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Table of contents
Amazon SageMaker and its Benefits🎯  
Features of Amazon SageMaker
Machine Learning with Amazon SageMaker
Generate the Data
Train the Data
Training the model
Evaluating the Model
Explore, Analyze, and Process Data
Fairness and Model Explainability
Practices for Evaluating Fairness and Explainability in ML Life Cycle
Working with Models
Train a Model
Deploy a Model
Validating an ML model
Model Monitoring
Frequently Asked Questions
How does Amazon SageMaker secure the code?
Can we use your tools with Amazon SageMaker?
Write some of the primary advantages of Amazon SageMaker.
What is SageMaker SDK?
Last Updated: Mar 27, 2024

Amazon SageMaker

Author Naman Kukreja
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The world is achieving new heights in technological achievements day by day. We can do this with the help of many algorithms, machine learning, and much more, but learning machine learning is quite tricky, and so is its implementation.


But if we have to work in technological advancements in this era, we have to learn at least the basics of machine learning algorithms and their implementation. So is there any easy way for its implementation? 

The answer to the above question is yes, and we can use sagemaker to train and implement our local machines quickly. We will learn all about amazon sagemaker while moving further in the blog. So let's get on with our topic without wasting any time further.

Amazon SageMaker and its Benefits🎯  

Amazon SageMaker is one of the many services provided by Amazon Web Services(AWS). With the help of this, you can train, build and deploy ML tools for industrial and personal work. As it provides AWS, you need not provide and manage your servers.

You can use machine learning in many areas due to its vast benefits. It automates such complex tasks as building a production-ready AI. It can also be used for back-end security threat detection and analytics for customers.

Deploying Machine learning algorithms is not easy even for experienced developers, but sagemaker makes it easy to apply and accelerate machine learning algorithms. We will learn more about sagemaker with its applications.

Features of Amazon SageMaker

Many features are available in Amazon SageMaker, but it has launched some new features in the latest launch of 2017. You can access and use all the features and functions in AWS SageMaker Studio. It is an IDE that consolidates all the capabilities.


There are many features available in sagemaker, and here we will discuss some of them:

📙Automation tools help the users to manage, debug and track ML models.

📗Autopilot makes the AI train the given data set and rank them in order of accuracy.

📙Auto ML makes it easier to do projects for both types of people, one who has coding experience and another who does not have any coding experience.

📗Easily discoverable, connectable, and can be terminated and managed by different amazon EMR clusters into a single account.

📙You can create a jupyter notebook easily and transfer all the data there with only one click.

Machine Learning with Amazon SageMaker

This section will understand the machine learning workflow and how it works with Amazon SageMaker. In machine learning, we work with computers and teach them to make predictions.

We initially insert some information and algorithm to make predictions and then integrate them with our application to make predictions in real-time. There are three steps in generating the data, training a model, and deploying a model.

Machine Learning with Amazon SageMaker


Generate the Data

Before doing any operation like training or deploying, you need some data. Here we will generate that data. It may vary from user to user need and from business to business requirements. There are three steps in developing the data.

1️⃣Fetch the Data: You can fetch the data either from your private repository or from any public repository.

2️⃣Clean the data: As the name suggests here, you check for any errors, and according to your requirements, you clean the data.

3️⃣Transform the data: Here, you will add some additional data with your cleaned data and combine their attributes so that this data can be trained.

Train the Data

We train the model using algorithms to behave accordingly in real-time situations. We cannot choose a random algorithm to train. It will depend on the number of factors, and we will discuss all of them in detail below:

Training the model

There are many steps involved in training the model. We have discussed all of them in this blog section.

📘Use an in-built algorithm provided by amazon sagemaker: Amazon Sage maker provides different algorithms that can be sued for training ML. If anyone of them satisfies your need, you can use them.

📘Amazon SageMaker Apache spark: Amazon sagemaker provides various libraries to train, and one of them can be used to train in Apache Spark.

📘Use your custom algorithms: You can use your algorithms to train. You have to combine your codes and create a docker image and specify the registry of the path in Amazon sage maker.

Evaluating the Model

Training is not alone enough, as, after training, we have to evaluate our model and then see its accuracy and whether it is capable of deploying or not. You can evaluate the model by two means, either online or offline.

📕Online Testing: You can use amazon sagemaker production variants. In this, your trained model small portion gets checked in different situations.

📕Offline Testing: In this, you will deploy your trained model on the alpha endpoint and use the previous or historical data to send inference requests.

Deploy the Model. You may not directly deploy the evaluated and trained model. You will re-engineer yourself and make different changes according to the need before posting it in hosting services. You can also deploy your model independently.

Your work is not finished after deployment. You have to monitor the data and collect ground truth.

Explore, Analyze, and Process Data

Before directly training the data model, the scientists usually analyze and preprocess it first. 

Amazon SageMaker allows the jobs to post-process and preprocess the running data at every stage. Processing provides you with many benefits when the data is combined with other high-level ML tasks like hosting and training. It provides compliance support and security built into sagemaker. You have the freedom to use either built-in data processing containers or your containers to process the custom jobs. After submission, the sagemaker analyses the input data process and releases the resource upon completion.

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Fairness and Model Explainability

Amazon SageMaker Clarify aids in the improvement of machine learning (ML) models by detecting potential bias and assisting in explaining the models' predictions. It aids in detecting various types of bias in pretraining and posttraining data that can arise during model training or production. SageMaker Clarify explains how these models use a feature attribution approach to make predictions. It also checks for bias or feature attribution drift in inferences made by models in production. SageMaker Clarify's fairness and explainability functionality include components that assist AWS customers in creating less biassed and more understandable machine learning models. It also contains tools to help you in creating model governance reports for use by risk and compliance teams and external regulators.

Machine learning models and data-driven systems are increasingly being used to aid decision-making in various industries, including finance, healthcare, education, and human resources. Machine learning applications help meet regulatory requirements, improve business decisions, and provide better insights into data science procedures by improving accuracy, productivity, and cost savings.

📔Data Science: Ml engineers and data scientists need to update the tools according to model requirements regularly. They have to keep an eye on all the model that provides disturbances and other hindrances, and after engineering, they have to remove that.

📔Regulatory: We have to understand why an ML model had made a specific prediction and whether it was influenced by any method during training or not. There are policy and ethical challenges posed by ML data-driven systems.

📔Business: Adopting AI systems in regulated domains necessitates trust, which can be established by providing credible explanations of trained model behaviour and how deployed models make predictions. Financial services, human resources, healthcare, and automated transportation are examples of industries where model explainability is critical.

Practices for Evaluating Fairness and Explainability in ML Life Cycle

You can follow certain practices to evaluate fairness and explainability in the ML life cycle.

1️⃣Explainability and Fairness by Design: You have to consider explainability and fairness in each stage of the ML cycle: dataset construction, problem formation, model training process, algorithm selection, deployment, and feedback.

2️⃣Fairness as a process: Fairness is highly dependent on the application. Furthermore, social, legal and other non-technical considerations may need to guide the selection of the attributes for which bias is to be measured and the choice of bias metrics. Building consensus and collaboration among key stakeholders (including product, policy, legal, engineering, AI/ML teams, and end-users and communities) is essential for successfully implementing fairness-aware ML approaches.

Working with Models

There are many steps involved while working with models. We will discuss each step in detail while moving forward in this section.

Working with Models

Train a Model

To train a model in sagemaker, you need to create a training job. Your training job must include the following instructions:

📙The URL of Amazon S3 where the training data has been stored.

📙The URL of the Amazon S3 bucket is where you want to keep the output.

📙All the resources required by sagemaker for model training.

You have some options available for training algorithm:

📗Use sagemaker provided algorithm: Sagemaker provides many algorithms. If any one of them satisfies your requirements, you can use it.

📗Use Sagemaker Debugger: To check the data and training parameters while working or learning different frameworks sagemaker debugger helps you detect errors automatically.

Deploy a Model

You can use Amazon SageMaker to deploy the project after training it to get predictions:

📘You can use SageMaker's real-time service to make persistent predictions.

📘Can use serverless inference for idle workloads for a particular time.

📘Asynchronous abstraction can be used for large payloads with low latency requirements.


SageMaker provides many features to manage and optimize resources.

📔It can manage models on edge devices to secure, maintain ML models, monitor, and optimize a fleet of edge devices like robots, smart cameras, mobile devices, and personal computers.

📔To optimize Py Torch, Tensor Flow-Lite, Tensor Flow, Keras, Gluon, and ONMX models for inference on windows, Linux, and various windows based processors Intel, ARM, Qualcomm, Nvidia, etc.

Validating an ML model

After training a model, we have to evaluate it in some scenarios to test its accuracy and performance are aligned with our business goals or not. We have various methods to validate the model:

📙Testing with Live Data: It supports testing by using different production variants. You can understand production variants as the models deployed on the same sagemaker and use the same inference code. We give a small fraction of total traffic for validation.

📙Offline testing: Here, we will not use live data. We will use the historical data. Deploy the model at the alpha endpoint. Here we will use a jupyter notebook to send data.

📙Validation using a holdout set: Ml practitioners set aside some part of the data as a holdout set. With the help of this, you can check how much inference your model is providing on the holdout set. This approach will also give you an idea of how often you will get the correct answers.

📙K-fold validation: In this, you distribute or split the dataset into k parts and use one of the k parts as a holdout.

Model Monitoring

Use the Amazon SageMaker model monitor to continuously monitor the quality of your machine learning models in real-time after you deploy a model into your production environment. When there are deviations in the model quality, such as data drift and anomalies, the Amazon SageMaker model monitor allows you to set up an automated alert triggering system. Amazon CloudWatch Logs collects log files of model status monitoring and notifies you when the model's quality falls below certain thresholds you set. CloudWatch saves the log files to an Amazon S3 bucket that you specify. Model deviations can be detected early and pro-actively using AWS model monitor products, allowing you to take immediate action to maintain and improve the quality of your deployed model.

Frequently Asked Questions

How does Amazon SageMaker secure the code?

It stores the code in ML storage volumes and with proper encryption.

Can we use your tools with Amazon SageMaker?

Although Amazon SageMaker provides complete end-to-end encryption, you can still use your tools with it without any problem.

Write some of the primary advantages of Amazon SageMaker.

With the help of this, you can deploy an inference pipeline, preprocess the input and work on it.

What is SageMaker SDK?

It is an open-source library for deploying and training ML models on Amazon SageMaker.


In this article, we discussed Amazon SageMakaer and its features, Machine learning, fairness and model explainability. We have also discussed the model in detail with an appropriate explanation of each step, like the training, deployment, validation, and monitoring of the model.

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