In this webinar, Mike Boyarski and Eric Hanson of SingleStore describe the promise of machine learning and AI. They show how businesses need to upgrade their data infrastructure for predictive analytics, machine learning, and AI. They then dive deep into using SingleStore to power operational machine learning and AI.
In this blog post, we will first describe how SingleStore helps you master the data challenges associated with machine learning (ML) and artificial intelligence (AI). We’ll then show how to implement ML/AI functions in SingleStore. At any point, feel free to view the (excellent) webinar.
Challenges to Machine Learning and AI
Predictive analytics is helping to transform how companies do business, and machine learning and AI are a huge part of that. The McKinsey Global Institute analysis shows ML/AI having trillions of dollars of impact in industry sectors ranging from telecommunications to banking to retail. AI investments are focused in automation, analytics, and fraud, among other areas.
However, McKinsey goes on to report that only 15% of organizations have the right technology infrastructure, and only 8% of the needed data is available to AI systems across an organization. The vast majority of AI projects have serious challenges in moving from concept to production, and half the time needed to deploy an AI project is spent in preparation and aggregation of large datasets.
The machine learning and AI lifecycle has ten steps, and several of them have data-related challenges. SingleStore addresses many of the toughest ones:
To sum up, key challenges in ML/AI implementation that are addressed by SingleStore include modernizing data infrastructure; simplifying and accelerating query performance against big data; and adding scalability and convergence to the process of operationalizing AI.
Overview of AI/ML Support in SingleStore
SingleStore has features that support key aspects of the machine learning and AI lifecycle:
Here’s an example of using the transforms capability in SingleStore Pipelines:
CREATE PIPELINE mypipeline AS LOAD DATA KAFKA '192.168.1.100:9092/my-topic' WITH TRANSFORM ('http://www.singlestore.com/my-transform.tar.gz', 'my-executable.py', '') INTO TABLE t
For more information, see the SingleStore Documentation.
Image recognition is an important capability enabled by ML/AI, and SingleStore has several customers using this today. You can train the model with other data components that connect well to SingleStore, including Apache Spark, TensorFlow, and Gluon. You can then use your model to extract feature vectors (called embeddings) from images. The feature vectors can then be stored in a SingleStore table for fast processing.
There are several SingleStore functions that are directly useful for vector similarity matching:
SingleStore’s capabilities are applicable to a variety of different job tasks in the machine learning and AI lifecycle.
SingleStore’s connectivity, capabilities, and speed make it a solid choice for machine learning and AI development and deployment.
For more information, view the webinar.