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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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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.
Great solution for data analytics
Databricks is a great solution for data analytics in each company. We use this platform for more than 2 years in our DataLake with 100 % satisfaction.
What do you like best?
Solution as a service, you don´t care about software or hardware.
What do you dislike?
Nothing :)
Reduced database network redistributions & run-time of key models by 99+%!
What do you like best about the product?
Incidentally, the thing I like most about Databricks isn't a product feature at all; I love Databricks's proactive and customer-centric service, always willing to make an exception or create a unique feature, all the while minimizing costs for the customer - as @Heather Akuiyibo & Shelby Ferson et al. have done for me and my former teams!
What do you dislike about the product?
Broadening programming logic and syntax.
What problems is the product solving and how is that benefiting you?
To name seven (7):
(1) User segmentation using a proprietary variation of a hierarchical DBSCAN clustering algorithm of high-dimensional data with novel distance [quasi] metric, based on hubness analysis;
(2) Leveraging the above in email targeting and invoking multi-armed bandit testing methodologies for email timing, frequency, and content, using decreasing-epsilon strategy;
(3) Modeling predicted underwriting criteria with a binary approval odds classification algorithm;
(4) Using a dynamic panel data, fixed effects model to predict the effect of changes in credit reports on user credit score;
(5) Employing an Autoregressive Integrated Moving Average (ARIMA) with optimized Akaike Information Criterion exploits to predict future revenue and growth (lagged results led to average error bounds of only 5 percent; cross-validation results were even stronger, though I was conservative in guaranteeing 7 percent error, on average);
(6) Refining a multiverse (context-aware) recommendation engine as an n-dimensional tensor (rather than the typical two-dimensional user-item matrix) for partner product recommendations, using High-Order Singular Value Decomposition to solve;
(7) Invoking a Convolutional Neural Network framework with a novel architecture and results of a Fourier Transform as input to classify dental x-rays and highlight to the dentist which teeth require fillings (after approximately two months, the model reached ~95 percent accuracy - in terms of actual agreement by dentists using the app - with F1 score in cross-validation performing on par).
(1) User segmentation using a proprietary variation of a hierarchical DBSCAN clustering algorithm of high-dimensional data with novel distance [quasi] metric, based on hubness analysis;
(2) Leveraging the above in email targeting and invoking multi-armed bandit testing methodologies for email timing, frequency, and content, using decreasing-epsilon strategy;
(3) Modeling predicted underwriting criteria with a binary approval odds classification algorithm;
(4) Using a dynamic panel data, fixed effects model to predict the effect of changes in credit reports on user credit score;
(5) Employing an Autoregressive Integrated Moving Average (ARIMA) with optimized Akaike Information Criterion exploits to predict future revenue and growth (lagged results led to average error bounds of only 5 percent; cross-validation results were even stronger, though I was conservative in guaranteeing 7 percent error, on average);
(6) Refining a multiverse (context-aware) recommendation engine as an n-dimensional tensor (rather than the typical two-dimensional user-item matrix) for partner product recommendations, using High-Order Singular Value Decomposition to solve;
(7) Invoking a Convolutional Neural Network framework with a novel architecture and results of a Fourier Transform as input to classify dental x-rays and highlight to the dentist which teeth require fillings (after approximately two months, the model reached ~95 percent accuracy - in terms of actual agreement by dentists using the app - with F1 score in cross-validation performing on par).
Recommendations to others considering the product:
Be open to the pitch. You may think things are "going fine" or proffer the idea of "if it ain't broke, don't fix it," but these represent short-term thinking traps such that scaling becomes inherently and implicitly constrained and limited. Databricks amounts to the forward-thinking businessperson.
How I experienced databricks
What do you like best about the product?
It is great when you have large amount of data, excellent for collaboration, perfect for using with visualisation tools and functions with many programming languages.
What do you dislike about the product?
Difficult to get a grasp on how many applications and funcrions it has.
What problems is the product solving and how is that benefiting you?
It s great for ELT of date to use with power BI
Recommendations to others considering the product:
Use it it s the best available and it s great!
Excellent infrastructure, can scale clusters in no time
What do you like best about the product?
Interactive clusters, user friendly, excellent cluster management
What do you dislike about the product?
Cluster takes some time to heat up on start, should support upsert without delta as business need pure upserts too
What problems is the product solving and how is that benefiting you?
Can seemlessly use pyspark, Python to build a robust pipeline
Recommendations to others considering the product:
It's the best infrastructure to build pipelines if you are planning to use spark in production
Awesome Experience
What do you like best about the product?
This is really a nice user friendly platform.
What do you dislike about the product?
I have not found any glitches. It is really good.
What problems is the product solving and how is that benefiting you?
Its really simple to manage data.
Recommendations to others considering the product:
NA
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