MLOps World Toronto: MLOps Beyond Training Simplifying and Automating the Operational Pipeline

Most data science teams start with building AI models and only think about operationalization later. But taking a production-first approach and automating components is the key to generating measurable ROI for the business. In this talk, Iguazio’s co-founder and CTO, Yaron Haviv, explains how to simplify and automate your production pipeline to bring data science to production faster and more efficiently. He displays real live use cases while going through all the different steps in the process.

Demo - Exploiting a data fabric to drive data literacy and data democratisation

Join Talend experts to learn how to drive data literacy and adoption throughout your organisation with a seamless data fabric. Discover how to balance collaboration, ease of use and governance to deliver trusted data insights and outcomes at the speed of the business.

Performance considerations for loading data into BigQuery

It is not unusual for customers to load very large data sets into their enterprise data warehouse. Whether you are doing an initial data ingestion with hundreds of TB of data or incrementally loading from your systems of record, performance of bulk inserts is key to quicker insights from the data. The most common architecture for batch data loads uses Google Cloud Storage(Object storage) as the staging area for all bulk loads.

People Were Skeptical of Data Warehouses. Now History Is Repeating.

This is a guest post by computer scientist Bill Inmon, recognized as the "father of the data warehouse." Bill has written 65 books in nine languages and is currently building a technology called textual ETL. Many years ago, there were no data warehouses. Most Ecommerce retailers relied on legacy systems with unintegrated data that couldn’t communicate with each other, resulting in data silos. Comparing data sets from these systems was almost impossible.

Differences between the C++ and Java MiNiFi agents

In this video we will go through all the differences between the C++ and Java MiNiFi agents. The video shows the differences observed on the Edge Flow Manager UI ranging from different information to the presence of buttons and dropdown elements determined by the agent type. Differences in feature set and functionality are also highlighted. The two implementations also have different footprints (memory and CPU) as well as a different set of available components. This video will help you determine the MiNiFi agent that best suits your use case.

The Chief Data Officer | Digital Transformation

Today, data isn't a cost center. It's a business driver. And Chief Data Officers are responsible for using data to create real results and transform their business. Meet Ray, the CDO at a high tech global electronics manufacturer. Ray relies on the Cloudera Data Platform to bring multiple data sources together, Ray's company can connect supply chain, go-to-market and product research data in one place, while lowering the cost on their network.

How SightX Uses ClearML to Build AI Drone Models

With the rise of drone usage, it’s easier to take aerial footage than ever before. The resulting data can trigger quick, effective action; removing guesswork and increasing aerial awareness, which can have profound implications on growing profits and trimming expenses. And as drone use rises, so does the usage of AI, to navigate, detect, identify, and track meaningful artifacts and objects.