Comet - Licensing only

What do you like best about the product?

It is very easy to setup you can setup it in very few lines of code and you can autolog in a minute it work across many python ml libraries like skitlearn and the code environment and parameter are saved automatically saved after one run of ml model.

What do you dislike about the product?

It is mainly python centric it supports less other languages like ruby kotlin and java and the cost of team and enterprise edition camn get very expensive as compared to any open source option like github.

What problems is the product solving and how is that benefiting you?

Building an ml in this day to day life is not same as writing normal software, the same code give same output but in ml the result depends upon the code , data which you upload what setting you setup some randomness and depends upon which environment you are working and you can change all of these constantly while experimenting the ai but when we use this comet ml platform, it make a proper environment for a team so it has same data setting in an environment which improves the quality of ml model as compared to a normal software

What do you like best about the product?

The best part is when we build a model in a team, it helps us check which team member is working on which part of the model, reducing time and duplication.

What do you dislike about the product?

The most disliked part of this software/platform is that we can't predict the price and the amount of credits that will be used on a project, leading to high usage and platform fees that are not easily affordable by a normal organization.

What problems is the product solving and how is that benefiting you?

When working on traditional software to create a model like GitHub, we can't identify which part of the work is being done by which member, leading to long hours and reducing our team's potential. Comet ML solves the problem of duplication among team members.

What do you like best about the product?

It helps us in data science projects to track, compare, visualize, and optimize machine learning experiments and LLM workflows in one place, and you can integrate Comet into your training script by adding just a few lines of code.

What do you dislike about the product?

It has paid tiers and usage fees in billing, such as extra usage of Opik or per user seat licenses, which increase the cost to use it and rise with an increase in team members and growing data.

What problems is the product solving and how is that benefiting you?

In engineering teams, team members often work individually on their local machines without a centralized hub, causing duplication of work when two members do the same task, leading to time loss. However, when we use Comet.ml, it helps team members track who is working on which project and then work accordingly.

What do you like best about the product?

Before Comet, the “best” model lived in someone’s memory or an old notebook name. What I like is that every serious run leaves a record—metrics, parameters, and context—in one place, so we can find what actually worked instead of re-running guesses. Comparing a few candidates takes minutes, and handing a result to someone else does not depend on them recreating my local setup.

What do you dislike about the product?

If the team logs inconsistently, the workspace gets noisy. You still need light discipline on what to track and how to name projects. It will not fix a messy process by itself.

What problems is the product solving and how is that benefiting you?

It solves the lost-experiment problem: results scattered across machines and chat threads. The benefit is faster decisions and less repeated work, because we can see prior runs and build on them instead of starting from scratch each time.

What do you like best about the product?

The best thing about Comet.ml is that it takes a lot of the manual effort out of experiment tracking. It's easy to see how different model runs performed, and the dashboards make it simple to spot trends without digging through logs. It has definitely helped make my workflow more organized. The automatic logging of metrics, parameters, and training runs has also made it much easier to compare experiments

What do you dislike about the product?

Although Comet integrates with major frameworks like PyTorch and TensorFlow, highly customized ML pipelines actually require additional logging and instrumentation work which is not good

What problems is the product solving and how is that benefiting you?

Since my work involves People Consulting, competency frameworks, working with large datasets of JDs/ competencies, using comet helps me in bringing more structure to this process as I can track experiments and their results in 1 place instead of relying on scattered files or notes or manually maintained records.

The biggest benefit is visibility and reduction of time spent manually.

What do you like best about the product?

I really like that Comet.ml allows me to track experiment metrics, parameters, model versions, and training outputs all in one place. When I make changes to features or model parameters, comparing runs becomes easy-eliminating the need to take separate notes or maintain spreadsheets. The dashboard also lets me see at a glance which experiment is performing better.

What do you dislike about the product?

The platform is packed with features. Because of this, I initially found the user interface a bit cluttered. It can take some time to learn how to organize logging effectively, and setting things up for large projects might also require some time.

What problems is the product solving and how is that benefiting you?

Avoid losing track of your machine learning experiments with Comet.ml. We often test multiple models, datasets, and hyperparameters simultaneously. Comet.ml enables us to keep a record of what was changed and the resulting output, allowing for quick and efficient model comparisons. It is also excellent for collaboration, as others can see previous experiments without any extra effort.

What do you like best about the product?

It gives good visibility into experiments, metrics, and model performance without making the workflow complicated.

What do you dislike about the product?

The main thing I would improve is the learning curve for new users. Some features and settings can take a little time to understand, especially when managing a large number of experiments. The overall experience is good, but the initial setup and navigation could be more straightforward.

What problems is the product solving and how is that benefiting you?

Comet.ml helps solve the problem of keeping machine learning experiments organized and easy to compare. Instead of manually tracking different runs, metrics, and model versions, everything can be monitored in one place. This makes it easier to understand what is working, compare experiments, and avoid losing track of previous results. It saves time and makes the overall ML workflow more manageable.

What do you like best about the product?

What I like most about Comet.ml is how easy it is to track experiments and how clearly it visualizes model performance. It makes it straightforward to compare runs, monitor metrics, keep results organized, and collaborate with team members. Overall, it helps make my machine learning workflow more efficient and reproducible.

What do you dislike about the product?

The main thing I dislike is that Comet.ml can have a bit of a learning curve for new users. Some of the more advanced features and dashboard configurations can feel overly complex, especially when you’re managing a large number of experiments. A simpler interface, along with more customization options, would make the overall experience better.

What problems is the product solving and how is that benefiting you?

Comet.ml helps solve the challenge of managing and tracking machine learning experiments, including parameters, metrics, datasets, and model versions. It lets me compare experiments more easily, reproduce successful results, and collaborate better with others. Overall, it saves time, reduces the need for manual tracking, and makes the ML development process more organized and efficient.

What do you like best about the product?

What I like most about Comet is that it keeps the entire ML experimentation process organized in one place. It makes it easy to track experiments, compare different runs, and see how changes in parameters or code affect model performance. I also like the visualization and model versioning capabilities because they make it much easier to understand results and reproduce successful experiments. For LLM and AI projects, the Opik capabilities are also useful for tracing and evaluating model and agent behavior.

What do you dislike about the product?

The main downside for me is that Comet has quite a lot of features, so it can take some time to understand the platform and decide which features are actually needed for a particular project. The interface can also feel a little overwhelming when managing a large number of experiments, metrics, and artifacts. A simpler onboarding experience and more streamlined navigation would make it easier for new users to get started.

What problems is the product solving and how is that benefiting you?

Comet helps solve the problem of managing and reproducing machine learning experiments as projects become more complex. Instead of manually keeping track of parameters, metrics, code versions, datasets, and models, everything can be linked and tracked in one place. This makes it much easier to compare experiments, identify what worked, reproduce results, and collaborate with other team members. It also provides useful observability and evaluation capabilities for LLM and AI applications through Opik

What do you like best about the product?

The best way for a beginner developer to understand ML and LLMs.

What do you dislike about the product?

Being more transparent leads to security threats.

What problems is the product solving and how is that benefiting you?

Supports entire development project using ML