BigML Reviews

BigML is a excellent platform which is easy to set up an even more easier to use.

They provide a very good dashboard where we can manage all the features provided by BigML.

As it is a API for Machine Learning algorithms, they also provide sample datasets so one can directly go hands on or even can play with custom user defined datasets.

The free pack comes with limited usage and I think the cost of paid version is bit too high. The cost can be reduced or there should be some smart pack where we will only pay per use.

BigML is very handy and simple to use but sometimes to get along one needs good practice and understanding. So I advice to play with the sample datasets BigML provides and get a clear idea about "How BigML Works!" before jumping to real projects.

As a morden tech based company we actively work on Machine Learning Projects. We believe in providing best solution, so we only go for best services.

Through these years BigML has proved itself and we have happily delivered many ML based projects with positive feedback from clients.

The dashboard is very well designed and provides with all the functionalities.

The provide easy to use API with very good tutorials which help a lot to learn the ins and outs of BigML API.

They also provide functionality where one can upload datasets and play arround with it on the go.

They even provide some free datasets so one can directly get hands on to the development part.

They provide a wide range of algorithms one can choose according to his/her convenience and the problem statement.

I don't dislike anything expect that I always would need a good internet connection when I wanna play around. I think if there is some provision where one can also get something free offline, it would be great!

This is not a place to learn. Don't directly jump to BigML, it is for production. If you wanna learn try other options and come here only when you have a good idea how things work in ML world.

Today in the world Machine Learning has provided solutions to lot of domains and applications. We here at Refurbish Co. Ltd. have a good number of employees involved in ML and so we provide solutions to the clients with the state of art machine learning algorithms.

As BigML is easily available and robust. I can say a production ready service, we use BigML to make such applications with it's ML API as a core.

Their customer support is great, every time I face a issue it get resolved within a day or two. Apart from that their service is also great, I really loved this concept where they provide every Machine Learning Algorithm just using a API, which can be used in any programming language. I think is really great thing. Also their website has good UI/UX.

I every time need a internet connection to test my application or even after I make a full working product. They provide no offline support like the famous machine learning library scikit-learn which is open source too. BigML is a paid service where as we can find many services in this market which are free of cost.

They provide some data sets for free to explore, use them for learning and understanding purpose.

Using it in applications where we perform Machine Learning tasks.

The best thing about BigML is you don't need any files on your local system, just an internet connection and API will let you do anything you want, from model deployment to prediction. You can save the model on cloud and use it from any device you want. Also, one more thing I would like to mention is the free datasets they provide for playing around with your models, there is a wide range of datasets that one can use directly with API.

We have been using BigML for a long time and have never faced issues.

They provide a variety of free datasets, I recommend to use them while you become familiar with the platform.

Deploying ML models on cloud facility they provide to use them in different projects we build in our organization.

This is a great platform where one can easily find any algorithm related to Machine Learning. Yes, even they provide a lot of data sets which really helps for learning and understanding the BigML API. The documentation is too good and up to the mark.

It is not a free service, where as one can easily find many open source libraries such as scikit-learn that meets the purpose and even its free.

There are a number of free datasets available, which are displayed on the dashboard itself when you sign in. I suggest you to play with those datasets, it will provide a brief information how BigML API works .

Deploying models on BigML cloud that are used to carry out various ML tasks using the powerful BigML API.

I really like that it provides service as a REST API. It is so fast and efficient that I always rely on it for all of the products I develop.

In free trail it limits the storage which forces one to buy a paid pack. I would really love to recommend it if it would have been providing more perks in free pack. It constraints itself to professional purpose and cannot be used for learning purpose.

Use the sample datasets they provide on the website for practice purpose, they really help one to understand how the algorithms work.

I use it almost everywhere where there is use of Machine Learning. For all the Machine Learning involving products we develop at Soni Tech we always use BigML.

The availability for storing on cloud is one of the best things I like about BigML. You can use to store your datasets or even trained models on cloud so that you can easily use them from anywhere you want.

The documentation don't really meet the needs. There is a lot more BigML can do which should be well explained in documentation.

While using it for first time use the provided datasets to well understand the process how it works so you can well format your own datasets later.

We are working on Machine Learning so with the help of BigML we have built many machine learning models which used for different purposes from classification to clustering.

- The UI/UX of the website is great.

- It supports multiple languages and is easy to use.

- Wide range of free datasets to play with.

- Provides almost all Machine Learning algorithms which are also optimized.

- It's not free and provides very less perks in free pack.

- One always needs an Internet connection as it totally works on Web API.

Play around with the wide range of free data sets to understand the algorithms they provide.

Creating Machine Learning models and using them to carryout various Machine Learning tasks in our organization.

- Cloud storage feature, where you can store your trained models and data sets, which can be used anywhere just with the access of internet.

- The tutorials, it is a great help!

The whole framework is API based so you cannot play with the data when you are offline. It is a major issue I am facing all the time.

If you are a learner first try scikit-learn or some other better library, as BigML doesn't provide offline support and more of a professional kind.

- Maintaining and managing Machine Learning models.

- Developing ML based projects using the API, which is very simple to use.

- Available algorithms that can be used by simple API support.

- There also different datasets available that can be used to train basic models.

- Cloud Storage.

- Supports multiple langua

Cannot work offline, everything works on web based API which requires internet. For even simple changes I need internet connection. There should be offline support.

There are not enough tutorials available, if you are a learner use scikit-learn. It's better has a good documentation as well as may tutorial videos are available on internet.

- Building machine learning based products using BigML API.

- Using cloud storage feature to store trained models.

It has almost all machine learning algorithms implemented, which on can use on the fly without any hesitation with the easily accessible API.

It is a totally a cloud platform, where one need to depend on the internet connection. No internet, no BigML. It would have been better if there would be some kind of offline support.

It is easy to use, but still I recommend you to start learning from scikit-learn: http://scikit-learn.org/ as it is very well documented and has offline support too, and go for BigML then after.

Making products which uses Machine Learning.

- Support for cloud storage.

- Sample datasets provided.

- It has implemented algorithms for clustering and classification that can be used through API.

- In the free pack it offers very less storage and also limits the number of tasks.

- Whole framework is API based and has no offline support.

Use scikit-learn before using bigML. It has good documentation and a lot of tutorials available on internet.

- Using as a cloud storage for trained models and datasets so I can remotely access them.

- Using API of different algorithms to develop applications.

It provides easy to use APIs for machine learning algorithms as well as can be used a cloud storage for managing ML data, might be some dataset or a custom algorithm.

All the APIs require a internet connection, won't work for offline systems. Some kind of offline support is required.

There are various datasets provided and several examples too, I advise to have a look on them to help you understand how bigML works.

Using bigML APIs for machine algorithms that might otherwise need us work more on ML part not the project.

We're still implementing but so far so good, we like the amount of predictive modules we can set up which will help us layer data for mapping and outreach into the communities we work in.

So far nothing other than making her we don't have an operator error during our set up to ensure we get the right predictive output.

We hope to use bigML in our research project that is currently focused in an urban area of over 1 million people. Part of what we hope to use bigML to do is predict technological (internet) usage based on survey data we collect over 5-9 months. The outcomes we hope will allow us to develop recommendations (policy and planning) to local governments, public institutions, and citizens.

- Cloud Storage for ML models.

- Provides a wide range of datasets for classification to clustering models.

- API is easy to use.

- No offline support.

- It is complex at some extent, a better option would be scikit-learn: http://scikit-learn.org/ for beginners.

There are lot of datasets that you can easily use to train your models. You can also directly use the trained models through API.

- Using as a cloud repository for all of our trained models.

- Developing projects with easy and simple-to-use API.

The algorithms it provide are fast as well as efficient. Also another benifit is we don't need any local storage space, it totally works on cloud and Web API.

I am using it from a long time and haven't faced any problems. I am happy with the experience so I would say, 'no dislikes' for BigML.

Along with algorithms, BigML provides a lot of datasets which can be used to have a basic knowledge of a particular algorithm. So for practice purpose I highly recommend to use this datasets.

Using BigML Web API in different applications we develop at Soni Tech to where a bit or a lot of Machine Learning is required.

Along with algorithms it also provides a lot of data sets, which really helps one to understand how it really works and directly get hands on with the service.

It offers very less perks for example: very storage space with the free account, there should be more perks provision with the free account.

BigML provides a lot of datasets, make use of them. Personally they have helped me a lot.

Developing Machine Learning Softwares

- Cloud storage for models.

- Detailed Documentation.

- Easy to deploy on any platform as it is API based.

You always need a internet connection to play with the code as it totally works on API.

If you are learner don't directly jump to bigML, try scikit-learn: http://scikit-learn.org/ first then use this platform as it is bit complex then scikit-learn.

- Creating apps and software that are based on Machine Learning algorithms.

- Cloud storage for developed models.

Cloud Storage facility where I can store useful datasets and trained models which can directly by accesed by API on the fly.

bigML is great platform for developing machine learning based projects. I am using it from a long time, honestly no dislikes.

It has a lot of examples and sample datasets, have a look at those would be a great help to beginners.

Implementing machine learning algorithms using APIs for developing projects for clients.

- Simple set up to get data loaded

- Easy to set target variables

- Easy to ignore variables

- Automatically creates date variables unnecessarily

- Takes a bit of time to understand how to configure the various models

- Processing concurrent threads is limited based on your tiers

There are many tools that have similar value propositions. This is a tool not meant for everyone, but most likely a business user who has a solid understanding of technical data science. If you do not have a background in data science, first take a crash course or considering purchasing a book to give more context on how to leverage data science methodologies.

Generally, we try to solve for objectives of both good and bad results (customer acquisition versus churn). By leveraging some machine learning out of the box, we can still get the majority of our insights without having to hire external consultants or a full time resource.

- Cloud Storage for Machine Learning models.

- Support for multiple languages.

- You can automate your Machine Learning workflows.

It totally works on API so you always need an internet connection and cannot operate when offline.

Use already implemented models for basic tasks they are efficient and also accurate.

- Using it as cloud storage of Machine Learning Models.

- Building projects using highly efficient algorithms of BigML that can easily accessed by API.

It is easy to manage and simple to use. We can directly implement machine learning models through API.

I personally think that there should be some kind of offline support.

Refer the examples, there are lot of them with good explanation.

Implementing Machine Learning algorithms using simple APIs in projects.

Free trial, product support, with minimal experience for learners of ML

ML DL is still an expert field, for those struggling, BigML is a great stepping stone.

matelabs.in an interesting alternative.

projects

I don't really remember exactly how to use it but it was easy to use, which is important for me.

There wasn't much that I remembered that I disliked.

Efficiency in productivity