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Empowering Collaboration and Efficiency in Data Science Workflows
What do you like best about the product?
Collaborative Environment
Reproducibility
Integration with Tools
Scalability
Automation and Workflow Orchestration
Security and Compliance Features
Model Deployment and Monitoring
User-Friendly Interface
Customer Support
Ease of Implementation
Frequency of use
Reproducibility
Integration with Tools
Scalability
Automation and Workflow Orchestration
Security and Compliance Features
Model Deployment and Monitoring
User-Friendly Interface
Customer Support
Ease of Implementation
Frequency of use
What do you dislike about the product?
Learning Curve
Cost: Some users may express concerns about the cost
Customization Challenges: Depending on specific use cases, users might face challenges in customizing certain aspects of the platform to align with their unique requirements
Cost: Some users may express concerns about the cost
Customization Challenges: Depending on specific use cases, users might face challenges in customizing certain aspects of the platform to align with their unique requirements
What problems is the product solving and how is that benefiting you?
Increased Collaboration: Centralized collaboration features enhance communication and teamwork among data science teams.
Improved Reproducibility: Version control and experiment tracking contribute to the reproducibility of machine learning experiments.
Enhanced Scalability: The ability to scale resources and handle larger datasets supports the growth of machine learning projects.
Efficient Deployment and Monitoring: Streamlined model deployment and effective monitoring contribute to the successful integration of machine learning models into production.
Flexibility and Integration: Integration with diverse tools allows data scientists to work with familiar frameworks and libraries.
Improved Reproducibility: Version control and experiment tracking contribute to the reproducibility of machine learning experiments.
Enhanced Scalability: The ability to scale resources and handle larger datasets supports the growth of machine learning projects.
Efficient Deployment and Monitoring: Streamlined model deployment and effective monitoring contribute to the successful integration of machine learning models into production.
Flexibility and Integration: Integration with diverse tools allows data scientists to work with familiar frameworks and libraries.
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The experience was easy and really good
What do you like best about the product?
Simple user interface and great customer support
What do you dislike about the product?
Some of the operations are difficult to manage
What problems is the product solving and how is that benefiting you?
We were creating some gpt's for our product
Perfect platform for AI, ML and data science
What do you like best about the product?
They provide the best AI, ML, data science solutions for various applications. I personally used their tools integrated in mathworks and anaconda. The users are now able to access various python ML pakages and R pakages in anaconda. Data science became more easy with those R pakages. In matlab, this platform helps a lot to run simulations easily. Moreover all the pakages are extremly fast and seamless.
What do you dislike about the product?
I feel the prices are high which can be reduced. The integration of domino with mathworks or anaconda can be made easier with less customization effort. The tools can be made handyy for a layman.
What problems is the product solving and how is that benefiting you?
I mainly integrated domino with anaconda and matlab. It provides a lot of ML/Data science/R pakages which is very helpful. In case of matlab, it helps a lot of simulation seamless. The main usecase is that it comes with the bundle of all previously implemented libraries.
Best use for machine learning
What do you like best about the product?
It make things easy to train the AI with machine learning, which was complicat to train.
What do you dislike about the product?
Minor issues when I start to use this platform earlier.
What problems is the product solving and how is that benefiting you?
I was using other platform to train and deploy the AI, the quality of data is not upto the mark which requires to rework on it. MLops platform performance so good to deploy.
An Overview of Domino Enterprise MLOps Platform
What do you like best about the product?
Domino's platform has user friendly interface that makes it easier to collaborate with teams and deploy models. The centralized environment helps in efficiently managing all the functionalities. Seamless integration with data science tools and cloud platforms.
What do you dislike about the product?
Complex implementation process for larger companies. Integration with third party applications require additional customizations. Customer support and training documents can be better. Needs adequate knowledge to work on this platform, hence not beginner friendly.
What problems is the product solving and how is that benefiting you?
Collaboration features for team interaction with good interface simplified workflows. Personalised dashboards and customizable features helps users to enhance the overall productivity. Improved performance with various data tools and cloud platforms. Restructuring training documents gives more knowledge in understanding of the platform.
Unleashing Enterprise AI with the robust MLOps
What do you like best about the product?
A comprehensive platform that seamlessly integrates the various stages of the machine learning lifecycle. Eases the collaboration among data scientists, engineers, and other stakeholders. Robust and scalable platform with simple user-friendly interface that caters to both data scientists and IT professionals, regardless of their technical expertise.
What do you dislike about the product?
The learning curve for new users can be high especially for those unfamiliar with similar MLOps platforms. It also lacks integrations with new AI/ML platforms.
What problems is the product solving and how is that benefiting you?
Domino's automation capabilities helped in rapidly deploying new models when needed. It also has provided visibility to other teams working on MLOps.
Very constricted and not tech friendly
What do you like best about the product?
Greta integration for optimal creation of AI workflows. Helps to structurize ML platforms well.
What do you dislike about the product?
Very Constricted to use and not many integrations are present. Workflow integrations are very complicated.
What problems is the product solving and how is that benefiting you?
Provides a central MLOps platform to see all the Algorithms & how are functioning. A centralized system helps keep the productivity up.
Great Central System for productive tracking.
What do you like best about the product?
The platform enables an easily copyable agile ML lifecycle for high AI impact. Moreover, it has a very good cost control feature.
What do you dislike about the product?
Constricts creativity and productivity in a nutshell.
What problems is the product solving and how is that benefiting you?
Its a central ML algorithm platform which explains in a nutshell all the Algorithms that are in production and their performance.
Domino Entreprise good tool for MLOps
What do you like best about the product?
Unified platform for all the persona and can cater hybrid cloud environment
What do you dislike about the product?
Domino platform is good but can add more integration from usage point of view
What problems is the product solving and how is that benefiting you?
Used primarily for end to end Machine learning ops activities which include model training, testing, model retraining etc
Domino Enterprise MLops platform
What do you like best about the product?
Domino Enterprise MLops platform is a unified platform for data science and analytics. It helps to boost team productivity and minizine costs. It's self serve AI Infrastructure is terribly fast and can scale upto lots of clusters depending on the workload. The Domino MLOps Integration actually accelarates the time to production at scale.
What do you dislike about the product?
Although Domino provides a good insight to their MLOps integrated platform, As a new user or beginner for generative-AI, it's challenging to understand the implementation steps to deploy the model to public cloud and monitor the models. Also it would be helpful if their service or customer support team provides guide for one click CICD workflow to deploy models.
What problems is the product solving and how is that benefiting you?
Domino MLOps platform provides unified model registry where it allows to track all models regardless of where they were trained. Also it allows to schedule batch jobs to enrich Data at rest for analytics and also mitigate potential security risk with automated workflows with regulatory controls.
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