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One stop place for setting up my entire data analysis pipeline
What do you like best about the product?
It offers multi-cloud support across AWS, GCP, and Azure
New features are aggressively released every quarter.
The UI is relatively user-friendly compared to AWS EMR or other similar products
New features are aggressively released every quarter.
The UI is relatively user-friendly compared to AWS EMR or other similar products
What do you dislike about the product?
Errors are not entirely straightforward sometimes.
Regular Maintenance can sometimes cause downtime or failure, which can be solved with proper scheduling and retry mechanisms.
Regular Maintenance can sometimes cause downtime or failure, which can be solved with proper scheduling and retry mechanisms.
What problems is the product solving and how is that benefiting you?
It is higly efficient in executing queries, analyzing data, performing complex table joins if workloads are properly setup across high-performing clusters.
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Lakehouse helps solving Big data , streaming and Analytical problem
What do you like best about the product?
Delta Lake , SQL Analytics , Optimized Photon engine
What do you dislike about the product?
Notebook UI , performance for SQL analytics on huge volume on the fly aggregate calculations
What problems is the product solving and how is that benefiting you?
Big data problem, ML , SQL analytics , Streaming and replacing traditional Oracle Datawarehouse
Recommendations to others considering the product:
Yes highly recommended for setting up Hadoop kind of cluster and dealing with Big data and Analytics and Machine learning ML flow.
Databricks as a market leader
What do you like best about the product?
It is a one-stop shop for all. It is clearly the best data processing solution that I have ever used.
What do you dislike about the product?
There is still room for improvements - for example, when you deploy, it could be nice to set the configuration of DBFS storage (is it LRS or GRS, etc.). When VMs are deployed, there should be more options to configure hard drives. It is a minor issue related to administrative tasks, but anyway, it is the best lakehouse on the market today.
What problems is the product solving and how is that benefiting you?
Data processing, data lake, machine learning, streaming live data...
Data Scientist
What do you like best about the product?
Experiment management, and model deployment.
What do you dislike about the product?
Support for code engineering and version control.
What problems is the product solving and how is that benefiting you?
Predictive modeling.
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
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.
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