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External reviews

49 reviews
from G2

External reviews are not included in the AWS star rating for the product.


    Damon Wilder C.

Amazing Platform for an Enterprise Data Warehouse and Data Science Platform

  • November 11, 2021
  • Review provided by G2

What do you like best?
Incredible support for Data Science, Machine Learning and SQL use cases all in one platform.
What do you dislike?
Theoretically their SQL Engine may not scale, but I have not seem that at all.
What problems are you solving with the product? What benefits have you realized?
Predictive outcomes on cases, lead generation, optimization of marketing strategies and support for common tools like PowerBI.
Recommendations to others considering the product:
Why use proprietary APIs when you have such a complete and open solution with DataBricks.


    Computer Software

Good but UI is not perfect

  • August 26, 2021
  • Review provided by G2

What do you like best?
The ability to monitor and relaunch jobs from the phone is amazing. I don't need to be on my computer on the weekend to relaunch a failed job.
What do you dislike?
I don't particularly appreciate how the mlflow menu is hidden from the left bar UI unless I enter a specific link that contains the ID of an experiment.
For instance, I cannot see anything in https://*******.cloud.databricks.com/#mlflow and I cannot see mlflow in the left bar unless I go to :
https://*******.cloud.databricks.com/#mlflow/experiments/3817218/runs/172f6fcb9f144bbc93cc5b3b857c50f1
What problems are you solving with the product? What benefits have you realized?
We are using databricks for weekly ETL and neural network model training and tracking of results. We like the fact that we can use different cloud providers based on our needs.


    Chen S.

Data Scientist

  • July 16, 2021
  • Review provided by G2

What do you like best?
Experiment management, and model deployment.
What do you dislike?
Support for code engineering and version control.
What problems are you solving with the product? What benefits have you realized?
Predictive modeling.


    Omkar M.

Easy of use

  • July 07, 2021
  • Review provided by G2

What do you like best?
The easy of use is the most useful feature i like about MLFlow, it can use locally without any need for deployment on any server, great UI which allows use to search through general experiments and flexibility of changing data stores.
What do you dislike?
The UI design is in Django I think which is a bit laggy and slow improvement can be done on that side.
What problems are you solving with the product? What benefits have you realized?
We tried to provide our data store as a plug-in for MLFlow.
Recommendations to others considering the product:
Yes


    Information Technology and Services

Its very helpful when we train ml model for tracking

  • July 04, 2021
  • Review provided by G2

What do you like best?
Machine learning model tracking and find best weight
What do you dislike?
Add support for other programming language like cpp
What problems are you solving with the product? What benefits have you realized?
Tracking multiple training and find out best weights


    Health, Wellness and Fitness

MLFLow as a great addition to our ML focused data pipelines

  • July 03, 2021
  • Review provided by G2

What do you like best?
MLFlow has been instrumental in providing a decoupled interface between training and prediction part of our ML pipelines where we can store model metadata as part of training cycle and then in prediction we are able to leverage and pick model with highest ROC or just latest by chronological sorting and overall just do good job in tracking our experiments.
What do you dislike?
Sometimes slow to render in databricks environment as UI but that could be related to our databricks setup.
What problems are you solving with the product? What benefits have you realized?
MLFlow is very helpful in ability to do concurrent run of training and prediction as prediction doesn't have to wait for training to finish, we can just pick last successful experiment from MLFlow and leverage it for predictions.


    Financial Services

MLflow is a very useful open source tool

  • July 02, 2021
  • Review provided by G2

What do you like best?
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?
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 are you solving with the product? What benefits have you realized?
I am currently utilizing the MLflow MLflow tracking utility.
Recommendations to others considering the product:
Highly recommend this tool to the users.


    Education Management

Great tool, helped me alot.

  • July 02, 2021
  • Review provided by G2

What do you like best?
Centralization and remote servers and API
What do you dislike?
Nothing specific. I like everything about mlflow.
What problems are you solving with the product? What benefits have you realized?
experiment tracking and ML metadata


    Computer Software

MLflow makes ML life cycle management quite streamlined with easy implementation.

  • June 28, 2021
  • Review provided by G2

What do you like best?
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?
I think there could be improvements within the documentation over how to use MLflow within existing codebases.
What problems are you solving with the product? What benefits have you realized?
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


    Navisha S.

Mlflow is currently the most useful tool for tracking performance of machine learning models

  • June 28, 2021
  • Review provided by G2

What do you like best?
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?
There aren't any major downsides but the model training part according to me is still better run locally for comfortable experiments
What problems are you solving with the product? What benefits have you realized?
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