We performed a comparison between Google Vertex AI and PyTorch based on real PeerSpot user reviews.
Find out in this report how the two AI Development Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."We extensively utilize Google Cloud's Vertex AI platform for our machine learning workflows. Specifically, we leverage the IO branch for EDA data in Suresh Live Virtual, employing Forte IT for training machine learning models. The AI model registry in Vertex AI is crucial for cataloging and managing various versions of the models we develop. When it comes to deploying models, we rely on Google Cloud's AI Prediction service, seamlessly integrating it into our workflow for real-time predictions or streaming. For monitoring and tracking the outcomes of model development, we employ Vertex AI Monitoring, ensuring a comprehensive understanding of the model's performance and results. This integrated approach within Vertex AI provides a unified platform for managing, deploying, and monitoring machine learning models efficiently."
"Google Vertex AI is an out-of-the-box and very easy-to-use solution."
"Vertex AI possesses multiple libraries, so it eliminates the need for extensive coding."
"It provides the most valuable external analytics."
"The monitoring feature is a true life-saver for data scientists. I give it a ten out of ten."
"It's been pretty scalable in terms of using multiple GPUs."
"I like that PyTorch actually follows the pythonic way, and I feel that it's quite easy. It's easy to find compared to others who require us to type a long paragraph of code."
"The tool is very user-friendly."
"Its interface is the most valuable. The ability to have an interface to train machine learning models and construct them with the high-level interface, without excess busting and reconstructing the same technical elements, is very useful."
"The framework of the solution is valuable."
"yTorch is gaining credibility in the research space, it's becoming easier to find examples of papers that use PyTorch. This is an advantage for someone who uses PyTorch primarily."
"I've noticed that using chat activity often presents a broader range of options and insights for a well-constructed question. Improving the knowledge base could be a key aspect for enhancement—expanding the information sources to enhance the generation process."
"It would be beneficial to have certain features included in the future, such as image generators and text-to-speech solutions."
"Google Vertex AI is good in machine learning and AI, but it lacks optimization."
"The solution is stable, but it is quite slow. Maybe my data is too large, but I think that Google could improve Vertex AI's training time."
"I believe that Vertex AI is a robust platform, but its effectiveness depends significantly on the domain knowledge of the developer using it. While Vertex AI does offer support through the console UI in the Google Cloud environment, it is better suited for technical members who have a deeper understanding of machine learning concepts. The platform may be challenging for business process developers (BPDUs) who lack extensive technical knowledge, as it involves intricate customization and handling numerous parameters. Effectively utilizing Vertex AI requires not only familiarity with machine learning frameworks like TensorFlow or PyTorch but also a proficiency in Python programming. The complexity of these requirements might pose challenges for less technically oriented users, making it crucial to have a solid foundation in both machine learning principles and Python coding to extract the full value from Vertex AI. It would be beneficial to have a streamlined process where we can leverage the capabilities of Vertex AI directly through the BigQuery UI. This could involve functionalities such as creating machine learning models within the BigQuery UI, providing a more user-friendly and integrated experience. This would allow users to access and analyze data from BigQuery while simultaneously utilizing Vertex AI to build machine learning models, fostering a more cohesive and efficient workflow."
"On the production side of things, having more frameworks would be helpful."
"The training of the models could be faster."
"I've had issues with stability when I use a lot of data and try out different combinations of modeling techniques."
"PyTorch could make certain things more obvious. Even though it does make things like defining loss functions and calculating gradients in backward propagation clear, these concepts may confuse beginners. We find that it's kind of problematic. Despite having methods called on loss functions during backward passes, the oral documentation for beginners is quite complex."
"I would like a model to be available. I think Google recently released a new version of EfficientNet. It's a really good classifier, and a PyTorch implementation would be nice."
"There is not enough documentation about some methods and parameters. It is sometimes difficult to find information."
Google Vertex AI is ranked 3rd in AI Development Platforms with 5 reviews while PyTorch is ranked 11th in AI Development Platforms with 6 reviews. Google Vertex AI is rated 8.4, while PyTorch is rated 8.6. The top reviewer of Google Vertex AI writes "A user-friendly platform that automatizes machine learning techniques with minimal effort". On the other hand, the top reviewer of PyTorch writes "Offers good backward compatible and simple to use". Google Vertex AI is most compared with Azure OpenAI, Microsoft Azure Machine Learning Studio, Amazon SageMaker, Hugging Face and Replicate, whereas PyTorch is most compared with OpenVINO, MXNet, Microsoft Azure Machine Learning Studio, Google Cloud AI Platform and Hugging Face. See our Google Vertex AI vs. PyTorch report.
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