What is Declarative Machine Learning?
Declarative machine learning systems provide the best of flexibility and simplicity to enable the fastest-way to operationalize state-of-the-art models. Users focus on specifying the “what”, and the system figures out the “how”.
Easy
Unlock cutting-edge deep learning on your dataset in just six lines.
Configurable
Start with smart defaults, but iterate on parameters as much as you’d like down to the level of code.
Proven
Our team pioneered declarative machine learning systems in industry, with Ludwig at Uber and Overton at Apple.
How it works
Connect your data with a few clicks
Choose from our menu of prebuilt data connectors that support your databases, data warehouses, lakehouses, and object storage.
Automatically train models on top of Ludwig
Train state-of-the-art deep learning models without the pain of managing infrastructure.
Operationalize models, in a familiar way
Access models via REST, Python, or PQL - our slight extension of SQL that puts machine learning in the language closest to data.
Use Cases
Predibase works across supervised machine learning use cases such as:
Unstructured data analytics
Write SQL-like analytical queries over text, images, video, audio in addition to tabular data.
Predictive Lead Scoring
Predict the value of a lead and likelihood of conversion based on your historical data.
Customer Service Automation
Classify incoming messages and predict responses to automate
Churn Prediction
Predict customer churn before it happens to increase retention
Anomaly & Fraud Detection
Detect anomalies or fraud in your data based on previously flagged results
Demand Forecasting
Forecast future demand based on historical trends in your data.
Recommendation Systems
Target recommendations based on previous user behavior to improve product engagement.
Many more
Predibase can support your machine learning use case, no matter how complex. Contact us to learn more about how we can help you with AI today.
Why Users Love Us:
State-of-the-art machine learning in your hands
Easily leverage powerful models such as BERT and GPT in production.
Glass Box, not Black Box
Automated Machine Learning that strikes the balance of flexibility and control, all in a declarative fashion.
Fits into existing workflows
Whether it’s PQL, Python, or REST, data users can choose what interface works for them.
ML Infrastructure, simplified.
Effortlessly scale training and deployment of ML without the headache.
Why Organizations Love Us:
ML in days, not months
With a declarative approach, finally train and deploy models as quickly as you want.
Democratizes access to machine learning
Equip your broader data organization to effectively use machine learning.
Unlock your company’s potential
Go from data to deployment like never before. The possibilities are endless.
Proven Open Source Technology
Ludwig
Ludwig is a deep learning toolbox to declaratively develop, train, fine-tune, test and deploy state-of-the-art models. Ludwig puts deep learning in the hands of analysts, engineers and data scientists without requiring low-level ML code.
Horovod
Horovod is a distributed deep learning framework that scales PyTorch and TensorFlow training to hundreds of machines. It supports Tensorflow, Keras, Pytorch, Apache MXNet and has been used to productionize deep learning models across industries
Join Our Slack Community!
Integrates with
Predibase is built for the modern data stack, allowing data practioners to easily connect data wherever it lives.