Deepnote
DeepnoteReviews from AWS customer
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Fantastic
What do you like best about the product?
* Real-time collaboration: I love that my whole team can edit the same notebook together, see each other’s cursors live, and chat right in the interface.
* Cloud-based environment: No setup hassles—everything runs in the cloud, so I don’t have to worry about local Python installs or dependency conflicts.
* Cloud-based environment: No setup hassles—everything runs in the cloud, so I don’t have to worry about local Python installs or dependency conflicts.
What do you dislike about the product?
Occasional slow load times: Notebooks can take a while to start up, especially if the project has many dependencies.
What problems is the product solving and how is that benefiting you?
* With Python, SQL, and R all in the same workspace, our data science team doesn’t have to switch platforms depending on the task. That consistency boosts our productivity.
* Spinning up a powerful GPU instance for model training right from the UI means I’m not stuck with my local laptop’s specs—and I only pay for what I use.
* Spinning up a powerful GPU instance for model training right from the UI means I’m not stuck with my local laptop’s specs—and I only pay for what I use.
good NCLC analytics tool
What do you like best about the product?
the team did a great job building an efficient interface where "conversational" features are not over-shadowing everything else and it has still quite good "deterministic" part.
What do you dislike about the product?
Tbh I really liked the tool and I don't see any obvious bugs, fails etc. I think it is a SOTA. software for user-friendly data-analytics. Maybe offer in future releases some nicely designed package for interpreting/analyzing correlations etc.?
What problems is the product solving and how is that benefiting you?
It didn't really solve any of my problems as I usually use raw python/sql for solving my problems. But the tool could help less technical people to quickly interpret and analyze data which I think is a big win
Powerful Easy to Use Notebook Environment
What do you like best about the product?
Deepnote is a genuinely useful application of AI in coding, in terms of predictive code suggestions I've never come across anything like it and the time it saves building out queries is exceptional. It was an easy set up and integration and it's an intuitive interface that doesn't take long to familiarise yourself with. The file structure makes it easy to quickly run ad hoc queries on once off data sets while the multiple integrations and project structure make bigger projects a breeze. Would quite literally now be lost without it.
What do you dislike about the product?
The only things I dislike really are smaller nitpicks rather than feature gaps. The menu to convert/duplicate code blocks is a little clunky and the "fix my code" AI is not as effective as the predictive piece.
What problems is the product solving and how is that benefiting you?
Within the analytics team it allows for collaborative working and we use it a lot for automation of weekly/monthly tasks
A Powerful and User-Friendly Tool for Daily Data Work
What do you like best about the product?
What I like most about Deepnote is that it feels like a supercharged version of Jupyter Notebooks. I use it daily at work to automate processes, connect to APIs, generate reports, and trigger actions. It’s very intuitive and user-friendly, even compared to other tools like Colab or Jupyter Lab. The built-in data visualizations are powerful and make analysis much easier. It was very easy to get started with—no complex setup or configuration was needed, so the ease of implementation was excellent. Everything just worked right out of the box.
On the few occasions I needed help, the customer support was responsive and helpful. Their team showed a great attitude and helped me resolve the issue quickly.
On the few occasions I needed help, the customer support was responsive and helpful. Their team showed a great attitude and helped me resolve the issue quickly.
What do you dislike about the product?
Overall, I’m very satisfied and haven’t had major issues. I did run into some difficulty integrating Google Sheets directly, but it was likely due to a lack of documentation or research on my part. I worked around it using the Google API. Also, while I don’t use the collaboration features much, they could be useful for team-based projects.
What problems is the product solving and how is that benefiting you?
Deepnote simplifies the process of developing and running Python-based workflows in the cloud without having to worry about local setup or infrastructure. It helps me stay productive by keeping my scripts, visualizations, and notes all in one place. This saves me time, reduces context switching, and makes it easier to track and reproduce my work. Its ability to handle API connections, data processing, and reporting in an integrated, notebook-style environment has streamlined many of my daily tasks.
Product Efficiency and Productivity
What do you like best about the product?
It efficiency feature. It make very easy to maintain ETL pipeline and share the projects with others.
What do you dislike about the product?
Sometime it takes too much time to run the scripts.
What problems is the product solving and how is that benefiting you?
It make the result of project very easy to share. I also use it for ETL purpose.
Deepnote is a game changer
What do you like best about the product?
The cloud based infrastructure allows our data science team to get answers much more quickly and collaborate much more effectively. As a sponsor of the technology, I highly recommend it
What do you dislike about the product?
There is not much room for improvement. Speed enhancements are always welcome
What problems is the product solving and how is that benefiting you?
Deepnote allows us to analyse our data much more effectively
More than just a managed Jupyter notebook
What do you like best about the product?
It's a combination of lots of little things: pre-configurable integrations (e.g. database connections) that drop into the flow as executable blocks, excellent data visualisation tools that let you interact with the data with almost no cognitive overhead, and the ability to schedule runs / build interfaces for easy workflow automation.
What do you dislike about the product?
It can be a bit aggressive about spinning down your instances
What problems is the product solving and how is that benefiting you?
Deepnote replaces a local Jupyter environment as a swiss army knife to solve data wrangeling, automation and visualisation problems
Make easier your process with and from data
What do you like best about the product?
Powerfull tool to make analysis, create control tables, execute process and alerts. Easy to use
What do you dislike about the product?
The cost, so i have to control how many people has access to build with it
What problems is the product solving and how is that benefiting you?
complex data analysys
Alerts and some process automation
Alerts and some process automation
Exceptional collaborative data science platform that revolutionized our workflow
What do you like best about the product?
What I love most about Deepnote is its seamless real-time collaboration features combined with its intuitive interface. The ability to work simultaneously with team members on Jupyter notebooks while having built-in version control and commenting systems has completely transformed how we approach data science projects. The platform's integration with popular data sources and cloud services, along with its powerful compute resources, makes it incredibly easy to go from data exploration to production-ready insights without the usual infrastructure headaches.
What do you dislike about the product?
While Deepnote is fantastic overall, there are a few minor areas for improvement. The pricing can become steep for larger teams or when requiring extensive compute resources for extended periods. Occasionally, I've experienced slight lag during peak usage times, and some advanced customization options that power users might expect from local Jupyter environments are still limited.
What problems is the product solving and how is that benefiting you?
The cumulative effect is that I can now focus on the actual data science work rather than fighting with tools and infrastructure. My productivity has increased significantly, and collaboration with my team has become seamless and enjoyable rather than a source of friction.
Nice idea, but to many bugs and missing features
What do you like best about the product?
Concept and Idea: The overall concept of ReView is appealing and innovative. It attempts to address key pain points in notebook and task management.
User Interface: Initially, the UI appears intuitive, visually appealing, and user-friendly, creating a positive first impression.
User Interface: Initially, the UI appears intuitive, visually appealing, and user-friendly, creating a positive first impression.
What do you dislike about the product?
Lack of Professional-Grade Features: Over prolonged usage, it's evident that ReView isn't fully tailored for professional environments, particularly IT professionals and engineering teams.
GPU Utilization Visibility: There's no capability to monitor GPU usage for notebooks running on specialized GPU instances, which is critical for machine learning tasks.
Versioning Issues: Proper version control mechanisms are insufficient, posing difficulties in maintaining and managing notebook iterations effectively.
Authentication Problems: OpenID Connect authentication for AWS integration doesn't function, significantly hindering cloud-based workflows.
Instability of Task Execution Order: Tasks don't consistently execute in the intended order, forcing repetitive executions of notebooks. This is particularly detrimental for iterative machine learning processes.
GPU Utilization Visibility: There's no capability to monitor GPU usage for notebooks running on specialized GPU instances, which is critical for machine learning tasks.
Versioning Issues: Proper version control mechanisms are insufficient, posing difficulties in maintaining and managing notebook iterations effectively.
Authentication Problems: OpenID Connect authentication for AWS integration doesn't function, significantly hindering cloud-based workflows.
Instability of Task Execution Order: Tasks don't consistently execute in the intended order, forcing repetitive executions of notebooks. This is particularly detrimental for iterative machine learning processes.
What problems is the product solving and how is that benefiting you?
Training ML Models
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