Transition from monolithic ML workflows to independant feature pipelines, training pipelines, and inference pipelines. This will enable improved collaboration between your Data and ML teams, and improve operations and maintanability of your ML systems.
Feature engineering at reasonable scale. Bring your own code with you, use any popular library and framework in Hopsworks.
Role-based access control, project-based multi-tenancy, custom metadata for governance.
Feature Engineering at scale, and with the freshest features. Batch or Streaming feature pipelines.
Your cloud, your infrastructure, on-premise or anywhere else; managed clusters on AWS, Azure, or GCP.
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Use Python, Spark or Flink with the highest performance pipelines for reading and writing features.
Enterprise Support available 24/7 on your preferred communication channel. SLOs for your feature store.
"Our journey with Hopsworks has been an amazing transformation that's really enabled us to be innovative and reach a point that we wouldn't have been able to reach otherwise.”
Achieve an 80% reduction in cost over time starting from the second ML models are deployed in production.
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MLOps with a feature store allows your organisation to put your data into production, faster.
Accelerate your machine learning projects and unlock the full potential of your data with our feature store comparison guide.
Read our docs to find out more.
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Hopsworks goes to Gotland! 🏰
Our team recently embarked on an amazing team building trip to the beautiful island of Gotland. It was an incredible opportunity for us to bond, recharge, strengthen our collaborative spirit and embrace the sunny season!
With #PyDataLondon2023 around the corner, we want to thank @hopsworks for their platinum sponsorship! Sponsor support is vital in empowering the #datascience community and driving innovation. We're thrilled to have you 🐍✨
Join us this June 2-4!
We started out writing a MLOps Glossary, but it became a dictionary. I hope it's useful for those who want to learn about MLOps and Feature Stores:
We received an incredible gift!!! Thanks to the Semantic Layer Summit, @AtScale, for your note, and congratulations on an amazing Summit!
Watch @jim_dowling's talk "Feature Store and the Semantic Layer" on demand! 📹
Want to 𝗹𝗲𝗮𝗿𝗻 𝗠𝗟 & 𝗠𝗟𝗢𝗽𝘀 𝗶𝗻 𝗮 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝘄𝗮𝘆? After 6 months of work, I finally finished 𝘛𝘩𝘦 𝘍𝘶𝘭𝘭 𝘚𝘵𝘢𝘤𝘬 7-𝘚𝘵𝘦𝘱𝘴 𝘔𝘓𝘖𝘱𝘴 𝘍𝘳𝘢𝘮𝘦𝘸𝘰𝘳𝘬 Medium series.
In 2.5 hours of reading & video materials, you will learn how to:
- design a batch-serving architecture
- use Hopsworks as a feature store
- design a feature engineering pipeline that reads data from an API
- build a training pipeline with hyper-parameter tunning
- use W&B as an ML Platform to track your experiments, models, and metadata
- implement a batch prediction pipeline
- use Poetry to build your own Python packages
- deploy your own private PyPi server
- orchestrate everything with Airflow
- use the predictions to code a web app using FastAPI and Streamlit
- use Docker to containerize your code
- use Great Expectations to ensure data validation and integrity
- monitor the performance of the predictions over time
- deploy everything to GCP
- build a CI/CD pipeline using GitHub Actions
- trade-offs & future improvements discussion
#machinelearning #course #mlops
hopsworks: Hopsworks - Data-Intensive AI platform with a Feature Store ⭐️ 812
#devopskhan #aws
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Introducing Hopsworks beers 2.0! 🍻☀️
As a team we are not just passionate about data and technology, we also happen to be a group of beer-loving individuals (hence the name). In fact, our love for beer runs so deep that we decided to take it a step further and brand our very own brews! Cheers!
Second workshop of the day that involves @huggingface Spaces & @Gradio, delivered by @jim_dowling 🤩
Check out his free course on serverless ML here 👉🏼 https://serverless-ml.org
Making some new friends at @pydatanyc - something might be stacking up soon - @noteable_io x @hopsworks?
Wanna learn how to generate real-time features for a crypto trading bot?
Here is an example with full source code ↓
🚀 Unlock the full potential of your machine learning workflows with Hopsworks and Katonic! 🤝
Hopsworks offers a central repository for managing, sharing, and exploring features across projects. 📊
Katonic, a powerful MLOps platform, automates tasks from model development to deployment and monitoring, ensuring faster and more efficient ML workflows. 🚀
By integrating Hopsworks with Katonic, you can get consistent feature management, improved model quality, and accelerated model development and deployment, resulting in better collaboration and simplified governance. 📈
#MachineLearning #FeatureEngineering #MLOps #Hopsworks #Katonic #DataScience #AI
We are here, and ready, at @PyDataNYC! Come and see hi 🔥
Anytime! Watch #FSS2022! 🚀
We hosted an amazing panel moderated by @jim_dowling from @hopsworks. 'APIs for Feature Stores' discussed the historical evolution and the future of APIs for feature stores.
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