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Its very helpful when we train ml model for tracking
What do you like best about the product?
Machine learning model tracking and find best weight
What do you dislike about the product?
Add support for other programming language like cpp
What problems is the product solving and how is that benefiting you?
Tracking multiple training and find out best weights
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MLflow is a very useful open source tool
What do you like best about the product?
MLflow tracking has been a major advantage for keeping up the record of the results of the experiments we carry out on the data using different parameters. Tracking the results and parameters is very iseful for achieving the most optimized solution.
What do you dislike about the product?
One small counter point is that it is not an easy tool and requires all in depth knowledge for making the best use of it.
What problems is the product solving and how is that benefiting you?
I am currently utilizing the MLflow MLflow tracking utility.
Recommendations to others considering the product:
Highly recommend this tool to the users.
MLflow makes ML life cycle management quite streamlined with easy implementation.
What do you like best about the product?
I like how it forces the developer to follow a certain code style which can basically help maintain the codebase much easily over time and have a proper documentation over it.
What do you dislike about the product?
I think there could be improvements within the documentation over how to use MLflow within existing codebases.
What problems is the product solving and how is that benefiting you?
My manager wanted to have a visual UI to track and monitor ml projects and metrics and also be able to import a model quickly and try it out, mlflow makes it really easy to do that.
Recommendations to others considering the product:
Read through the documentation
Mlflow is currently the most useful tool for tracking performance of machine learning models
What do you like best about the product?
It's very useful when it comes to tracking performance of machine learning models. Acts like a dashboard that would otherwise have to be built from scratch.
What do you dislike about the product?
There aren't any major downsides but the model training part according to me is still better run locally for comfortable experiments
What problems is the product solving and how is that benefiting you?
There are some generic models that are used and needs to be kept a track of the performance of the model with respect to recent data. And if the model performance is declining we retrain the model using mlflow
Easy and fun to use
What do you like best about the product?
I learned to datamine with Python on Databricks and I use it daily. It is a nice software, user friendly and easy to connect to multiple sources
What do you dislike about the product?
The errors can be a little more explanatory than what it is currently.
What problems is the product solving and how is that benefiting you?
Helping the client make business decisions using the purchase and engagement data on the Azure Cloud
Best Open-Source Platform to calibrate the models with keeping tracking of the experiments and store
What do you like best about the product?
One please with all nessarery fetaure.
1. tracking
2. storage of models and files related documentation like log, config file.
3. validation of the model with meteric feature and plot crossponding to it and genrate the report from the experiments.
4. Data callibation with data 🧱, SQL and other cloud providers.
1. tracking
2. storage of models and files related documentation like log, config file.
3. validation of the model with meteric feature and plot crossponding to it and genrate the report from the experiments.
4. Data callibation with data 🧱, SQL and other cloud providers.
What do you dislike about the product?
Anything which dislike is nothing tell yet, But If we can build something like feature where we can do more advanced anlaytics crossponding to parameters and meterics and generate the different plot from that tabuler data, like we have d-tale (github: https://github.com/man-group/dtale), beacuse I was running some simulation run with differnet experiments and then write the report crossponding with the experiments and with differnet signnificant plot, demonstrates the report write up.
What problems is the product solving and how is that benefiting you?
I was running some simulation run with differnet experiments and then write the report crossponding with the experiments and with differnet signnificant plot, demonstrates the report write up.
Perfect code sharing repository
What do you like best about the product?
Having a platform to share codebase with team members and run machine learning models on the cloud.
What do you dislike about the product?
Sometimes we have to restart clusters to fix memory errors, which leads to data loss.
What problems is the product solving and how is that benefiting you?
Collaboration on code among team members. Running applications on the cloud.
Brilliant on developing the best collaborative platform for data scientists and data engineers
What do you like best about the product?
An interface that is better than Jupyter notebooks that allows SQL, Scala, PySpark, Python, R and the ability to collabortate on notebooks
What do you dislike about the product?
DPU based billing is fixed and minimum is 3 node cluster. For a small entity the advantages of using AWS Glue interface to Spark 2.x outweighs the benefits of a permenant cluster runnig with Databricks.
What problems is the product solving and how is that benefiting you?
Big data management in lake type architecture using Parquet formats, PySpark developments and enhancements.
Great platform for our Big Data needs
What do you like best about the product?
Easy administration, easy to create jobs from notebooks, great development environment, new and exciting features coming.
What do you dislike about the product?
Taking away our dedicated customer service rep and replacing this with just a support GUI.
What problems is the product solving and how is that benefiting you?
All our data pipelines are on Databricks. Benefitted from improved performance on Spark.
One stop shop for all your data problems
What do you like best about the product?
It has got everything in it. IDE, Version Control, Scheduling whatnot.
What do you dislike about the product?
I didn't find something that discomforts me yet.
What problems is the product solving and how is that benefiting you?
Currently, I'm using it as an ETL tool. It's easy to use and connects with any data source—excellent documentation and help from the community.
Recommendations to others considering the product:
Just go for it. You can do many things you want to do with your data.
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