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AWS SageMaker

Make machine learning (ML) models for every use case then train and deploy them in a secure environment with fully managed infrastructure, tools, and processes.

Benefits of SageMaker


  • Enhance the usability of ML

ML tools that include integrated development environments for data scientists as well as no-code visual interfaces for business analysts will enable more people to innovate with machine learning (ML).

  • Prepare data on a large scale.

To support machine learning, enormous amounts of structured data (tabular data) and unstructured data (pictures, video, and audio) must be accessed, labelled, and processed.

  • Increase the speed with which machine learning is developed

With optimized infrastructure, you can reduce training time from hours to minutes or even seconds. With purpose-built tools, you may increase team productivity by up to tenfold.

  • Streamline the Machine Learning (ML) lifecycle.

Make MLOps methods consistent and automated across your organization to create, train, deploy, and manage models in a distributed environment at scale.

In order to make it easier to construct high-quality models, SageMaker automates the heavy lifting associated with each phase of the machine learning process. SageMaker combines all of the components required for machine learning into a single toolset, allowing models to be put into production more faster and with significantly less work and expense.

How it Works

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Amazon SageMaker Data Wrangler

When preparing data for machine learning (ML), Amazon SageMaker Data Wrangler cuts the time it takes from weeks to minutes. Simplify the process of data preparation and feature engineering by using SageMaker Data Wrangler’s single visual interface to perform all of the work involved in data preparation from selection to exploration to visualization. Importing data from several sources can be done with a single click using SageMaker Data Wrangler’s data selection tool. There are more than 300 built-in data transformations in SageMaker Data Wrangler, so you don’t have to write any code to normalize, convert, or combine features. With Amazon SageMaker Studio, the first machine learning (ML) IDE with an integrated development environment (IDE), you can easily preview and analyze these changes to ensure that they are done as you intended. Once your data is ready, you may use Amazon SageMaker Pipelines to create fully automated ML processes, which you can then save in the Amazon SageMaker Feature Store for future use.

  • Quickly prepare data for ML

Just a few clicks to choose and query data

AWS Lake Formation, Amazon S3, Amazon Athena, Amazon Redshift, Amazon SageMaker Feature Store, and Amazon SageMaker Data Wrangler all have data selection tools in SageMaker Data Wrangler. You can also query data sources and import data from CSV files, Parquet files, and database tables right into SageMaker.

  • Transform data quickly and easily

With SageMaker Data Wrangler’s 300+ pre-configured data transformations, you can easily turn your data into model-friendly formats without writing a single line of code. In PySpark, SQL, and Pandas, you may turn a text field column into a numeric column with a single click.

  • Visualize your data to learn more

SageMaker Data Wrangler provides a set of robust pre-configured visualization templates to help you understand your data. All histograms, scatter plots, line plots, and bar charts are available. Using templates like the histogram, you can create and change visualizations without writing code.

  • Get ML model accuracy quickly

Faster ML data preparation diagnosis

Prior to deploying models into production, SageMaker Data Wrangler helps you detect discrepancies in your data preparation routine. You can quickly assess the accuracy of your supplied data and evaluate if extra feature engineering is required to increase performance.

  • One click from prep to production

Streamline ML data prep workflows

Export your data preparation procedure to a notebook or code script in one click. SageMaker Data Wrangler automates model deployment and management by integrating with Amazon SageMaker Pipelines. In addition, it publishes features to the Amazon SageMaker Feature Store so your team can reuse them for their own models and analyses.


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  • Transparency

Amazon SageMaker Clarify delivers data to improve model quality by detecting biases during data preparation and after training, and it does it automatically. Model explainability reports are also provided by SageMaker Clarify, allowing stakeholders to understand how and why models produce predictions.

  • Protection and Confidentiality

Amazon SageMaker enables you to operate in a fully secure machine learning environment from the very beginning. When it comes to complying with a wide range of industrial laws, you can employ a complete set of security features.

  • Data Marking and Labeling

Amazon SageMaker Ground Truth Plus simplifies the process of creating correct training datasets without the need to develop labelling apps or manage labelling workforces on-premises. The data is uploaded and Amazon SageMaker Ground Truth Plus controls the workflows by providing an expert workforce and a centralized database.

  • Featured Retailer

Amazon SageMaker Feature Store is a feature store designed specifically for machine learning (ML) that serves features in both real-time and batch mode. The ability to securely store, discover, and share features means that you will constantly receive the same features throughout both the training phase and the inference phase, saving months of development time.

  • Amazon’s Data Processing on a Massive Scale SageMaker 

Processing expands the simplicity of use, scalability, and dependability of SageMaker to data processing tasks that are currently executing. Connection to existing storage, provisioning of the resources required to run your job, saving of the output to a persistent storage location, as well as logs and metrics, are all possible with SageMaker Processing.

  • Machine Learning with No Coding

ML models and accurate predictions may be generated by business analysts using Amazon SageMaker Canvas, a visual, point-and-click solution that eliminates the need to write code or have ML experience. Additionally, you can simply publish results, explain and analyse models, and share models with others using this software.

  • Free Machine Learning Environment

SageMaker Studio Lab is a free, no-configuration development environment for building and training machine learning models on Amazon AWS. In addition to an open-source Jupyter Notebook that’s integrated with GitHub, Amazon SageMaker Studio Lab provides you with 15 GB of dedicated storage.

  • Jupyter Notebooks can be created with a single click.

Amazon SageMaker Studio Notebooks are one-click Jupyter notebooks, and the underlying computing resources are entirely elastic, allowing you to simply scale up or down the amount of available compute resources as required. Notebooks may be shared with a simple click, ensuring that all of your colleagues have access to the same notebook, saved in the same location.

  • Algorithms Pre-installed

As well as over 15 built-in algorithms that are available in pre-built container images and can be used to quickly train and run inference, Amazon SageMaker also provides a number of pre-built container images.

  • Pre-built solutions as well as open-source models are both available.

Using pre-built solutions that can be deployed with a few clicks, Amazon SageMaker JumpStart allows you to get started with machine learning quickly and efficiently. It also facilitates the deployment and fine-tuning of more than 150 widely used open-source models with a single click.

  • AutoML

Using your data, AutoML Amazon SageMaker Autopilot generates, trains, and tunes the best machine learning models, allowing you to maintain complete control and visibility. After that, you can either publish the model to production with a single click or iterate on it to increase the model’s accuracy.

  • Optimized for the most popular frameworks

Amazon SageMaker is tailored for a wide range of popular deep learning frameworks, including TensorFlow, Apache MXNet, PyTorch, and many others. Frameworks are always up to date with the most recent version, and they are optimized for performance on AWS to ensure maximum efficiency. You do not need to explicitly configure these frameworks, and you may use them within the built-in containers without any further effort.

  • Local Mode

Local testing and prototyping are made possible by Amazon SageMaker. It is possible to download the Apache MXNet and TensorFlow Docker containers that were used in SageMaker from the GitHub project. To test scripts before deploying them to training or hosting environments, you can download these containers and use the Python SDK.

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  • Amazon’s Reinforcement Learning System 

In addition to typical supervised and unsupervised learning, SageMaker now offers reinforcement learning. SageMaker offers built-in, fully-managed reinforcement learning algorithms, including some of the most recent and best-performing algorithms available in the academic literature.

  • Management and tracking of experimental results

Amazon When working with machine learning models, SageMaker Experiments can assist you in tracking iterations by collecting the input parameters, configurations, and results, and storing them as ‘experiments’. In SageMaker Studio, you can browse through active experiments, search for prior experiments, review previous experiments and their outcomes, and compare experiment findings. SageMaker Studio also allows you to create custom experiments.

  • Runs for debugging and profiling are included.

Using the Amazon SageMaker Debugger, you can gather metrics and profile training jobs in real time, allowing you to identify and correct performance issues before the model is launched into production.

  • Spot training under supervision

Amazon SageMaker offers Managed Spot Training, which can help you cut training expenditures by up to 90 percent by utilising existing resources. Training jobs are automatically executed as soon as additional compute capacity becomes available, and they are designed to be resilient to disruptions caused by changes in compute resource availability.

  • Adaptive Model Tuning on an Automated Basis

As a result of tweaking millions of combinations of algorithm parameters, Amazon SageMaker can automatically modify your model, resulting in more accurate predictions than you could achieve manually, saving you weeks of time and effort. To swiftly tune your model, automatic model tuning use machine learning techniques.

  • Compiler for Training

Because of enhancements at the graph and kernel levels that enable more efficient use of GPUs, the Amazon SageMaker Training Compiler can accelerate training by up to 50%. It is integrated with SageMaker’s implementations of TensorFlow and PyTorch, allowing you to accelerate training in these popular frameworks while still maintaining compatibility.

  • Training with a single click

When it’s time to train, simply describe the location of the data and the type of SageMaker instances to use, and you’ll be up and running in a matter of seconds. SageMaker creates a distributed compute cluster, runs the training, sends the results to Amazon S3, and then decommissions the cluster and starts over.

  • Distributed Training

Amazon dispersed training is made easier with SageMaker. SageMaker enables you distribute your data across numerous GPUs in order to achieve near-linear scaling efficiency.. SageMaker also helps you distribute your model across many GPUs by automatically profiling and splitting your model with less than ten lines of code.

  • CI/CD

Amazon SageMaker Pipelines automates the whole machine learning (ML) lifecycle, including data preparation, model training, and deployment.

  • Models Must Be Constantly Monitored

Using Amazon SageMaker Model Monitor, you can keep track of model quality issues and be notified as soon as they arise so you can take action to correct them. SageMaker automatically emits essential metrics, which can be collected and seen in SageMaker Studio for all models trained using the software.

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    • Inferences based on serverless architecture

    Using Amazon SageMaker Serverless Inference (preview) enables you to install ML models on a pay-per-use pricing basis without having to worry about servers or clusters. Scaling and shutting down compute capacity is done automatically using Amazon SageMaker.

    • Inference Adviser

    SageMaker Inference Recommender helps you determine the optimum deployment configuration and execute load tests to optimise inference performance at the lowest possible cost without the need for custom testing infrastructure.. Your model can either be deployed to one of the suggested instances, or a fully managed load test can be carried out on any of the instance types you choose.

    • Incorporation of Kubernetes

    For orchestration and pipeline management, you can combine Amazon SageMaker with Kubernetes and Kubeflow. Models can be trained and deployed in SageMaker using Kubernetes operators and can be used without having to manage Kubernetes for ML utilizing components for Kubeflow pipes from SageMaker.

    • The Endpoints of Multiple Models

    Using Amazon SageMaker, you can run a large number of bespoke machine learning models at a low cost and with no effort. Multiple models can be deployed and served from a single SageMaker endpoint utilizing SageMaker Multi-Model endpoints.

    •Inference Pipelines

    For real-time and batch inference, you can use Amazon SageMaker’s Inference Pipelines to transfer raw input data to and process it in real time. By using Inference Pipelines, you are able to create feature-data processing and feature-engineering pipelines and distribute them.

    •Any Device Can Run Models

    Using Amazon SageMaker Neo, you can train and deploy ML models in the cloud or on the edge all at once. Machine Learning (ML) is used by SageMaker Neo to improve the performance of a trained model by up to twice as much and to consume less than one-tenth of the memory required.

    •Edge Devices: Operating Models and Their Applications

    Amazon SageMaker Edge Manager makes it simple to monitor and manage models that are operating on edge devices, thanks to its cloud-based architecture. In order to continuously enhance model quality, SageMaker Edge Manager collects data from devices and transfers it securely to the cloud, where it is monitored, labelled, and retained by experts.

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