62 Neptune.ai Testimonials

"With Neptune, I have a mature observability layer to access and gain all the information. I can check any model’s performance very quickly. It would take me around a minute to figure out this information. I don’t have to go deeper and waste a lot of time. I have the results right in front of me. The time we have gained back played a significant part."

"The killer feature in Neptune is custom dashboards. Without this, I wouldn’t be able to communicate my simulations to Developers, the Analytics team, and business stakeholders without any hassle. Neptune gives our Data Scientists the piece of mind that their best results won’t be lost and that communication will be a breeze."

"Clearly, handling the training of more than 7000 separate machine learning models without any specialized tool is practically impossible. We definitely needed a framework able to group and manage the experiments."

“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.”

“Indeed it was a game-changer for me, as you know AI training workloads are lengthy in nature, sometimes also prone to hanging in colab environment, and just to be able to launch a set of tests trying different hyperparameters with the assurance that the experiment will be correctly recorded in terms of results and hyper-parameters was big for me.”

"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."

“I had been thinking about systems to track model metadata and it occurred to me I should look for existing solutions before building anything myself. Neptune is definitely satisfying the need to standardize and simplify tracking of experimentation and associated metadata. My favorite feature so far is probably the live tracking of performance metrics, which is helpful to understand and troubleshoot model learning. I also find the web interface to be lightweight, flexible, and intuitive.”

"One of the biggest challenges [we had] was managing the pipelines and the process itself because we had 40 to 50 different pipelines. Depending on the exact use case or what kind of data we’d like to output, we could have different combinations for running them to get different outputs. So basically, the entire system isn’t so simple."

"I like the dashboards because we need several metrics, so you code the dashboard once, have those styles, and easily see them on one screen. Then, any other person can view the same thing, so that’s pretty nice."

"I’ve used Neptune from 2019, first for my personal projects and now within the company. During this time, I saw changes and improvements in UI, but also performance and reliability. But at the same time, I always appreciated that it never became too cluttered with too many things. It’s straight to the point and it’s very effective in what it does."

"We use Neptune for keeping track of all our research work and monitoring of on-going model training. Since everything is tracked in Neptune it is super easy to keep track of what we did, how we did it, and what the results were. It makes it a lot easier also direct future research directions."

"Neptune’s UI is highly configurable, which is way better than MLflow."

"I really appreciate that I’ve never seen any outage in Neptune. And since we’re training an LLM, that it’s super critical to not have any outages in our loss curve. Other than that, there are things you often take for granted in a product: reliability, flexibility, quality of support. Neptune nails those and gives us the confidence."

"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."