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Watch MindsDB presentation recording at RedisConf 2021 to learn about how automated machine learning can be applied inside Redis!
Date: April 20-21, 2021
Event details: https://redislabs.com/redisconf/
Presentation Abstract:
The classical machine learning workflows are complex. It takes weeks to months to validate, test and deploy ML models and integrate them into an application, not to mention how expensive it is.
But what if machine learning becomes standard functionality of the data-layer tools, like Redis? Furthermore, what if ML models would learn and predict/forecast from the data contained in Redis automatically?
MindsDB achieved significant progress in bringing machine learning into popular databases, and now we are doing it for Redis! MindsDB’s open-source integrations make it possible to implement ML projects in a matter of hours, and it only requires basic data on database skills.
In this journey, we discovered and solved some machine learning problems that are hard even for ML engineers but are common for Redis, i.e. forecasting over time-series data streams, taking into account multiple parameters (model features) at once.
For example, real time forecasting of anomalies in inventory for all products in all stores from a stream containing inventory updates over time.
This problem is complex even for the most experienced ML engineering teams. Not only because ML on real-time streams of data is very challenging, but also because In a traditional ML approach, you would need to train one model for each product at each store, which can mean thousands or hundreds of thousands of models, not even thinking of the logistic nightmare to bring such many models to production.
We have made incredible progress in solving these time-series problems, and now we bring such capabilities in Redis Streams and we would like to share with you our solutions and discuss some exciting ideas that have occurred in the process.
Speaker:
Jorge Torres, MindsDB CEO
Co-founder & CEO of MindsDB. Recently research scholar at UC Berkeley researching machine learning automation and explainability. Before shenanigans at MindsDB, I worked for a number of data-intensive start-ups that aimed to impact millions of people, like working with the first CTO in the US government Aneesh Chopra building data systems that analyze billions of patient records and lead to millions in savings, as a very arly engineer at Skillshare or working as the first full-time engineer at the Couchsurfing facilitating cultural exchange for tens of millions of people.
Watch MindsDB presentation recording at RedisConf 2021 to learn about how automated machine learning can be applied inside Redis!
Date: April 20-21, 2021
Event details: https://redislabs.com/redisconf/
Presentation Abstract:
The classical machine learning workflows are complex. It takes weeks to months to validate, test and deploy ML models and integrate them into an application, not to mention how expensive it is.
But what if machine learning becomes standard functionality of the data-layer tools, like Redis? Furthermore, what if ML models would learn and predict/forecast from the data contained in Redis automatically?
MindsDB achieved significant progress in bringing machine learning into popular databases, and now we are doing it for Redis! MindsDB’s open-source integrations make it possible to implement ML projects in a matter of hours, and it only requires basic data on database skills.
In this journey, we discovered and solved some machine learning problems that are hard even for ML engineers but are common for Redis, i.e. forecasting over time-series data streams, taking into account multiple parameters (model features) at once.
For example, real time forecasting of anomalies in inventory for all products in all stores from a stream containing inventory updates over time.
This problem is complex even for the most experienced ML engineering teams. Not only because ML on real-time streams of data is very challenging, but also because In a traditional ML approach, you would need to train one model for each product at each store, which can mean thousands or hundreds of thousands of models, not even thinking of the logistic nightmare to bring such many models to production.
We have made incredible progress in solving these time-series problems, and now we bring such capabilities in Redis Streams and we would like to share with you our solutions and discuss some exciting ideas that have occurred in the process.
Speaker:
Jorge Torres, MindsDB CEO
Co-founder & CEO of MindsDB. Recently research scholar at UC Berkeley researching machine learning automation and explainability. Before shenanigans at MindsDB, I worked for a number of data-intensive start-ups that aimed to impact millions of people, like working with the first CTO in the US government Aneesh Chopra building data systems that analyze billions of patient records and lead to millions in savings, as a very arly engineer at Skillshare or working as the first full-time engineer at the Couchsurfing facilitating cultural exchange for tens of millions of people.
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