Amazon Tensor Flow vs. C5i Discovery

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

Course5 Discovery was able to help my organization with cross-functional data by monetizing it. Our data was transformed into augmented dashboards which are easy to navigate - I've never used a platform like this, so I found that this solution minimal enough for a beginner, but also intelligent enough for a seasoned professional. Course5 Discovery was able to curate personal stories around our data which allowed us to make better business decisions because we were basing them on facts and analytics. More importantly, these analytics are presented in a way that makes sense! The insights provided to our business were a mix of facts that were both predictive and prescriptive, which brought out the business to the next level. We no longer have to take the time to run endless reports, and then spend hours trying to decipher them. Instead, with Course5 Discovery all the information, data, and insights we need are right in front of us when we need it most.

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

• Data Visualization.
• Predictive Modeling.
• Trend Analysis.
• Data Analysis.

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

• Multi-Platform use.
• Mobile Suport.
• Customization.

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

• 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 saved time because the analytics were presented in a way that made sense.
• Return on Investment.
• Scalability as a company.

Incentivized