We learned about Amazon SageMaker and Connecting AWS to Visual Studio Code in the previous articles, AWS SageMaker and How To Connect VS Code To AWS. In this article, we’ll learn to set up the notebook instance in Amazon SageMaker.

Notebook is the primary tool through which interaction is done with the SageMaker ecosystem. There are numerous other ways for the interaction to the functionalities of Amazon SageMaker with this approach widely used. Let us learn how to create the instance of the notebook within the Amazon SageMaker for the Machine Learning application’s modeling, training, and validation.

Step 1


In this article, we’ll learn how to clone repositories from GitHub using git clone which will enable us to run the repo on the notebook instance created in AWS. This will become highly beneficial for Machine Learning Engineers and Data Scientists looking to explore the notebooks from other creators in the production environment without having to install all packages and libraries on their local notebook and having to go through the complications of any dependencies and resource limitations.

Amazon Web Services provides a wide range of features for Machine Learning and Amazon SageMaker is at the forefront. We learned to…

In this article, we’ll learn about AWS SageMaker and various tools provided by it for Machine Learning purposes. The article describes the ways the machine learning workflow is supported by different tools of SageMaker, SageMaker Instances, Availability Zones for SageMaker, and Instances that can be used for Deep Learning. This will give us a brief overview of Amazon SageMaker as a whole.

Amazon SageMaker

Amazon SageMaker is a cloud platform dedicated to artificial intelligence, machine learning, and deep learning which enables creating, training, tuning, and deploying models for machine learning in the cloud. Large-scale machine learning models can be managed easily with…

In this article, we’ll learn briefly about Amazon Web Services (AWS) and then go through a hands-on procedure to connect Visual Studio Code (VS Code) to AWS with the credential and configuration.

Amazon Web Services (AWS)

Amazon Web Services is one of the leading cloud computing platforms and provides services related to remote computing, servers, security, storage, networking, and many more. It provides scalable and reliable solutions for cloud computing for businesses across the globe and controls 31% of the market share in the cloud computing infrastructure globally.

Visual Studio (VS) Code

Visual Studio Code can be understood as a code editor with an integrated development environment that…

Various aspects of Cloud Computing, its major characteristics, and a comparative analysis between different cloud computing platforms were discussed in the articles Major Characteristics of Cloud Computing and Comparative Analysis of Cloud Platforms respectively. In this article, we’ll learn about the plethora of benefits cloud computing provides as well as the risk aspects of its usage. Each of the pros and cons is described in detail.


  • Cost Saving and Reduced Investment
  • Increase in Scalability
  • High Availability and Reliability
  • Efficient Collaboration
  • Automatic Updates

Cost Saving and Reduced Investment

Cloud Computing has significant hands-on reducing the costs proportionally to the usage of the services and reducing the…

In this article, we’ll learn about Cloud Computing and its different characteristics. These constitute the major features the cloud computing services must have inherently in order to cater to the wide domain of possible users in such a way that it mitigates the possible risks and hazards and at the same time provides ease of use with economical benefits.

Cloud Computing

Let us start with an example of a commonly used feature. The OneDrive of Microsoft, Google Drive of Google, and the Apple iCloud. Each of these is a cloud storage provider. This has completely transformed the user behavior of depending on…

There are numerous cloud platforms providing services for machine learning. This article discusses the comparative analysis between these commonly used cloud platforms and each of its various features supported for Machine Learning. There are different features that are common in all of these platforms while some are specially provided by specific platforms alone. The ones that will be discussing here are Amazon’s AWS SageMaker, Google’s ML Engine, Microsoft’s Azure AI, Cloud Foundry, and PaperSpace. Let us learn in detail about each of these individually.

Amazon Web Services (AWS)

Amazon Web Services provides the Amazon Web Services SageMaker platform which enables the building, training, and…

In this article, we’ll learn about the various properties of modeling and deployment of machine learning applications. We’ll explore Hyperparameters, Model Versioning, Model Monitoring, Model Updating, Model Routing, and Model Predictions. Moreover, we’ll also dive into on-demand predictions and batch predictions.


A machine learning model can be understood as the file which is trained with sets of data using algorithms such that it becomes capable to recognize specific types of patterns. Modeling is the process of using mathematical models in order to generate predictions so that patterns can be identified. This is one of the key processes in Machine Learning…

In the last article, we discussed deployment and production environment such that it consisted of two primary programs, the application itself and the model by communicating with each other using an interface we know as the endpoint. Today, we’ll learn about the Containers and how Machine Learning Applications can benefit from them. We’ll learn briefly about Model, Application, and then dive into Containers and specifically Docker and explore its structure and its advantages.


To understand the Model, let us take an instance of a simple Python model which is to be created, trained, and validated in the modeling component part…

In this article, we’ll learn about how communication is done between the machine learning models and the full-fledged application that uses components of artificial intelligence. In today’s world where artificial intelligence is adding value to multitudes of domains, let us learn how software applications are enabled with AI.

Different methods of deployment were discussed in the previous article, Machine Learning Workflow and Methods of deployment. With deployment, the model can be made available for usage through a web application or software. …

Ojash Shrestha

Man on a Mission - to create epochal impact

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