Systems | Development | Analytics | API | Testing

Scaling NLP Pipelines at IHS Markit - MLOps Live #17

The data science team at IHS Markit will be sharing practical advice on building sophisticated NLP pipelines that work at scale. Using a robust and automated MLOps process, they run complex models that make massive amounts of unstructured data searchable and indexable. In this session, they will share their journey with MLOps and provide practical advice for other data science teams looking to.

A Finance Leader's Guide to Data Modernization

In today’s tech-forward companies, CFOs are tasked with managing and overseeing an increasingly expansive domain of systems and technologies to thrive. The rise of regulatory considerations, novel market drivers and a globally connected business environment is creating an entirely new set of pressures on both the structure of the department and on leadership.

4 Tips for Recognizing and Avoiding Analytics Bias

One of the key cornerstones of the emerging field of ethical, explainable AI is recognizing and avoiding bias. As AI takes on a greater role in organizations with sometimes opaque calculations, there is an increased urgency in many businesses to get ahead of these challenges, and companies, such as IBM, Salesforce and Microsoft, have already added roles specifically with the aim of ensuring that ethics are a key consideration of AI.

Fighting Financial Crime and Earning Trust Using Data-Driven Compliance

One of the most challenging and complex elements of operating a financial services institution is compliance. Managing risk, security and privacy to earn customers’ trust has long been at the core of financial services, but this foundation has been shaken over recent years.

AI and ML: No Longer the Stuff of Science Fiction

Artificial Intelligence (AI) has revolutionized how various industries operate in recent years. But with growing demands, there’s a more nuanced need for enterprise-scale machine learning solutions and better data management systems. The 2021 Data Impact Awards aim to honor organizations who have shown exemplary work in this area.

Introducing Continual Integration for dbt

Today we’re pleased to announce Continual Integration for dbt. We believe this is a radical simplification of the machine learning (ML) process for users of dbt and presents a well-defined path that bridges the gap between data analytics and data science. Read on to learn more about this integration and how you can get started.

Automating MLOps for Deep Learning

MLOps holds the key to accelerating the development, deployment and management of AI, so that enterprises can derive real business value from their AI initiatives. Deploying and managing deep learning models in production carries its own set of complexities. In this talk, we will discuss real-life examples from customers that have built MLOps pipelines for deep learning use cases. For example, predicting rainfall from CCTV footage to prevent flooding.