machine Learning Model with Amazon SageMaker” cloud lab now!

Test yourself

Solve the quiz given below to test if you’d choose the right feature of SageMaker.

SageMaker foundations quiz

1

Which feature of SageMaker should we use to transform data for machine learning workflows?

A)

SageMaker Model Monitor

B)

SageMaker Data Wrangler

C)

SageMaker Canvas

D)

SageMaker Feature Store

Question 1 of 30 attempted

Conclusion

SageMaker provides a comprehensive suite of services to simplify data preparation, model training, and model deployment and management. We can leverage these services to exponentially increase our productivity.

Frequently asked questions

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How does SageMaker pricing work?

SageMaker’s pricing depends on the size of the underlying infrastructure you use and the time period you use it for. The details of pricing can be found on the SageMaker’s pricing web page.


How do I get started with SageMaker quickly?

Amazon SageMaker Jumpstart provides pre-built solutions, models, and notebooks to help users quickly start with machine learning (ML) projects. It offers access to open-source models, AWS-trained models, and end-to-end solutions for common use cases like fraud detection, demand forecasting, and personalization. Jumpstart simplifies the process of model deployment, fine-tuning, and experimentation, making it ideal for users who want to accelerate their ML projects with minimal setup.


What are the key features of AWS SageMaker?

Key features include SageMaker Studio (an IDE for ML), support for popular ML frameworks (like TensorFlow and PyTorch), model training and tuning, automated machine learning (AutoML), real-time model deployment, and monitoring. It also offers SageMaker Experiments for tracking model performance and SageMaker Jumpstart for pre-built models and solutions.


How does Amazon SageMaker benefit data scientists and developers?

SageMaker reduces the complexity of setting up ML infrastructure, making it easier for data scientists and developers to focus on model development and deployment. It also supports collaboration, rapid experimentation, and scaling without requiring deep infrastructure knowledge.


What machine learning frameworks does SageMaker support?

SageMaker supports several popular ML frameworks, including TensorFlow, PyTorch, scikit-learn, Apache MXNet, and XGBoost. It also offers built-in algorithms and the flexibility to bring custom frameworks and algorithms.


How does SageMaker compare to other ML platforms?

SageMaker stands out for its fully managed environment, integration with other AWS services, and end-to-end support for the entire ML life cycle. It simplifies tasks like data labeling, hyperparameter tuning, and model deployment, making it more accessible than many other platforms.


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