What users are saying about

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92 Ratings

Amazon SageMaker

6 Ratings
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Score 7.4 out of 101

IBM Watson Studio<a href='https://www.trustradius.com/static/about-trustradius-scoring' target='_blank' rel='nofollow'>Customer Verified: Read more.</a>

92 Ratings
<a href='https://www.trustradius.com/static/about-trustradius-scoring' target='_blank' rel='nofollow'>trScore algorithm: Learn more.</a>
Score 7.4 out of 101

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Likelihood to Recommend

Amazon SageMaker

SageMaker is great for serving Jupyter notebooks, particularly if you already use other AWS products, such as S3. SageMaker's model retraining function is useful if you write a few Lambda functions to invoke jobs. Its model serving function is useful if your team has limited resources and is willing to submit to SageMaker's opinions.
Gavin Hackeling profile photo

IBM Watson Studio

Data science ideation and POC is definitely a sweet spot in my opinion of DSX. It is easy to get up and running and can elevate people that have the business knowledge but lack some of the senior science skills to be proficient analytics users.

Moving the models developed to a production ready model is not an easy path and often taking the analytics idea to a product involves translating the method and approach to other tools.
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Pros

  • SageMaker is useful as a managed Jupyter notebook server. Using the notebook instances' IAM roles to grant access to private S3 buckets and other AWS resources is great. Using SageMaker's lifecycle scripts and AWS Secrets Manager to inject connection strings and other secrets is great.
  • SageMaker is good at serving models. The interface it provides is often clunky, but a managed, auto-scaling model server is powerful.
  • SageMaker is opinionated about versioning machine learning models and useful if you agree with its opinions.
Gavin Hackeling profile photo
  • Standard software packages (python and R) are available and ready to run.
  • Data from various sources (e.g. external databases) accessed and loaded from DSx.
  • Customer support provides valuable guidance and helps to solve problems.
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Cons

  • SageMaker does not allow you to schedule training jobs.
  • SageMaker does not provide a mechanism for easily tracking metrics logged during training.
  • We often fit feature extraction and model pipelines. We can inject the model artifacts into AWS-provided containers, but we cannot inject the feature extractors. We could provide our own container to SageMaker instead, but this is tantamount to serving the model ourselves.
Gavin Hackeling profile photo
  • An actual IDE for python would be very helpful.
  • Some python packages were not up-to-date and it was not possible to install the current version.
  • It should be somehow possible to monitor the used resources and system load (CPU/RAM).
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Likelihood to Renew

No score
No answers yet
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IBM Watson Studio8.2
Based on 1 answer
because we find out that DSX results have improved our approach to the whole subject (data, models, procedures)
Davide Tognon profile photo

Usability

No score
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IBM Watson Studio9.2
Based on 2 answers
The UI flawlessly merges this offering by providing a neat, minimal, responsive interface
Isaiah King profile photo

Reliability and Availability

No score
No answers yet
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IBM Watson Studio8.2
Based on 1 answer
From time to time there are services unavailable, but we have been always informed before and they got back to work sooner than expected
Davide Tognon profile photo

Performance

No score
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IBM Watson Studio8.2
Based on 1 answer
Never had slow response even on our very busy network
Davide Tognon profile photo

Support

No score
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IBM Watson Studio8.2
Based on 1 answer
I received answers mostly at once and got answered even further my question: they gave me interesting points of view and suggestion for deepening in the learning path
Davide Tognon profile photo

In-Person Training

No score
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IBM Watson Studio8.2
Based on 1 answer
The trainers on the job are very smart with solutions and very able in teaching
Davide Tognon profile photo

Online Training

No score
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IBM Watson Studio8.2
Based on 1 answer
The Platform is very handy and suggests further steps according my previous interests
Davide Tognon profile photo

Implementation

No score
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IBM Watson Studio7.3
Based on 1 answer
It surprised us with unpredictable case of use and brand new points of view
Davide Tognon profile photo

Alternatives Considered

We have not invested in another machine learning software at this time and so far this has proved very successful with our machine learning teams. As mentioned, I am training these individuals simply on the fundamentals of the software and using it/customizing it for their needs. It has been very easy to do this and has gotten great reviews across the organization so far.
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We did not evaluate comparable platforms. The customer suggested using DSx.
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Scalability

No score
No answers yet
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IBM Watson Studio8.2
Based on 1 answer
It helped us in getting from 0 to DSX without getting lost
Davide Tognon profile photo

Return on Investment

  • We have been able to deliver data products more rapidly because we spend less time building data pipelines and model servers.
  • We can prototype more rapidly because it is easy to configure notebooks to access AWS resources.
  • For our use-cases, serving models is less expensive with SageMaker than bespoke servers.
Gavin Hackeling profile photo
  • Positive impact: Reduces effort significantly.
Bhaumik Pandya profile photo

Pricing Details

Amazon SageMaker

General
Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No
Additional Pricing Details

Amazon SageMaker More Information

IBM Watson Studio

General
Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No
Additional Pricing Details

IBM Watson Studio More Information