Amazon SageMaker Review

A solution with great computational storage, has many pre-built models, is stable, and has good support

What is our primary use case?

I know about SageMaker and its capabilities, and what it can do, but I have not had any hands-on experience.

It's a machine learning platform for developers to create models.

What is most valuable?

There are pre-built solutions for everything. For example, if you want to build a deep learning model, we already have AlexNet, the internet, and all of the packages are inside. You don't have to recreate the same thing from scratch, but instead, you can use their models. You can use their model and use their data, then you can use your data.

I am a big fan of their computational storage capabilities. It's a relational database itself. It's a new SQL and you get different types of services. That is one of the best things that I like when doing my research.

I cannot quantify it as it is based on your requirements, but I can say that it's very flexible and you are able to increase all of the RAM and the GPU support.

They are doing a very good job on their end. They are evolving. I have learned that they have already integrated an IDE into Amazon SageMaker. They are doing a good job of evolving.

What needs improvement?

The pricing is complicated and should be simplified.

I would suggest that Amazon SageMaker provide free slots to allow customers to practice, such as a free slot to try out working with a Sandbox. This would be beneficial for newcomers, especially those who are getting into the cloud space. They could explore this area and get all of the aspects including data engineering, data recognition, and data transformation.

For how long have I used the solution?

I have been familiar with this solution for three months.

What do I think about the stability of the solution?

From my findings, it's quite stable.

Amazon promises that they will provide you with stability, and it is quite a stable platform.

If you are facing any issues it may be related to the computational storage capability that you opted for. For example, if you are opting for a full code row and you have a lot of data that is taking a lot of time, then you have to go back to retrieve it. That flexibility is within the AWS, but you have to bear the cost.

What do I think about the scalability of the solution?

It's quite scalable.

How are customer service and technical support?

The technical support is very good and I am satisfied with it.

Which solution did I use previously and why did I switch?

I researched Amazon SageMaker on my own.

How was the initial setup?

The initial setup is straightforward. It's not complex.

What's my experience with pricing, setup cost, and licensing?

The pricing is complicated as it is based on what kind of machines you are using, the type of storage, and the kind of computation. It is already decided, but if you want to have a look at how it is broken down or how they are calculating it, then they provide a tool where you can go and specify your options. These include what you want, how much storage, the RAM, and whether you want GPU support. You can include everything and then you can get the estimated cost.

AWS is an additional cost.

Which other solutions did I evaluate?

We are not with Anaconda Solutions, we use their packages. We are exploring their interface and it's capabilities. We are currently on a different tool, on a different platform. We are using their package managers to access the set of solutions deployed.

What other advice do I have?

I am not exposed to Amazon SageMaker but I know it's capabilities. I know exactly what we can do and how we can do it. We have been provided with several solutions for image processing, speech processing, and text processing. They have provided a built-in solution for every task. You can use tools for deploying your model, you just have to plug and play.

There is no cessation from what I can see. Whatever they have in the industry, they can solve 98% of the use cases.

There is also data engineering which is quite important. It's where the real work is done.

Amazon has already provided a free slot for each of the services that we have done. With Amazon SageMaker, however, I have not seen that.

I have not yet explored everything, but they are doing good work.

In terms of the dashboard, I can say that I have not explored the visualization aspect very much, but they have their tools. I don't know how flexible it is and how much customization you can do. That's something on the visualization side that I don't enjoy very much. My interests are mostly towards data engineering or data science.

I would rate this solution a nine out of ten.

Which deployment model are you using for this solution?

Public Cloud
**Disclosure: I am a real user, and this review is based on my own experience and opinions.
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