With the increasing adoption of cloud-based machine learning solutions, Amazon SageMaker has gained popularity in recent years. It provides more than twenty features that you can use separately or in combination with others for building machine learning (ML) applications. If you look at the Amazon SageMaker landing page, you will find it hard to decide on which features to use for your machine learning tasks.

This article introduces you to the different features of Amazon SageMaker and how you can use them. For this, the article discusses the problems that each feature in SageMaker tries to solve and how you can use them in your machine-learning projects.

What is Amazon SageMaker?

Amazon SageMaker is a managed machine learning platform for building ML applications. It provides a set of tools that we can use to create pipelines for data processing, train, test, deploy, and monitor machine learning models. SageMaker provides dedicated features to perform any and every task in a machine learning project lifecycle. 

Features of Amazon SageMaker

Amazon SageMaker has more than twenty features that you can use in ML tasks. These features perform one or more tasks in the machine learning lifecycle and you can use them separately or in combination to create ML applications. Let us discuss each feature one by one to understand their use cases.

Automatic Model Tuning

SageMaker’s Automatic Model Tuning feature is designed to help machine learning engineers and data scientists fine-tune the ML models efficiently. 

While training machine learning models, we need to tune the hyperparameters of the model to obtain the best results. For example, if we are training a decision tree model such as a random forest or gradient boosting tree to create a machine learning application for classifying loan applications, we need to specify the number of decision trees and the depth of the decision trees in the model. 

To obtain the best-performing model, we specify multiple values for the hyperparameters. Then, we need to train the machine learning model on each possible set of hyperparameters to obtain the best results. Retraining and evaluating the models is a resource-intensive task and requires a lot of time and computation.

• SageMaker Automatic Model Tuning feature helps you identify the best parameters for any machine learning model by running multiple training jobs parallelly. For this, you just need to specify all the hyperparameters and the metric you want to optimize. Amazon SageMaker trains the models with each set of hyperparameters and evaluates them for the most optimized results.
• Training models with each set of hyperparameters and evaluating them for performance is costly in terms of time and resources. SageMaker Automatic Model Tuning feature uses an early stopping feature to save computation time and costs. Using the previous training results, the early stop feature predicts if a particular hyperparameter might lead to the best results. If not, it pre-empts the execution saving time and money.
• SageMaker Automatic Model Tuning feature also helps us minimize the training time by selecting the best hyperparameters for the model at the earliest. It uses hyperparameter tuning methods such as the Bayesian search method and the Hyperband method. These methods help find the best hyperparameters in the shortest possible time using the information on how the machine learning model behaves with respect to other hyperparameter values while tuning.
• SageMaker’s Automatic tuning feature is integrated with SageMaker Jumpstart. This helps you tune your machine-learning models in a single click without worrying about the internal details while creating prototypes or base ML models using Jumpstart. 
• Automatic model tuning is also integrated with SageMaker Autopilot. This helps you find the best version of a model using hyperparameter optimization mode while creating ML models using SageMaker Autopilot.

Amazon SageMaker Autopilot

Training a machine learning model requires several steps such as data preprocessing, feature engineering, algorithm selection, model training, model evaluation, hyperparameter tuning, etc. To perform these tasks manually, you need to have a thorough understanding of machine learning concepts. Hence, only experienced data scientists and machine-learning engineers can create ML models by executing all these steps manually.

SageMaker Autopilot is a complete AutoML tool that you can use to build machine-learning models with tabular data without knowing anything about machine-learning processes. 

With Sagmaker Autopilot, you just need to provide a tabular dataset and specify the target column for prediction or classification tasks. SageMaker Autopilot automatically finds and trains the best ML model that you can directly deploy into production. Thus, a software engineer, a project manager, or a business analyst having tabular data available can use SageMaker Autopilot to train machine learning models without any knowledge about how machine learning works. 

• SageMaker Autopilot automatically identifies the type of machine learning problem we need to perform and trains the ML models by selecting the best algorithm based on the input data. Thus, if you are a data analyst or a software engineer who has no idea about ML algorithms, you can use Autopilot to create machine learning models with your data and generate the required results.
• After training the models, Autopilot creates a dashboard for all the models with different metrics such as accuracy, recall, precision, etc. You can review all the metrics and select the best model for deployment according to your use case.
• SageMaker Autopilot also provides you with the feature importance for each machine learning model using SageMaker Clarify. You can use the feature importances to analyze how each feature impacts the output result.

SageMaker Canvas

Canvas is a no-code feature that you can use to utilize ready-to-use models for tasks like classification, regression, sentiment analysis, object detection, forecasting, etc. If you are a project manager, a data analyst, or a business analyst who doesn’t know how to code, you can use SageMaker Canvas for creating machine learning applications.

• Canvas provides you with ready-to-use models for sentiment analysis, object detection, regression, and classification tasks. To get started, you can upload your data, such as text, images, or documents, and select a ready-to-use model to generate predictions with a single click.
• Using Canvas, you can also create custom models for classification, regression, forecasting, sentiment analysis, object detection, etc. SageMaker Canvas provides a visual, point-and-click interface for creating ML models and generating predictions using any input data.
• Data cleaning is important for any machine learning or data science project. Canvas provides built-in features for exploratory data analysis. This helps you prepare, explore, analyze, and clean your data before performing machine learning tasks.
• SageMaker Canvas also helps you collaborate easily with your teammates. You can share your Canvas models with teammates using SageMaker Studio. The teammates and review and update the models and you can again use them for predictions. 

Amazon SageMaker Clarify

Clarify is designed for data scientists and machine learning engineers to detect, analyze, and rectify bias in ML models. When we train machine learning models, they don’t always produce accurate results. The deviation of predictions from expected results might be a result of bias in training data, the live prediction data, or the ML model itself. In such situations, SageMaker Clarify can help you identify the bias in the data or the machine learning model so that you can correct it.

• SageMaker Clarify analyzes the input data and generates a visual report with a description of the metrics and measurements of potential bias. You can analyze the report and identify steps to remediate the bias. After this, you can use SageMaker Data Wrangler to perform operations such as undersampling, oversampling, or SMOTE to rebalance the data. 
• In a machine learning application, it is also possible that the trained ML model is biased. To identify potential bias in trained ML models, you can run bias analysis using SageMaker Clarify.
• Bias can also be introduced in deployed ML models if the live prediction data differs significantly from the training data. For example, consider that you are building a machine learning application for house price prediction with mortgage rates as an input feature. Now, mortgage rates are always changing. Hence, if the mortgage rates in the prediction data vary significantly compared to the training data, the predictions can be biased. In such situations, you can integrate Clarify with SageMaker Model Monitor to analyze metrics when bias occurs beyond a certain threshold.
• You can also integrate Clarify with SageMaker Experiments to identify feature importances and partial dependence plots to identify which features contribute most to the predictions. Clarify can also provide scores detailing which features contribute the most to the model’s prediction if the model is run on new unseen data.

Data Wrangler

Data wrangling is one of the most time-consuming tasks in the lifecycle of a machine learning project. To produce good results with ML models, the data scientists need to spend a significant amount of time on data transformation and statistical analysis of the training data. 

Amazon SageMaker Data Wrangler is designed to help data scientists transform data and make it ready for ML tasks within minutes. 

• The SageMaker Data wrangler provides a single visual interface using which data scientists and ML engineers can perform data selection, data cleaning, analysis, visualization, and transformation at scale. It provides a set of more than 300 transformations that you can use to clean and transform your data.
• SageMaker Data Wrangler also provides data quality and insights reports. You can use the reports to verify data quality issues such as duplicate rows, missing values, and incorrect data types for columns. It also helps detect anomalies such as class imbalance, outliers, and data leakage. After verifying the data quality, you can quickly train ML models using the transformed dataset. 
• You can also use scatter plots, histograms, box plots, and other charts provided in the Data Wrangler to understand your data in a better manner. Data Wrangler also provides visualizations such as bias reports, multicollinearity, feature correlation, target leakage, etc. to analyze the data in a better manner while creating machine learning applications.
• For tabular data, SageMaker Data Wrangler provides transformations with which you can flatten JSON files, delete duplicate rows, impute missing data, perform encoding, and apply time-series–specific transformations. For image data, SageMaker Data Wrangler provides in-built operations like blurring, enhancement, resizing, etc. Apart from in-built transformation, you can also create custom transformations using PySpark, OpenCV, Pandas, SQL, and imaging libraries. 
• After transforming data using Data Wrangler, you can use SageMaker Autopilot to build ML models if you aren’t very much into machine learning. Otherwise, you can use SageMaker Model Training to train ML models manually. You can also deploy the data transformation workflow using the SageMaker Data Wrangler UI or export it as a notebook to use later.

SageMaker Debugger

Training machine learning models is a resource-intensive and time-consuming process. If a trained ML model doesn’t provide accurate results, we are forced to discard it and all the time and resources utilized while training the model go to waste. SageMaker Debugger helps you to minimize this loss by real-time monitoring of training metrics and system resources while training ML models. If you are a data scientist or an ML engineer, you can use SageMaker Debugger to optimize resource usage by monitoring training metrics and taking preemptive measures if you detect anomalies while training.

• With SageMaker Debugger, you can easily identify errors like data sampling errors, gradient values becoming too large or too small, and out-of-bound value errors at the earliest possible time. Once you detect these errors, you can preempt the process and restart model training. This helps you save resources as well as time.
• The Debugger also monitors the utilization of hardware resources such as RAM and GPU. It analyzes the model training process to collect detailed metrics that can help us identify bottlenecks. You can access the metrics and download detailed reports created by Debugger using SageMaker Studio.
• SageMaker Debugger also has built-in analytics that analyze inputs, outputs, and transformations while training ML models. Hence, if the training process runs into overfitting or overtraining or when hardware resources are underutilized, you can easily identify the anomalies and take preventive measures to save resources. You can also automate preempting the training jobs based on certain defined conditions using AWS Lambda.

SageMaker Model Deployment

Deploying machine learning models to production is a critical task. The operational difficulties in deploying models are such that most of the data science and machine learning projects are delayed due to problems in deployment only.  To avoid this, SageMaker Model Deployment provides many infrastructure and deployment options that data science teams can use to deploy the ML models seamlessly. 

• SageMaker Model deployment provides inference options for almost every use case for machine learning models. Whether you want to deploy models for live recommendation or offline batch inference, SageMaker Deployment provides options for all the use cases.
  • If you are building low-latency and high-throughput applications for live recommendation systems for e-commerce websites or digital ad recommendations, you can use real-time inference.
  • For low latency and high throughput use cases that encounter intermittent traffic patterns, you can use serverless inference for ML model deployment.
  • For ML applications with low latency and large payloads of up to 1 GB or long processing times of up to 15 minutes, you can use Asynchronous Inference.
  • For creating machine learning applications with offline inference on data batches for ML applications with large datasets, you can use the Batch Transform inference option.
• SageMaker Deployment also provides cost-effective and scalable deployment types for getting machine learning models into production.
  • For deploying a single model on a container hosted on dedicated instances for low latency and high throughput use cases, SageMaker Deployment provides single-model endpoints.
  • It also provides multiple-model endpoints for deploying multiple machine-learning models that share a single container hosted on dedicated instances.
  • If you have multiple models that use different frameworks like Tensorflow, PyTorch, Scikit-learn, Hugging Face, etc., you can also use multi-container endpoints to deploy multiple containers that share dedicated instances for models using different frameworks.
  • For cases where you need to run multiple machine learning models in a sequence, you can use serial inference pipelines to deploy multiple containers that share dedicated instances and execute in a sequence.

SageMaker Deployment also provides an inference recommender that we can use to choose the best available infrastructure and configuration to deploy machine learning models. It helps us achieve optimal inference performance for the machine learning models and save infrastructure costs for the deployed model.

Distributed Training Libraries

For deep learning tasks such as object detection in images or natural language processing, model training becomes a bottleneck in the project due to high resource and time consumption. To expedite this process, SageMaker provides us with model parallelism and data parallelism libraries that help us train deep learning models within the shortest possible time. To understand this, consider the following examples.

• ML engineers and data scientists often try to improve the performance of deep learning models by increasing the size of the neural network and the number of layers in it. This results in models with billions of parameters as in models like GPT-3, GPT-4, and Falcon. For instance, Falcon has 40 billion parameters and it is trained on one trillion tokens. In such cases, it can take weeks to split the model layers and operations across GPUs. 
• Object detection models and computer vision applications are trained on image and visual data that are huge in size. We need to train models with thousands of GBs of data to improve its capabilities.

SageMaker provides us with data parallelism libraries that help us split the training data and quickly scale to thousands of GPUs. It helps reduce the training time from days to minutes. 

Similarly, SageMaker also provides model parallelism libraries that automatically analyze and split the model efficiently to enable us to train large deep-learning models within minutes.

Amazon SageMaker Edge

With advancements in the Internet of Things (IoT), edge devices like routers, routing switches, firewalls, security cameras, sensors, etc. have also started harnessing machine learning capabilities in applications like intrusion detection and facial recognition. However, these devices need to make low-latency decisions and process data in milliseconds using limited hardware resources. 

For this, SageMaker Edge helps ML engineers optimize the trained machine-learning models so that we can deploy them on any edge device. 

• The SageMaker Edge compiler compiles the trained machine-learning model and packages it into an executable format that can be deployed on EDGE devices. It also applies performance optimizations that can make the deployed models run up to 25 times faster on the target EDGE devices.
• Amazon SageMaker Edge Manager also provides a dashboard to monitor the performance of ML models running on each EDGE device where the models are deployed. This helps us visually understand overall system health and identify the problematic models or devices through a dashboard in the console. If we identify any problem, we can analyze the cause, take corrective steps, and deploy the corrected models on the affected devices.
• Additionally, The SageMaker Edge Agent allows us to collect and store data and metadata using custom triggers. This helps us retrain existing machine learning models with live production data or build new models with ease. We can also use this data to conduct analysis for model drift, data drift, etc.

SageMaker Experiments

Machine learning applications aren’t built in a single iteration. We need to train the models, evaluate their performance, and retrain the models iteratively to generate the best output model. For each iteration, we need to store the parameters, metrics, and executable files to troubleshoot and reproduce models. This is a tedious task and cannot be done manually. 

SageMaker Experiments is a managed service to help data scientists and ML engineers track and analyze machine learning experiments.

• While training models using SageMaker Training, SageMaker Autopilot, SageMaker pipelines, or even Notebooks in other IDEs, you can use SageMaker Experiments to log the metrics, parameters, and executable files. As all the data is stored in a centralized repository, you can access and analyze data for any model.
• You can use SageMaker Studio to analyze the data from the ML experiments visually. SageMaker Studio also allows the team members in a machine learning or data science team to access the same information. This makes collaboration easier and makes sure that the experiment results are consistent.
• You can also use store-trained machine learning models using SageMaker Experiments to reproduce the training and testing results while auditing the experiments. This helps you to analyze the cause of changes occurring in an ML model if it starts behaving differently after deployment.

SageMaker Feature Store

While training machine learning models, we perform data processing to create features from existing data. These features are used to train the machine learning models. If we don’t store the calculated features, we need to execute the entire data processing pipeline again to calculate the features if we want to reuse them. This leads to monetary costs as well as delays in model training. To avoid this, we use feature stores. A feature store helps us manage datasets and feature pipelines which speed up tasks in a data science project. It also helps avoid the repetitive task of calculating the same features multiple times. 

SageMaker Feature Store helps data scientists and ML engineers create a fully managed feature store to store, manage, and share features for ML models.

• We can use SageMaker Feature Store as a standalone service or integrate it with other SageMaker services across the project lifecycle to manage features in the data.
• We can use SageMaker Data Wrangler and publish the calculated features directly into SageMaker Feature Store. Then, we can use the features for model training or inference. You can also browse the feature store using SageMaker Studio.

We can use SageMaker Feature Store’s offline storage and online storage for training and real-time inference respectively. However, it is hard to keep the offline storage and online storage in sync.

With SageMaker Feature Store, we can process, standardize, and use features at scale across the machine learning lifecycle. It helps us keep the offline and online datasets in sync. This is critical because if the online and offline feature stores diverge, it can negatively impact model accuracy.

SageMaker Geospatial ML

There are many use cases where we need to process geospatial data to create machine-learning applications. SageMaker GeoSpatial ML helps us build machine learning models using geospatial data by providing us with large-scale geospatial datasets and pre-trained models.

• You can use SageMaker Geospatial ML for creating machine learning applications for monitoring climate change, and deforestation, measuring gas emissions, creating climate resiliency plans, managing disaster response, improving power grid reliability, etc. 
• With SageMaker GeoSpatial ML, you can directly access satellite imagery, maps, or location data, transform them, and use them in your ML applications. 

Amazon SageMaker Data Labeling (Ground Truth)

To train machine learning models, we need correctly labeled datasets that we can rely upon. Consider the following scenarios.

• You want to build an enterprise-level text classification or tagging model and you don’t have labeled texts. How would you proceed? Even if you have raw text data from sources like complaints, reviews, and social media posts and comments, you need to label them to build a good text classification or tagging application. 
• Similarly, if you want to create an object detection model but don’t have labeled images that you can use for input, you need to label the data first to create an ML application that can detect and track objects.

To solve the data labeling problem, SageMaker provides us with two services namely Ground Truth and Ground Truth Plus.

• SageMaker Ground Truth is a self-service feature offered by Amazon for data labeling tasks. It helps you label images, text files, and videos for building ML systems. You can also generate synthetic data to create datasets for training machine learning models using this feature. 
• While using Ground Truth, you can control the data labeling workflow and use your own human annotators or third-party services for data labeling. However, finding an expert workforce for data labeling can also be a tedious task. Using Ground Truth requires some expertise and it can only be used by machine learning engineers and data scientists and a trained team of people responsible for data labeling.
• Sagemaer also provides Ground Truth Plus. This is a fully managed service. Thus, whether you are a data Scientist, an ML Engineer, a Data Operations Manager, a software engineer, a project manager, or a Program Manager, you can use Ground Truth Plus. It allows you to create high-quality datasets without the need to build labeling applications or manage labeling workforces on your own. SageMaker provides an expert workforce trained on data labeling tasks for machine learning applications. It helps us label the data correctly with all the security, privacy, and compliance requirements. 
• Ground Truth Plus also provides services for generating high-quality synthetic datasets to fine-tune foundation models for generative AI tasks. It also provides a skilled workforce to review model outputs. This helps us ensure that the synthetic datasets are aligned with human preferences. This helps you generate high-quality data for any and every machine-learning task.

Amazon SageMaker Jumpstart

SageMaker Jumpstart is designed to accelerate the machine-learning project lifecycle. If you want to try machine learning in your current application, and you aren’t sure whether it’s a good idea, you can use SageMaker Jumpstart to build and test prototypes for your machine learning applications.

• Jumpstart provides pre-trained foundation models for tasks like text summarization, image generation, text classification, etc. You can select the models and customize them for your use case with your data. After training the models, you can easily deploy them with a few clicks and check their performance. 
• SageMaker JumpStart also provides pre-trained models and algorithms from frameworks such as TensorFlow, Scikit-Learn, PyTorch, HuggingFace, MxNet GluonCV, etc. You can access the algorithms using the SageMaker Python SDK to build your machine-learning applications.
• To use Jumpstart, you need to have a significant understanding of machine learning concepts. Hence, a machine learning engineer or a data scientist is the right person to use SageMaker Jumpstart.

SageMaker for K8

After training machine learning models, machine learning engineers and data scientists also need to manage and schedule machine learning workflows. Normally, we use open-source tools like Kubernetes or Kubeflow pipelines for controlling MLOps workflows to automate the deployment and management of containerized ML applications. However, manually managing the Kubernetes-based ML infrastructure is a time-consuming and challenging task.

• Amazon SageMaker Operators and Components automatically set up the necessary resources with autoscaling for ML model deployment. This eliminates the need to manually set up Kubernetes environment for machine learning applications.
• By leveraging the Amazon SageMaker Operators and Components, you can set yourself free from installing updates to the system. SageMaker manages the updates and installs. It helps us ensure that the data science teams use the latest deep learning and machine learning frameworks and other tools. This saves time for MLOps engineers and they can focus on other productive tasks.
• We can quickly set up development environments including Jupyter Notebooks, job management tools, and Python libraries for working on Kubernetes-based ML platforms using Amazon SageMaker Studio and SageMaker Notebooks. This helps the MLOps teams save time and the data science teams get into action quickly.

SageMaker Model Monitor

After deploying a machine learning model, data scientists and machine learning engineers still need to monitor them for bias, data drift, or inaccurate predictions so that they can take corrective measures. However, monitoring the models manually in production is a difficult task. 

SageMaker Model Monitor gives us a complete solution for model monitoring in production.

• With SageMaker Model Monitor, we can select the data we want to monitor and analyze it without writing any code.  We can select data from a menu of options such as input data, prediction output, etc. The Model Monitor captures the data, timestamp, model name, and endpoints so we can analyze model predictions based on the recorded data. We can also specify the sampling rate for capturing data as a percentage of overall traffic if we want to monitor machine learning applications with high-volume real-time predictions. Then, we can use SageMaker Studio to collect and visually analyze data generated from Model Monitor.
• Although the initial training data or model may not be biased, changes in the prediction data may cause bias to develop over time in a trained model. For example, if there is a substantial change in demographics in an area, it can cause a machine learning model for evaluating home loan applications to become biased if the data points such as income, age, or employment status change significantly from the original training data. In such situations, we can use Model Monitor along with SageMaker Clarify to identify potential bias in the data or the deployed machine learning models.

SageMaker Notebooks

Data scientists love to work with Jupyter Notebooks. This helps them execute code and visualize results from each step while working on machine learning projects, especially in the data exploration and cleaning steps. However, converting Jupyter Notebooks into production-ready jobs is a tedious task and most data scientists face difficulties in this process.

SageMaker Notebooks provide us with fully managed Jupyter notebooks for exploring data and building machine-learning models. In SageMaker, we get two types of fully managed Jupyter Notebooks i.e. SageMaker notebook instances and SageMaker Studio notebooks.

• SageMaker provides standalone and fully managed Jupyter Notebook instances in the Amazon SageMaker console. With Notebook instances, you can select resource configurations, network access, encryption, and version control from a wide variety of options. Then, you can proceed with the machine learning tasks as required.
• SageMaker Studio Notebooks are collaborative notebooks. They integrate with SageMaker Studio and other SageMaker tools easily. We can train and test models, track experiments, deploy and monitor the models, and manage pipelines in a single place using SageMaker Studio Notebooks.
• SageMaker Studio Notebook also helps data scientists convert the Jupyter Notebooks into production-ready jobs within minutes. Once we select a notebook, SageMaker Studio Notebook takes a snapshot of the entire notebook, packages its dependencies in a container, builds the infrastructure, and runs the notebook as an automated job on a schedule. This reduces the time in moving a notebook to production from weeks to hours. 

SageMaker Pipelines

To create a machine learning model starting from the raw data, we need to perform different operations like data cleaning, data split, data scaling, training the model, etc. For a large project with hundreds of workflows with different models, it becomes very hard to manage the operations of the machine learning application.  For such cases, SageMaker Pipelines provides a reliable way to create, automate, and manage end-to-end ML workflows at scale.

• We can automate data ingestion, data transformation, model training, testing, tuning, and deployment using SageMaker Pipelines. We can also visualize and manage the workflow using Amazon SageMaker Studio.
• SageMaker Pipelines allow us to store and reuse the workflow steps in a machine learning project. This gives us a headstart for future ML projects as we can use the stored workflow steps to build, test, register, and deploy models.
• Amazon SageMaker Pipelines log every step of the ML workflow. This creates an audit trail of all the model components. We can use the audit trail to track the source of the error if anomalies occur in the ML model.

SageMaker RStudio

SageMaker RStudio is a fully managed, cloud-based RStudio IDE for data science and machine learning tasks. If you have an existing RStudio workbench, you can import it into SageMaker using your current license at no additional cost. You can also bring your entire development environment into SageMaker in a custom docker image.

SageMaker RStudio helps you unify the Python and R data science teams by allowing the data scientists and ML engineers to switch freely between SageMaker RStudio and SageMaker Studio IDE without losing context. The data scientists can seamlessly switch between the SageMaker Studio Notebooks and RStudio IDE for Python and R development. 

The codes, datasets, and repositories of the RStudio and SageMaker Studio are synchronized automatically. This helps reduce context switches and boosts productivity.  Thus, if you are someone who manages Python and R data science teams working on different aspects of the same project, you can bring them together using SageMaker RStudio and SageMaker Studio.

Shadow Testing

When models are already in production and we want to replace them with new models, we need to be careful about the scalability and performance of the new model. Sometimes, the new model isn’t good enough and the entire system goes down. To avoid this, SageMaker provides a shadow testing feature. In shadow testing, the new model is deployed parallely to the existing production model. After that, the live data that is fed to the production model is also fed to the new model and we can observe the performance of the new model before deploying it into production.

• When running shadow tests in SageMaker, we can configure the percentage of actual inference requests in the production environment being sent to the test models. By controlling the data input to the new model, we can check its performance and scalability.
• While shadow testing, SageMaker creates a live dashboard that shows metrics such as latency and accuracy of the production model and the new model side-by-side. We can review the test results, validate the new model, and deploy it into production in a single click after comparing its performance with the previously deployed model.

SageMaker Studio Lab

SageMaker Studio Lab is a free machine-learning development environment to help students and professionals get started with machine learning. 

• You can create a free Studio Lab account using a valid email address and start experimenting with machine learning concepts. You don’t need an AWS account for this.
• SageMaker Studio Lab provides 12-hour CPU sessions and 4-hour GPU sessions to help you build machine-learning models using Jupyter Notebooks and open-source frameworks like Tensorflow and PyTorch. Thus, you have enough time to build ML models using different algorithms to understand their functioning.
• SageMaker Studio Lab provides 15 GB of free long-term storage. This helps you save Jupyter notebooks and other resources while experimenting with ML concepts. When a session ends, SageMaker automatically saves your entire work in the storage. After experimenting in Studio Lab, you can even deploy the created ML models by directly exporting them or deploying them using SageMaker.

Amazon SageMaker Studio

SageMaker Studio is an integrated development environment that provides a single web-based visual interface to perform all machine learning development steps. You can prepare data, build, train, and deploy ML models, and monitor the deployed models from a single interface. This helps you improve the productivity of data science and ML teams. Basically, SageMaker Studio brings all the SageMaker features into a single interface. 

Amazon SageMaker Model Training

Training machine learning and deep learning models is a resource-intensive and time-consuming task. For training enterprise-level ML applications, we need hundreds of cores of CPUs or GPUs with hundreds of gigabytes of RAM. 

SageMaker Model Training provides configurable hardware infrastructure that can automatically scale up or down, from one to thousands of GPUs. This helps us train ML models faster in a cost-effective manner as we need to pay only for the resources we use.

• SageMaker Model Training supports data parallelism and model parallelism. This helps us train machine learning and deep learning models with terabytes of training data by splitting the data and model training efficiently to the available infrastructure.
• To reduce training costs, SageMaker Model Training automatically runs training jobs when compute capacity becomes available. With the help of SageMaker Profiler, we can also optimize training performance with hardware profiling insights such as aggregated GPU and CPU utilization metrics. This helps us reduce training costs by up to 90 percent when compared to using dedicated in-house training infrastructure.

Alternatives to Amazon SageMaker

Many alternatives to Amazon SageMaker provide similar services that we discussed in this blog. Some of the top alternatives to SageMaker are as follows.

Vertex AI

Google Vertex AI is a managed ML platform. It provides many tools for training, testing, tuning, and deploying machine learning models. Vertex AI provides many features such as Model Garden, Vertex AI Notebooks, Vertex AI Training, Vertex AI Prediction, Evaluation, Pipelines, Monitor, Model registry, Feature Store, etc. These features are a direct replacement of the features provided by Amazon SageMaker.

IBM Watson Studio

IBM Watson Studio is an integrated development environment for training, running, and managing ML models. It also provides tools such as AutoAI, advanced data refinery, integrated visual tools, model training and development, model monitoring and management, risk management, and support for open-source frameworks to help us build machine learning applications. 

Azure Machine Learning

Azure Machine Learning is an enterprise-grade service for managing end-to-end ML lifecycle. It provides features and tools for data labeling, data pre-processing, Automated machine learning, tracking experiments, training, tuning, and deploying ML models. It also provides support for Jupyter Notebooks, Model registry, error analysis, checking data drifts, managed endpoints for deployed models, etc.

Conclusion

Amazon SageMaker is a robust and powerful tool for machine learning. It offers a plethora of features that you can use to create machine-learning applications. As we explored in this blog, alternatives such as Vertex AI, Azure ML, and IBM Watson also provide features similar to Amazon Sagemaker. However, it’s crucial to recognize that these tools may not be the ideal choice for everyone or every project. If you have a team of machine learning engineers and data scientists, you can use any of the above tools to build ML models and deploy them.

I hope you found this blog useful. You might also like this article on Python libraries.

Stay tuned for more informative articles. Happy Learning!