Benefits of SageMaker

• Enhance the usability of ML

If the tools that are accessible to innovate with machine learning include integrated development environments for data scientists and visual interfaces that don’t require any coding for business analysts, then more people will be able to innovate with machine learning (ML).

• Prepare data on a large scale.

Both structured (table data) and unstructured (pictures, video, and audio) information must be gathered, categorized, and processed in order to aid in machine learning applications.

• Accelerate the emergence of novel machine learning approaches.

Training can be cut down from hours to minutes or even seconds with the right infrastructure. You can tenfold the productivity of your team using custom tools.

• Streamline the Machine Learning (ML) lifecycle.

Create, train, deploy, and manage models in a distributed environment at scale by standardising and automating MLOps procedures throughout your organisation.

By automating the labor-intensive aspects of each step in the machine learning process, SageMaker makes it faster and easier to build high-quality models. Using SageMaker’s machine learning toolbox, models may be developed and deployed much more quickly and cheaply than they would have been otherwise.

How it Works

Image sourced from Amazon Web Services

Amazon SageMaker Data Wrangler

Amazon SageMaker Data Wrangler reduces the time it takes to prepare data for machine learning (ML) from weeks to minutes. A single visual interface may be used to accomplish all of the work needed in data preparation and feature engineering, from selection through exploration and visualisation, using SageMaker Data Wrangler. SageMaker Data Wrangler’s data selection tool makes it easy to import data from multiple sources.No need to create any code to normalize, convert, or combine features using SageMaker Data Wrangler’s more than 300 built-in data transformations Amazon SageMaker Studio’s integrated development environment (IDE) makes it simple to evaluate and analyse these alterations to ensure that they are done exactly as you intended. Using Amazon SageMaker Pipelines, you can construct completely automated machine learning processes, which you can then save in the Amazon SageMaker Feature Store for future use.

• Quickly prepare data for ML

Choosing and querying data only takes a few mouse clicks.

As well as Amazon SageMaker Data Wrangler, AWS Lake Formation, and Amazon S3, Amazon Athena, Amazon Redshift, and AWS SageMaker Feature Store, AWS SageMaker Data Wrangler also provides data selection choices for AWS Lake Formation. A wide range of data sources, including CSV files, Parquet files, and database tables, can be accessed directly by SageMaker.

• Transform data quickly and easily

A single line of code is not required to change your data with SageMaker Data Wrangler’s 300+ pre-configured transformations. With a single click, you can convert a text field column to a numeric column in Python, SQL, and Pandas.

• Visualize your data to learn more

Pre-configured visualisation templates are available in SageMaker Data Wrangler to help you better comprehend your data. Every type of graph, from histograms to bar charts, can be found here. To develop and modify visualisations, you don’t need to write any code.

• Get ML model accuracy quickly

Diagnostics based on ML data prepared more quickly

SageMaker Data Wrangler assists you in detecting discrepancies in your data preparation routines before models are sent into production. You may rapidly examine the veracity of the data you’ve provided and determine whether additional feature engineering is required to boost performance.

• One click from prep to production

Streamline the pre-processing of ML data

With a single click, you may save your data preparation technique as a notebook or script. SageMaker Data Wrangler integrates with Amazon SageMaker Pipelines to facilitate model deployment and management. The Amazon SageMaker Feature Store publishes features that your team can use in their own models and analyses, which in turn makes them available to other teams.

Features

• Transparency

In order to increase model quality, Amazon SageMaker Clarify automatically detects biases during data preparation and after training. SageMaker Clarify’s model explainability reports help stakeholders better understand how and why models make predictions.

• Protection and Confidentiality

Amazon SageMaker provides a completely secure machine learning environment from the start, making it easy to get up and running quickly. Security elements can be used to ensure compliance with a wide range of industrial laws.

• Data Marking and Labeling

Create accurate training datasets with Amazon SageMaker Ground Truth Plus without having to build or manage labelling teams on-premises. Amazon SageMaker Ground Truth Plus provides a skilled workforce and a centralized database to control the workflows.

• Featured Retailer

Amazon SageMaker Feature Store provides ML features in both real-time and batch modes through its ML feature set. During the training and inference phases, features can be safely stored, discovered, and shared. This saves a lot of time during development.

• Amazon’s Data Processing on a Massive Scale SageMaker

Simplified data processing is now possible because to the scalability, dependability, and ease of use of SageMaker. SageMaker Processing allows you to connect to an existing storage system, provision resources for your job’s execution, store output to a persistent storage location, and collect logs and metrics, all of which are possible.

• Machine Learning with No Coding

Businesses can use Amazon SageMaker Canvas instead of writing code or having prior understanding of machine learning to develop machine learning models and make accurate predictions. You can also publish your results, explain and analyse your models, and share your models with others using this software.

• Free Machine Learning Environment

Amazon AWS’s SageMaker Studio Lab offers a no-configuration free development environment for building machine learning models. An open-source Jupyter Notebook linked to GitHub is included with Amazon SageMaker Studio Lab, along with 15 GB of dedicated storage.

• Creating Jupyter Notebooks is as simple as clicking a button.

Using Amazon SageMaker Studio Notebooks, you have one-click access to Jupyter notebooks and can quickly scale up or down the computing resources that are available. Your colleagues can access the same notebook that you have saved in the same spot when you share it with a single click.

• Algorithms Pre-installed

With over 15 built-in algorithms, Amazon SageMaker delivers a wide range of pre-built container images that may be used to train and do inference.

• Both pre-built and open-source models are options.

It’s easy to get started with machine learning using Amazon SageMaker JumpStart, which uses pre-built solutions that can be deployed in a few clicks. With a single click, more than 150 widely used open-source models may be deployed and fine-tuned.

• AutoML

Automated machine learning models are trained and tuned using your own data, all under your total and complete control. Next, you have the option to either publish the model to the production environment with a single click or work on it to improve its accuracy.

• Optimized for the most popular frameworks

Many of the most popular deep learning frameworks are supported by Amazon SageMaker, including TensorFlow, Apache MXNet, PyTorch, and more. Using the most recent version, frameworks are constantly up-to-date and optimized for AWS performance. As long as you’re using the built-in containers, these frameworks don’t require any further configuration.

• Local Mode

Amazon SageMaker makes it feasible to conduct local testing and prototyping. From the GitHub repository, you can get Apache MXNet and TensorFlow Docker images used in SageMaker for your own usage. You can use the Python SDK and these containers to test scripts before deploying them to training or hosting environments.