62 Neptune.ai Testimonials

"When working with projects that had thousands of runs, loading the interface and sorting through data was super slow in Weights & Biases. Neptune is better at handling large-scale. We’re happy with this choice."

“Within the first few tens of runs, I realized how complete the tracking was – not just one or two numbers, but also the exact state of the code, the best-quality model snapshot stored to the cloud, the ability to quickly add notes on a particular experiment. My old methods were such a mess by comparison.”

“The problem with training models on remote clusters is that every time you want to see what is going on, you need to get your FTP client up, download the logs to a machine with a graphical interface, and plot it. I tried using TensorBoard but it was painful to set up in my situation. With Neptune, seeing training progress was as simple as hitting refresh. The feedback loop between changing the code and seeing whether anything changed is just so much shorter. Much more fun and I get to focus on what I want to do. I really wish that it existed 10 years ago when I was doing my PhD.”

"No more DevOps needed for logging. No more starting VMs just to look at some old logs. No more moving data around to compare TensorBoards."

"I used Weights & Biases before Neptune. It’s impressive at the beginning, it works out of the box, and the UI is quite nice. But during the four years I used it, it didn’t improve —they didn’t fully develop the features they were working on. So I appreciate that Neptune has been noticably improved during the whole time I’ve been using it."

“Neptune allows us to keep all of our experiments organized in a single space. Being able to see my team’s work results any time I need makes it effortless to track progress and enables easier coordination.”

“What we like about Neptune is that it easily hooks into multiple frameworks. Keeping track of machine learning experiments systematically over time and visualizing the output adds a lot of value for us.”

“Neptune provides an accessible and intuitive way to visualize, analyze and share metrics of our projects. We can not only discuss it with other team members, but also with management, in a way that can be easily interpreted by someone not familiar with the implementation details. Tracking and comparing different approaches has notably boosted our productivity, allowing us to focus more on the experiments, develop new, good practices within our team and make better data-driven decisions. We love the fact that the integration is effortless. No matter what framework we use – it just works in the matter of minutes, allowing us to automate and unify our processes.”

"As our company has grown from a startup to a sizeable organization of 200 people, robust security and effective user management have become increasingly evident and vital."

"MLflow requires what I like to call software kung fu, because you need to host it yourself. So you have to manage the entire infrastructure — sometimes it’s good, oftentimes it’s not."

"Building something like a power line is a huge project, so you have to get the design right before you start. The more reasonable designs you see, the better decision you can make. Optioneer can get you design assets in minutes at a fraction of the cost of traditional design methods."

"We initially aimed for a GKE deployment for our experiment tracking tool. However, the other solution we explored had a rigid installation process and limited support, making it unsuitable for our needs. Thankfully, Neptune’s on-premise installation offered the flexibility and adjustability we required. The process was well-prepared, and their engineers were incredibly helpful, answering all our questions and even guiding us through a simpler deployment approach. Neptune’s on-prem solution and supportive team saved the day, making it a win for us."

"We use PyTorch Lightning, and it was just a matter of changing the tracker from Weights and Biases to Neptune. It’s like two lines of code. It’s actually quite easy."

"Self-hosted deployment for ML solutions will become more and more important. People don't feel comfortable with valuable intellectual property being stored in 3rd party DBs. For us, such deployment was too difficult and time-consuming in the previous solution. We could achieve that with Neptune, and it allowed us to close important deals that had stringent security requirements."

"We’ve got a few teams across different countries and different time zones and prior to Neptune, we were just shipping each other zips of like TensorBoard logs, so being able to see it all in space and it’s all just logged to the central area is really great and has helped us compare our results a lot faster and a lot more efficiently."