Amazon SageMaker enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning.
N/A
Splunk Enterprise
Score 8.6 out of 10
N/A
Splunk is software for searching, monitoring, and analyzing machine-generated big data, via a web-style interface. It captures, indexes and correlates real-time data in a searchable repository from which it can generate graphs, reports, alerts, dashboards and visualizations.
N/A
TensorFlow
Score 7.7 out of 10
N/A
TensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.
I have used Keras and MATLAB along with this. Also used Caffe and pyTorch sometimes, but all of them are not as powerful as TensorFlow. Keras is in good competition with TensorFlow but Keras won't allow you a lot of customization in your algorithms. And TensorFlow gives you the …
One major advantage of TensorFlow over Keras and other deep learning libraries is that it is the most powerful. It gives you power to write your own full customised algorithm that is not available in Keras. And it is fast too as compared to another tool as it can perform better …
It allows for one-click processes and for things to be auto checked before they are moved through the process but through the system. It also makes training easy. I am able to train users on the basic fundamentals of the tool and how it is used very easily as it is fully managed on its own which is incredible.
It's well suited for what I do, which is network security operations. And that's for anything from troubleshooting incidents, troubleshooting performance, troubleshooting for the purpose of a compliance and auditing. It's not best suited for users who are new in terms of they're new to the product and they have expectations that probably Splunk cannot meet.
TensorFlow is great for most deep learning purposes. This is especially true in two domains: 1. Computer vision: image classification, object detection and image generation via generative adversarial networks 2. Natural language processing: text classification and generation. The good community support often means that a lot of off-the-shelf models can be used to prove a concept or test an idea quickly. That, and Google's promotion of Colab means that ideas can be shared quite freely. Training, visualizing and debugging models is very easy in TensorFlow, compared to other platforms (especially the good old Caffe days). In terms of productionizing, it's a bit of a mixed bag. In our case, most of our feature building is performed via Apache Spark. This means having to convert Parquet (columnar optimized) files to a TensorFlow friendly format i.e., protobufs. The lack of good JVM bindings mean that our projects end up being a mix of Python and Scala. This makes it hard to reuse some of the tooling and support we wrote in Scala. This is where MXNet shines better (though its Scala API could do with more work).
It's very good for the hardcore programmer, but a little bit complex for a data scientist or new hire who does not have a strong programming background.
Most of the popular library and ML frameworks are there, but we still have to depend on them for new releases.
Theano is perhaps a bit faster and eats up less memory than TensorFlow on a given GPU, perhaps due to element-wise ops. Tensorflow wins for multi-GPU and “compilation” time.
We are using Splunk extensively in our projects and we have recently upgraded to Splunk version 6.0 which is quite efficient and giving expected results. We keep track of updates and new features Splunk introduces periodically and try to introduce those features in our day to day activities for improvement in our reporting system and other tasks.
You can literally throw in a single word into Splunk and it will pull back all instances of that word across all of your logs for the time span you select (provided you have permission to see that data). We have several users who have taken a few of the free courses from Splunk that are able to pull data out of it everyday with little help at all.
Splunk maintains a well resourced support system that has been consistent since we purchased the product. They help out in a timely manner and provide expert level information as needed. We typically open cases online and communicate when possible via e-mail and are able to resolve most issues with that method.
Community support for TensorFlow is great. There's a huge community that truly loves the platform and there are many examples of development in TensorFlow. Often, when a new good technique is published, there will be a TensorFlow implementation not long after. This makes it quick to ally the latest techniques from academia straight to production-grade systems. Tooling around TensorFlow is also good. TensorBoard has been such a useful tool, I can't imagine how hard it would be to debug a deep neural network gone wrong without TensorBoard.
The online course was simple clear and described the main capabilities of the solution. There is also an initial module that can be done for free so anyone can familiarize themselves with the functionality of this solution. On the other hand, however, there could be more free online courses. Maybe even with a certificate, this would broaden the group of people who are familiar with the platform while increasing familiarity with the solution itself.
Amazon SageMaker took the heavy lifting out of building and creating models. It allowed for our organization to use our current system for integration and essentially added on a feature to help all levels of Data scientists and IT professionals in our department and company as a whole. The training was simple as well.
I didn't get to fully evaluate Logstash as our corporation was already using Logstash, but both seemed like viable solutions to the problem that we were having. I wanted to evaluate Logstash some more, both did seem like they would work for the business needs that we had, we went with splunk as many teams were already using it.
Keras is built on top of TensorFlow, but it is much simpler to use and more Python style friendly, so if you don't want to focus on too many details or control and not focus on some advanced features, Keras is one of the best options, but as far as if you want to dig into more, for sure TensorFlow is the right choice
I don't have any numbers to share but Splunk has positively served as a 24/7 monitoring tool that has saved hours of work by self-detecting, saving statistics and alerting problems in the system or from external interfaces as soon as they happen.
Splunk dashboards does a solid job in collecting, analyzing data and creating reports that contain an entire day's activity and then automatically sent out to the business.
Splunk is very easy to learn and very useful to any program or business application.
