Amazon Deep Learning AMIs vs. Amazon Virtual Private Cloud (VPC) vs. IBM Cloud Object Storage

Amazon AMIs has been very useful for the quick setup and implementation of deep learning for data analysis which is something I have used the service for in my own research. We commonly use the service to enable students to run intensive deep learning algorithms for their assessments. This service works well in this scenario as it allows students to quickly set up a suitable environment and get started with little hassle. If you are looking to run simple, surface level deep learning algorithms (kind of contradictory statement I know) then AMI is more complicated than most will need. When it comes to teaching the basics of Machine Learning, this kind of system is unnecessary and there are other alternatives which can be used. That being said this service is a must if you are looking to run complex deep learning via the cloud.

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If you are going to deploy within AWS, you need to know how to leverage VPCs. VPCs have several items you must configure for them to be usable and to even link one VPC to another. If you are only going to deploy a few resources or use some of the default configurations, you still need to have working knowledge of how a VPC functions. If you are used to working with VLANs and the ideology behind those, that is the best direct reference to how a VPC can be described, but with additional layers on top of that with route tables, internet gateways, etc.

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In my experience, IBM Cloud Object Storage is well suited for projects like the one I am working on. This includes the use of natural language classification and the uploading of data to train a machine learning model for tag suggestions based on a body of text. Using IBM Cloud Object Storage has helped with this greatly. IBM Cloud Object Storage has also been great for Big Data Analytics thanks to its scalablilty and ease of use for large datasets. Alongside IBM Watson and our team's internal big data tools we've managed to process and analyze data more efficiently, leading to key insights that have driven business value for our clients.

• Setting up environment
• Support for different types of machines
• Perfect for Machine Learning / Deep Learning use cases
• Nvidia / Cuda / Conda support easily

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• Pricing model & On demand service commitment.
• Well, architectural infrastructure development processing before spending the money.
• AWS VPC are providing hassle-free using with all others services from AWS very easily.
• AWS business support is very helpful & [gives] the right direction according to client’s situations.

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• IBM Cloud Object Storage is an excellent choice for disaster recovery and backup solutions. Its high durability and geographic redundancy ensure that our backup data is safe and can be quickly restored in case of a disaster. This capability is crucial for maintaining our business continuity and minimizing downtime. We have deployed our loads in an IKS cluster distributed in 3 different AZs with stateful data allocated in COS.
• We have a video streaming application and need to store and deliver a vast library of video content to millions of users worldwide, so we store our data in COS, which is cheap and reliable.
• We have a bunch of data that must be analyzed and stored in datasets for fraud detection, risk management, and customer insights. In these cases, this data is moved from Onprem to IBM Cloud so we can use cheap storage like COS.

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• Some aspects of the User Interface are quite confusing and activating packages can be a bit convoluted
• It can be a bit confusing to switch between frameworks for novice users

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• The biggest issue with VPC networks is knowing how you can leverage VPC endpoints to ensure your resources within the VPC are not reaching out over the Internet to get to AWS services such as S3 and others.

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• Searching and retrieving—full-text search or metadata search—is one of the significant areas of improvement. It isn't easy to search for data with this.
• Integration with other IBM cloud services is trickier. For example, integrating this with API Connect to access the data from API will be difficult for users.
• Support - I think you should have more support community.

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because it is a robust, safe and flexible product

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For my use cases, it has been a very smooth experience. Even my new colleagues have been able to get on top of things very quickly. This shows how easy it is to work with

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We rarely face downtime or access issues with IBM Cloud Object Storage. It’s mostly available when we need it, even during peak hours or heavy data loads.

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I would give it a 9 because it works smooth with our AI and analytics tools, no major slowdown. Pages and dashboards load fine most of the time, and reports finish in decent time even when data is heavy.

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I have been working in IT sector for more than 15 years. I have worked with various vendors. IBM's sales team, support team have been really helpful. After we start to use their product, their UX design team also contacted us to get feedback from us. They are really interested about our experience.

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I just researching and applying the tools on their platforms to ensure a good learning path, based on my needs. Reading the documentation related with resources, tools. Is too big, but I am trying to know more about it every day. It is a good way to know more about their resources. A new way to attract new customers. At the end of the day, we are all involved in improvement and automation of our tasks and resources for customers and end-users.

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Yes Our organization used IBM professional services to implement IBM object storage because of its data consistency and multiple way to upload and download data and its encryption security features. Also that its brand matter for the any organization to secure the layer and storage. It sis also verify that application and system are compatibale for this product

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Both of these services provide similar functionality and from my experience both are top class services which cover most of your needs. I think ultimately it comes down to what you need each service for. For example Amazon DL AMIs allows for clustering by default meaning I am able to run several clustering algorithms without a problem whereas IBM Watson Studio doesn't provide this functionality. They both provide a wide range of default packages such as Amazon providing caffe-2 and IBM providing sci-kitlearn. My main point is that both are very good services which have very similar functionality, you just need to think about the costs, suitability of features and integration with other services you are using.

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Amazon S3 is a great service to safely back up your data where redundancy is guaranteed, and the cost is fair. In the past I have used Amazon S3 for data that we backup and hope we never need to access, but in the case of a catastrophic or even small slip of the finger with the delete command, we know our data and our client's data is safely backed up by Amazon S3. Amazon S3 service is a good option, but based on the features it provides compared with IBM Cloud Object Storage, it is less suitable. IBM Cloud Object Storage is also integrated with more services, like IBM Cloud SQL and IBM Aspera, which AWS does not provide to transfer files at maximum speed in the world.

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Scaling up the number of users can lead to significant increases in licensing costs, which, while not a technical limitation, can be a practical constraint for some organizations

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• Saves a lot of Infra Costs
• Saves a lot of time in handling environment issues
• Easy to start a new instance

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• AWS VPCs are actively used for following various compliance and regulatory needs such as network separation for PCI DSS. The ability to keep resources and access to those resources controlled through the initial steps of creating VPCs has helped tremendously.

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• This allows us to recommend a platform to our clients that will quickly help them create new, efficient business processes with very little development.
• This saves clients hours and days of manual analysis of images, allowing the system to do the work when attaching Object Storage to models.
• There is a learning curve in utilizing the storage and the modeling, but once up and running, it works well during deployment.

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