Amazon Tensor Flow vs. IBM watsonx.ai

A well-suited scenario for using AWS Tensor Flow is when having a project with a geographically dispersed team, a client overseas and large data to use for training. AWS Tensor Flow is less appropriate when working for clients in regions where it hasn't been allowed yet for use. Since smaller clients are in regions where AWS Tensor Flow hasn't been allowed for use, and those clients traditionally don't have enough hardware, this situation deters a wider use of the tool.

Incentivized

Based on my experience, I can recommend that you have a good AI management system in your company account, and if you have the money at your disposal to invest in IBM watsonx, do not hesitate. We are using API models to obviously build a work environment with sustainable flow as well. We have AI and ML lifecycle support.

Incentivized

• Amazon Elastic Compute Cloud (EC2) allows resizable compute capacity in the cloud, providing the necessary elasticity to provide services for both, small and medium-sized businesses.
• Tensor Flow allows us to train our models much faster than in our on-premise equipment.
• Most of the pre-trained models are easy to adapt to our clients' needs.

Incentivized

• it has many Reliable tools for algorithm modeling visualization.
• Highly secured, Integrated and all data optimized in one management
• Easily prepared and extract data from document.

Incentivized

• SageMaker isn't available in all regions. This is complicated for some clients overseas.
• For larger instances, when using a GPU, it takes a while to talk to a customer service representative to ask for a limit increase. Given this, it's recommendable to ask in advance for a limit increase in more expensive and larger cases; otherwise, SageMaker will set the limit to zero by default.
• Since the data has to be stored in S3 and copied to training, it doesn't allow to test and debug locally. Therefore, we have to wait a lot to check everything after every trail.

Incentivized

• APIs integration could be improved.
• steep learnings for tuning AI models
• performance lag

Incentivized

Microsoft Azure is better than Amazon Tensor Flow because it provides easier and pre-built capabilities such as Anomaly Detection, Recommendation, and Ranking. AWS is better than IBM Watson ML Studio because it has direct and prebuilt clustering capabilities AWS, like IBM Watson ML Studio, has powerful built-in algorithms, providing a stronger platform when comparing it with MS Azure ML Services and Google ML Engine.

Incentivized

IBM watsonx.ai is more enterprise oriented providing more options regarding on-premises setup and other compliance issues. Better suited for the corporate world.

Incentivized

• Positive: It has allowed us to work with our overseas teams without any large hardware investing.
• Positive: Pre-trained models significantly reduce the time to develop solutions for our clients.
• Negative: Since it's a relatively new tool, you have to be careful about not paying for large errors while learning to use the tool.

Incentivized

• We have already met our objectives in creating a supportive environment.
• This open-source tool increases the financial feasibility of the workflow.
• High price.

Incentivized