Introduction to streaming for data scientists
As machine learning moves towards real-time, streaming technology is becoming increasingly important for data scientists. Like many people coming from a mach...
As machine learning moves towards real-time , streaming technology is becoming increasingly important for data scientists. Like many people coming from a machine learning background, I used to dread streaming. In our recent survey, almost half of the data scientists we asked said they would like to move from batch prediction to online prediction but can’t because streaming is hard, both technically and operationally. Phrases that the streaming community take for granted like “time-variant results”, “time travel”, “materialized view” certainly don’t help. Over the last year, working with a co-f
Explore this link on the map →related reading
- Real-time machine learning: challenges and solutionshuyenchip.com
- Machine learning is going real-timehuyenchip.com
- What Is Streaming Data? - Streaming Data Explained - AWSaws.amazon.com
- Streamlit • A faster way to build and share data appsstreamlit.io
- GlassFlow | ClickHouse Data Ingestion: CDC, Backfill & Schema Evolutionglassflow.dev
- Streaming ingestion to a materialized view - Amazon Redshiftdocs.aws.amazon.com
- MosaicML StreamingDataset: Fast, Accurate Streaming of Training Data from Cloud Storage | Databricks Blogmosaicml.com
- The Distributed Computing Manifesto | All Things Distributedallthingsdistributed.com
- Evolution of ML Fact Store. by Vivek Kaushal | by Netflix Technology Blog | Netflix TechBlognetflixtechblog.com
- Mediummaximebeauchemin.medium.com
- Materialized views are obviously useful – Sophie Alpertsophiebits.com
- Why data scientists shouldn’t need to know Kuberneteshuyenchip.com