AWS foundations: How Amazon SageMaker can help?

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.

SageMaker Studio

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.

Data preparation

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.

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

• SageMaker Data Wrangler: It offers simplified processing and feature engineering of data. It can be efficiently used to combine multiple features to get data insights for preparation. Furthermore, it is helpful in detecting anomalies in data. Data Wrangler helps us explore, cleanse, and visualize data on a single web interface.

• EMR clusters: 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 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.

Model training

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 training are:

• AutoML: 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.

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

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

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 deployments, and rolling updates without disrupting the serving of predictions.

• 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 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.

Test yourself

Solve the quiz given below to test if you'd choose the right feature of Sagemaker.

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.