Within the SAP space, enterprises can decide to go one of two ways for their machine learning needs. On the one hand, SAP Leonardo offers users an integrated system that can handle all their needs for IoT, Machine Learning, and even blockchain. All of this is contained within an SAP native shell, making it simple to integrate into enterprise-level projects. SAP Leonardo deals with digital transformation and doesn’t confine itself to the realm of machine learning.
On the other hand, TensorFlow dedicates itself to providing tools directly related to the field of machine learning. TensorFlow itself is an ML environment that allows users to build and test machine learning protocols. The built-in libraries, tools, and community resources that it offers will enable users to push machine learning boundaries in their projects. Building and Deploying ML models for use with SAP projects couldn’t be more natural. The depth of tools that TensorFlow offers enables developers to think big in their ML project development.
The Similarities in ML Using Both Platforms
Both tools are very good at putting together basic ML programs that function well. For SAP users, Leonardo offers access to libraries that deal with interaction for SAP objects natively. The templating that Leonardo offers gives users a way to get simple projects up and running faster than TensorFlow. Even so, both systems cater to using machine language to automate processes. TensorFlow’s integration with SAP might take a little more effort than Leonardo’s, but the results are similar. Both communities have quite a robust set of documentation and community support for their APIs. If you’re writing an app for an online casino on either system and need some insight into function calls, you have the same level of support regardless of which platform you use.
Significant Differences Between TensorFlow and SAP Leonardo
While both of these systems perform similar functions, they do have a lot of operational distinctness. SAP Leonardo should be familiar for most SAP users and uses a wide range of languages to build out functionality, including the familiar ABAP and HANA SQL Script. Data scientists can delve into Node.js and R if they so wish to code their applications. TensorFlow has a different subset of languages, including Python (the best-supported one currently), C++, Java, and even Go. A lot of programmers have reported that TensorFlow is a bit difficult to code for, however.
TensorFlow offers a whole slew of features, including support for Deep Learning and Statistical/Analytical tools. TensorFlow also ships with TensorBoard, a visualization tool that coders can use to visualize the learning process. Unfortunately, SAP Leonardo doesn’t have anything this in-depth to debug their machine learning code.
On the positive side, SAP Leonardo does come with built-in natural language processing (NLP). Thanks to this particular set of libraries, developers have access to a powerful conversational artificial intelligence. For TensorFlow, there’s no built-in NLP library, but you can put one together with a bit of work. Importing a few libraries from the existing Python database should give you enough of a framework for heavy lifting. From there, you can go on to code your NLP system yourself.
Which One Is Better?
The system you choose to use for your company’s machine learning solution depends entirely on which one your coders are more comfortable working in. TensorFlow offers a better all-around library system if you’re trying to build a unique and revolutionary ML approach. You may need to have the team code some wrappers for SAP data objects and conversion between them, but that’s a simple task.
Companies that have skilled ABAP coders might benefit more from using SAP Leonardo. While Leonardo doesn’t offer a lot of built-in libraries, it is easy to work within the standard SAP data space. If you’re going to be implementing your machine learning system alongside IoT and other digital transformation initiatives that use SAP as a backbone, then Leonardo may be far better than TensorFlow, simply because of its ease of use in this regard.