A Guide to Manage Amazon Machine Image: From Cloud to the On-Premises

Published on Feb 22, 2023

Why do people opt for on-premise storage

Uploading an Amazon Machine Image (AMI) to Amazon Simple Storage Service (S3) and downloading it to your on-premises machine can be useful for creating backups, sharing images with others, or moving images between regions. In this article, we will explain the process of uploading an AMI to S3 and downloading it to your data center, how to create an AMI from an on-premises backup, and how to launch an instance from that AMI.

Benefits of maintaining AMI on-premise data center

Compliance and security: Some organizations are required to keep specific data within their data centers for compliance or security reasons. Keeping AMIs in an on-premises data center allows them to maintain control over their data and ensure that it meets their compliance and security requirements.

Latency and bandwidth: Keeping AMIs in an on-premises data center can reduce the latency and bandwidth required to access the images since they are stored closer to the instances that will use them. This can be especially beneficial for firms with high traffic or large numbers of instances and also to avoid data transfer charges.

Cost savings: By keeping AMIs in an on-premises center, organizations can avoid the costs associated with storing them in the cloud. This can be especially beneficial for companies with large numbers of images or with high storage requirements.

Backup and Disaster Recovery: A copy of the AMI allows organizations to have an additional layer of backup and disaster recovery. In case of an unexpected event in the cloud, the firm can launch an instance from an on-premises copy of the AMI.

It’s important to note that keeping AMIs in an on-premises data center can also have some disadvantages, such as increased maintenance and management costs, and reduced flexibility. Organizations should weigh the benefits and drawbacks carefully before deciding to keep AMIs in an on-premises data center.

Uploading AMI to S3 bucket using AWS CLI

To upload an AMI to S3, you will need to have an AWS account and the AWS Command Line Interface (CLI) installed on your local machine.

Step 1: Locate the AMI that you want to upload to S3 by going to the EC2 Dashboard in the AWS Management Console and selecting “AMIs” from the navigation menu.

Step 2: Use the AWS ec2 create-store-image-task command to create a task that exports the image to S3. This command requires the image-id of the instance and the S3 bucket you want to store the image in.

Step 3: Use the AWS ec2 describe-import-image-tasks command to check the status of the task you just created.

Once the task is complete, the AMI will be stored in the specified S3 bucket.

Downloading the AMI from the S3 bucket

Now that the AMI has been uploaded to S3, here’s how you can download it to your local machine.

Use the AWS s3 cp command to copy the AMI from the S3 bucket to your local machine. This requires the S3 bucket and key where the AMI is stored and the local file path where you want to save the AMI.

Or else you can use the AWS S3 console to download the AMI file from the S3 bucket.

By following these steps, you should be able to successfully upload an AMI to S3 and download it to your local machine. This process can be useful for creating backups, sharing images with others, or moving images between regions.

It’s important to note that uploading and downloading large images may take some time, and may incur some costs associated with using S3 and EC2 instances. It’s recommended to check the costs associated before proceeding with this process.

Creating AMI from the local backup in another AWS account

To create AMI from the local backup in another AWS account, you will need to have an AWS account and the AWS Command Line Interface (CLI) installed on your local machine. Then, upload your local AMI backup on S3 on another AWS account

Step 1: Locate the backup that you want to create an AMI from. This backup should be stored in an S3 bucket in the format of an Amazon Machine Image (AMI).

Step 2: Use the AWS ec2 create-restore-image-task command to create a task that imports the image to EC2. This requires the object key of the image in S3, the S3 bucket where the image is stored, and the name of the new image.

Creating AMI from the local backup in another AWS account

Step 3: Use the AWS ec2 describe-import-image-tasks command to check the task status you just created.

Once the task is complete, the AMI will be available in your EC2 Dashboard.

Now the AMI has been created, let’s discuss the process of launching an instance from that AMI.

Step 1: Go to the EC2 Dashboard in the AWS Management Console and select “Instances” from the navigation menu.

Step 2: Click the “Launch Instance” button to start the process of launching a new instance.

Step 3: Select the newly created AMI from the list of available AMIs.

Step 4: Configure the instance settings as desired and click the “Launch” button.

Step 5: Once the instance is launched, you can connect to it using SSH or Remote Desktop.

Conclusion

In this article, we learned about the process of uploading and downloading an Amazon Machine Image (AMI) to Amazon Simple Storage Service (S3) and downloading it to an on-premises machine. We dived into the benefits of maintaining AMIs in an on-premises data center, including compliance and security, reduced latency and bandwidth, cost savings, and backup and disaster recovery. The steps for uploading an AMI to S3 using the AWS Command Line Interface (CLI) and downloading it from S3 were explained in detail. Finally, the process of creating an AMI from a local backup in another AWS account was discussed and demonstrated.

Hope you found this article helpful and interesting.

Want to read more such content?

Check out our blog: Implementing a Clean Architecture with Nest.JS

About the Author:

Suraj works as a Software Engineer at Mantra Labs. He’s responsible for designing, building, and maintaining the infrastructure and tools needed for software development and deployment. Suraj works closely with both development and operations teams to ensure that the software is delivered quickly and efficiently. During his spare time, he loves to play cricket and explore new places.

In 1989, a missing $0.50 bolt led to the mid-air explosion of United Airlines Flight 232. The smallest oversight in manufacturing can set off a chain reaction of failures. Now, imagine a factory floor where thousands of components must function flawlessly—what happens if one critical part is about to fail but goes unnoticed? Predictive analytics in manufacturing ensures these unseen risks don’t turn into catastrophic failures by providing foresight into potential breakdowns, supply chain risk analytics, and demand fluctuations—allowing manufacturers to act before issues escalate into costly problems.

Industrial predictive analytics involves using data analysis and machine learning in manufacturing to identify patterns and predict future events related to production processes. By combining historical data, machine learning, and statistical models, manufacturers can derive valuable insights that help them take proactive measures before problems arise.

Beyond just improving efficiency, predictive maintenance in manufacturing is the foundation of proactive risk management, helping manufacturers prevent costly downtime, safety hazards, and supply chain disruptions. By leveraging vast amounts of data, predictive analytics enables manufacturers to anticipate machine failures, optimize production schedules, and enhance overall operational resilience.

But here’s the catch, models that predict failures today might not be necessarily effective tomorrow. And that’s where the real challenge begins.

Why Predictive Analytics Models Need Retraining?

Predictive analytics in manufacturing relies on historical data and machine learning to foresee potential failures. However, manufacturing environments are dynamic, machines degrade, processes evolve, supply chains shift, and external forces such as weather and geopolitics play a bigger role than ever before.

Without continuous model retraining, predictive models lose their accuracy. A recent study found that 91% of data-driven manufacturing models degrade over time due to data drift, requiring periodic updates to remain effective. Manufacturers relying on outdated models risk making decisions based on obsolete insights, potentially leading to catastrophic failures.

The key is in retraining models with the right data, data that reflects not just what has happened but what could happen next. This is where integrating external data sources becomes crucial.

Is Integrating External Data Sources Crucial?

Traditional smart manufacturing solutions primarily analyze in-house data: machine performance metrics, maintenance logs, and operational statistics. While valuable, this approach is limited. The real breakthroughs happen when manufacturers incorporate external data sources into their predictive models:

Weather Patterns: Extreme weather conditions have caused billions in manufacturing risk management losses. For example, the 2021 Texas power crisis disrupted semiconductor production globally. By integrating weather data, manufacturers can anticipate environmental impacts and adjust operations accordingly.
Market Trends: Consumer demand fluctuations impact inventory and supply chains. By leveraging market data, manufacturers can avoid overproduction or stock shortages, optimizing costs and efficiency.
Geopolitical Insights: Trade wars, regulatory shifts, and regional conflicts directly impact supply chains. Supply chain risk analytics combined with geopolitical intelligence helps manufacturers foresee disruptions and diversify sourcing strategies proactively.

One such instance is how Mantra Labs helped a telecom company optimize its network by integrating both external and internal data sources. By leveraging external data such as radio site conditions and traffic patterns along with internal performance reports, the company was able to predict future traffic growth and ensure seamless network performance.

The Role of Edge Computing and Real-Time AI

Having the right data is one thing; acting on it in real-time is another. Edge computing in manufacturing processes, data at the source, within the factory floor, eliminating delays and enabling instant decision-making. This is particularly critical for:

Hazardous Material Monitoring: Factories dealing with volatile chemicals can detect leaks instantly, preventing disasters.
Supply Chain Optimization: Real-time AI can reroute shipments based on live geopolitical updates, avoiding costly delays.
Energy Efficiency: Smart grids can dynamically adjust power consumption based on market demand, reducing waste.

Conclusion:

As crucial as predictive analytics is in manufacturing, its true power lies in continuous evolution. A model that predicts failures today might be outdated tomorrow. To stay ahead, manufacturers must adopt a dynamic approach—refining predictive models, integrating external intelligence, and leveraging real-time AI to anticipate and prevent risks before they escalate.

The future of smart manufacturing solutions isn’t just about using predictive analytics—it’s about continuously evolving it. The real question isn’t whether predictive models can help, but whether manufacturers are adapting fast enough to outpace risks in an unpredictable world.

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In the manufacturing industry, predictive analytics plays an important role, providing predictions on what will happen and how to do things. But then the question is, are these predictions accurate? And if they are, how accurate are these predictions? Does it consider all the factors, or is it obsolete?