Hopsworks Academy

Fabio reviews the different ways to generate training data for machine learning models, from the features stored in Hopsworks.

Jim explores the machine learning pipeline framework known as FTI; Feature Training and Inference and argues on how this is a better mind map for ML systems, both batch and real-time.

Fabio presents the 3 main ways to keep feature pipelines in production with Hopsworks. From the management of the code and the deployment to the monitoring.

Mahmoud presents the different options and details on how to deploy managed Hopsworks (the enterprise version of Hopsworks) on any clouds via the Hopsworks Control Plane or Terraform.

Fabio introduces the concepts and different use cases for the external feature groups in Hopsworks; data that does not resides in Hopsworks but can still be used for machine learning, even for real-time use cases.

Fabio walks through the different ways to version data and features in the Hopsworks Feature Store.

Fabio shows how users can create streaming feature pipelines in Hopsworks using Spark Streaming and Kafka.

Fabio shows how to work with feature groups in the Hopsworks feature store and how Hopsworks uses dataframe to help datascientists manipulate data.

A guide to how to use the Hopsworks API and Feature Views to retrieve data for real-time inference.

Fabio introduces feature views in Hopsworks; a way to join feature groups together in a point in time correct manner with a simple API and avoid inconsistencies in the pipeline by using the same view for training and inference.

In this makerspace, we show you how to build and operate a real-time feature streaming pipeline that writes at 1m ops/sec - TikTok Scale.

We build a LLM that not only detects whether a scanned image of a cheque is a fraud, but also writes an explanation for why the cheque has been marked as fraud.

We build an AI system that allows you to search your private PDF files.

We look into extending RAG for LLMs to include the ability to query structured data and API calls using function calling.

Lex and Aleksey demonstrate how we use LLMs to automate feature engineering and streamline ML pipelines with Hopsworks, making data transformation and deployment more efficient and scalable.

Javier demonstrates how we migrated Hopsworks to Kubernetes, reducing deployment times significantly. With our approach, we've cut deployment from 2 hours to less than 30 minutes using a Helm install.

Antonios demonstrates the high availability and robustness capabilities of the Hopsworks platform.

In this makerspace, we show you how to build and operate a real-time feature streaming pipeline that writes at 1m ops/sec - TikTok Scale.

Jim explores the machine learning pipeline framework known as FTI; Feature Training and Inference and argues on how this is a better mind map for ML systems, both batch and real-time.

We build an AI system that allows you to search your private PDF files.

We build a LLM that not only detects whether a scanned image of a cheque is a fraud, but also writes an explanation for why the cheque has been marked as fraud.

Mahmoud presents the different options and details on how to deploy managed Hopsworks (the enterprise version of Hopsworks) on any clouds via the Hopsworks Control Plane or Terraform.

Fabio introduces the concepts and different use cases for the external feature groups in Hopsworks; data that does not resides in Hopsworks but can still be used for machine learning, even for real-time use cases.

Fabio shows how to work with feature groups in the Hopsworks feature store and how Hopsworks uses dataframe to help datascientists manipulate data.

Fabio presents the 3 main ways to keep feature pipelines in production with Hopsworks. From the management of the code and the deployment to the monitoring.

Fabio introduces feature views in Hopsworks; a way to join feature groups together in a point in time correct manner with a simple API and avoid inconsistencies in the pipeline by using the same view for training and inference.

We look into extending RAG for LLMs to include the ability to query structured data and API calls using function calling.

Fabio reviews the different ways to generate training data for machine learning models, from the features stored in Hopsworks.

A guide to how to use the Hopsworks API and Feature Views to retrieve data for real-time inference.

Antonios demonstrates the high availability and robustness capabilities of the Hopsworks platform.

Lex and Aleksey demonstrate how we use LLMs to automate feature engineering and streamline ML pipelines with Hopsworks, making data transformation and deployment more efficient and scalable.

Javier demonstrates how we migrated Hopsworks to Kubernetes, reducing deployment times significantly. With our approach, we've cut deployment from 2 hours to less than 30 minutes using a Helm install.

Fabio shows how users can create streaming feature pipelines in Hopsworks using Spark Streaming and Kafka.

Fabio walks through the different ways to version data and features in the Hopsworks Feature Store.

Lex explains what is a feature store in a nutshell; what does it do, why is it important, and how it helps organisations build more models faster.