Show HN: Build predictive models with no prior ML experience
12 comments
Lovely one
Do you perform model accuracy metrics on this data and provide it back to the customer? One thing I've noticed about a lot of enterprise low/no code ML platforms (dialogflow, I'm looking at you) is that they give you everything you need to train the models, but nothing to evaluate them. Always seemed like an 'all the rope you need to hang yourself' kind of situation, come crawling back for consultants when you need us. Instead of just giving users the tools they need to calibrate models themselves. Would love to hear about this functionality in your product.
If you are trying to predict a categorical column, we will record the accuracy, precision, recall, etc and display it to you after the training. We’ll also summarize to you how it compares to a baseline score. We also explain each metric.
Here is a sample screenshot: https://www.loom.com/i/67abbc4a1af949a8b40390df0605ae4c
Everything we do in Mage is to help educate and help the end user (product developer) become proficient themselves. That means explaining everything that we’re doing behind the scenes.
Here is a sample screenshot: https://www.loom.com/i/67abbc4a1af949a8b40390df0605ae4c
Everything we do in Mage is to help educate and help the end user (product developer) become proficient themselves. That means explaining everything that we’re doing behind the scenes.
That's great to hear. Thanks a lot for your time, I'll look into using Mage :)
Can’t wait to see what you build! We’d love to get your feedback, comments, concerns, ideas, etc. Thank you soooo much.
The last time I saw a demo of one of these “citizen data scientist” tools, the sales guy driving it developed a model with .99 auc and “row number” in the data set as the most important feature.
Here is a screenshot of the top features of a model I just trained quickly on the Titanic data: https://www.loom.com/i/8c090a2431c34dcb88aa598b6cb0389d
Here are the metrics: https://www.loom.com/i/fd435e6e39e24db7bc630283bb044506
Here are the metrics: https://www.loom.com/i/fd435e6e39e24db7bc630283bb044506
That is very funny. We like to demo with the Titanic data. Performs decent. Top features are gender, age, fare, etc.
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My name is Tommy DANGerous (or Tommy Dang) and I’m the CEO and co-founder at Mage. I worked at Airbnb for over 5 years as a product developer building features for guests.
Mage is a web-based tool for building, training, and deploying ML models that make predictions based off your data.
Training and using ML models in production typically requires working knowledge of building data pipelines, algorithms, infrastructure for deployment and inference, and more. Because of this highly specialized skillset, mostly data scientists and ML engineers are the only ones able to build and use ML models. Existing ML tools cater to this audience.
Over my 5+ years at Airbnb, I helped build and launch the Airbnb Experiences product, created ML models before and after we had in-house tooling, and built a devtool platform called Omni. I worked with 100s of product developers across the company and saw that they knew how ML is being used and had ideas on how they would apply ML to their specific feature. However, they relied on data science resource to help them implement their ideas even though we had ML tools built in-house for data scientists.
Mage is a low-code tool that you can access via your web browser. You can build ML models through our user interface. How it works:
1. First, you add data by uploading a file or connecting to data source like Amplitude, AWS Redshift, S3, Snowflake, GCP BigQuery, etc. Once you add your data, we store it on AWS S3 for fast retrieval and transformations. 2. Next step is you are given suggestions on how to enhance your dataset. You can perform functions like filtering, aggregating, adding columns, etc. We provide a GUI for you to perform these transformations. Behind the scenes, we’re translating your input into code using the Pandas API. 3. Once you’re done preparing and cleaning your data, we’ll train your model by launching a few data pipelines in Airflow, use Spark to build your training data, and then run our proprietary ML pipeline to train your model. 4. Finally, when you’re ready to use the model, we’ll deploy your model to an online API endpoint that is running on AWS ECS. You can get your model’s predictions via a POST request.
Existing ML tools are designed and built for data scientists and ML engineers. Mage is designed and built for product developers by product developers. This means we designed our tool to be usable by someone with no ML experience and we provide guided suggestions throughout the process to help educate users and help them become an ML expert.
We’d love for you to try a demo of Mage without needing to sign up: https://www.mage.ai/onboarding. If you love it and want to use the entire set of features, you can sign up and use it for free (the best developers tools are free!). Thank you so much, your support is ultra appreciated!