Show HN: AutoAI – A framework to find the best performing AI/ML model
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Re: Show HN: AutoAI – A framework to find the best performing AI/ML model
#2Hi HN, we have seen a lot of AutoML frameworks out there. As a Data Scientist myself, I have refrained from using these because at the end of the day, you have to submit complete source code to your clients, not just a functioning model. That is why we created AutoAI. Given data and target (value to predict), it can automatically discover and fully train the best performing AI solution. Still, most importantly, it also goes on to produce high-quality Jupyter Notebook code. AutoAI does Whitebox AutoML. A much-needed feature for Data Scientists. Do give it a try, and let me know what you think.
Re: Show HN: AutoAI – A framework to find the best performing AI/ML model
#3I noticed a variety of AutoML solutions in the market addressing similiar pain points. How is this different from other solutions/platforms.... Isn't this a saturated space?
Re: Show HN: AutoAI – A framework to find the best performing AI/ML model
#4I noticed a variety of AutoML solutions in the market addressing similiar pain points. How is this different from other solutions/platforms.... Isn't this a saturated space?
Thanks for your question. Yes, we did research the space a lot before making AutoAI. Here is what we found:
PyCaret: Semi-automatic. You do the first run; then you figure the next set of runs. Ensemble models require manual configuration.
Tpot: Does a great job. Generates 4-5 lines of py code too. But does not support Neural Networks / DNN. So works only for problems where GOFAI works.
H2O.ai: They have an open-source flavor, but the best way to use it is the enterprise version on the H2O cloud. The interface is confusing, and the final output is black-box.
Now there are many in the enterprise category, such as DataRobot, AWS SageMaker, Azure etc. Most are unaffordable to Data Scientists unless your employer is sponsoring the platform.
AutoAI: This is 100% automated. Uses GOFAI, Neural Networks and DNN, all in one box. It is 100% White-box. It is the only AutoML framework that generates high-quality (1000s of lines) of Jupyter Notebook code. You can check some example codes here: https://cloud.blobcity.com
Re: Show HN: AutoAI – A framework to find the best performing AI/ML model
#5I noticed a variety of AutoML solutions in the market addressing similiar pain points. How is this different from other solutions/platforms.... Isn't this a saturated space?
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Re: Show HN: AutoAI – A framework to find the best performing AI/ML model
#6I noticed a variety of AutoML solutions in the market addressing similiar pain points. How is this different from other solutions/platforms.... Isn't this a saturated space?
Thanks for your question. Yes, we did research the space a lot before making AutoAI. Here is what we found: PyCaret : Semi-automatic. You do the first run; then you figure the next set of runs. Ensemble models require manual configuration. Tpot : Does a great job. Generates 4-5 lines of py code too. But does not support Neural Networks / DNN. So works only for problems where GOFAI works. H2O.ai : They have an open-so…
AzureML and its AutoML are not unaffordable. It's literally a free service. You only pay for any compute you may consume, and for that you only pay the bare VM price. But you don't have to, you can also use your local compute for training.
Re: Show HN: AutoAI – A framework to find the best performing AI/ML model
#7Earlier quoted context omitted.
Thanks for your question. Yes, we did research the space a lot before making AutoAI. Here is what we found: PyCaret : Semi-automatic. You do the first run; then you figure the next set of runs. Ensemble models require manual configuration. Tpot : Does a great job. Generates 4-5 lines of py code too. But does not support Neural Networks / DNN. So works only for problems where GOFAI works. H2O.ai : They have an open-so…
AzureML and its AutoML are not unaffordable. It's literally a free service. You only pay for any compute you may consume, and for that you only pay the bare VM price. But you don't have to, you can also use your local compute for training.
That is not true actually. You must purchase an Azure VM for training a model. Deploying trained models on your own infrastructure is permitted. Reasonably speaking we would have to call this an enterprise solution, as there is no way to get a trained model without paying Azure fees. Most models will require GPU, and it is not like we can run AzureML on the free GPU offered by Google Colab.
Re: Show HN: AutoAI – A framework to find the best performing AI/ML model
#8Looks like a nice project. I just bookmarked it to try sometime.
At a previous job, my boss wanted me to spend time on AutoML. I based my work on Google’s AdaNet [1] that did architecture search inside a single TensorFlow session. Unfortunately that project seems to have been abandoned.
Re: Show HN: AutoAI – A framework to find the best performing AI/ML model
#9Hi HN, we have seen a lot of AutoML frameworks out there. As a Data Scientist myself, I have refrained from using these because at the end of the day, you have to submit complete source code to your clients, not just a functioning model. That is why we created AutoAI. Given data and target (value to predict), it can automatically discover and fully train the best performing AI solution. Still, most importantly, it al…
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Re: Show HN: AutoAI – A framework to find the best performing AI/ML model
#10Looks like a nice project. I just bookmarked it to try sometime. At a previous job, my boss wanted me to spend time on AutoML. I based my work on Google’s AdaNet [1] that did architecture search inside a single TensorFlow session. Unfortunately that project seems to have been abandoned. [1] https://github.com/tensorflow/adanet
Thanks Mark. Please do share your experience. Very keen on hearing how you find our project. We are still in beta, so lots of scope for improvement. We are open to any suggestions you might have.