Machine Learning

What risk managers need to know about AI governance

As more businesses begin to realize the full potential of AI to deliver business results from their data, they're starting to bump up against their ability to manage it all. As the amount of data and number of models grow, organizations can accrue significant technical debt. Chief risk officers (CROs) and model risk managers can be left asking themselves, "Do I spend more to keep up with model demand, or do I accept more risk?"

Dissecting the need for ethical AI

Until recently, topics like data ethics and ethics in AI were limited to academic circles and non-profit organizations rallying for citizen data rights. Fast forward to 2020, and the scenario is very different; AI ethics has become a mainstream topic that's a top priority for big organizations. With data collection and processing capabilities growing by the day, it's become easier than ever to train machine learning (ML) models on this collected data. However, organizations have come to realize that, without building transparency, explainability, and impartiality into their AI models, they're likely to do more harm than good to their business. This podcast will explore why ethical AI is the need of the hour, and what key factors AI leaders should consider before implementing AI in their organization's ecosystem.

Handling Large Datasets in Data Preparation & ML Training Using MLOps

Data science has become an important capability for enterprises looking to solve complex, real-world problems, and generate operational models that deliver business value across all domains. More and more businesses are investing in ML capabilities, putting together data science teams to develop innovative, predictive models that provide the enterprise with a competitive edge — be it providing better customer service or optimizing logistics and maintenance of systems or machinery.


Machine Learning Guide: Choosing the Right Workflow

Machine learning (ML) and analytics make data actionable. Without it, data remains an untapped resource until a person (or an intelligent algorithm) analyzes that data to find insights relevant to addressing a business problem. For example, amidst a network outage crisis a historical database of network log records is useless without analysis. Resolving the issue requires an analyst to search the database, apply application logic, and manually identify the triggering series of events.


Algorithmia ML Model Performance Visualization Made Easy with This InfluxDB Template

Measuring your machine learning model will help you understand how well your model is doing, how useful it is, and whether your model can perform better with more data. This is what Algorithmia Insights — a feature of Algorithmia Enterprise MLOps platform — does. Algorithmia platform accelerates your time to value for ML by delivering more models quickly and securely, as it is estimated that 85% of machine learning models never make it to production.

Lessons Learned on Operationalizing Machine Learning at Scale with IHS Markit

According to Gartner, over 80% of data science projects never make it to production. This is the main problem that enterprises are facing today, when bringing data science into their organization or scaling existing projects. In this session, Senior Data Scientist Nick Brown will share his lessons learned from operationalizing machine learning at IHS Markit. He will discuss the functional requirements required to operationalize machine learning at scale, and what you need to focus on to ensure you have a reliable solution for developing and deploying AI.

The Importance of Data Storytelling in Shaping a Data Science Product

Artificial intelligence and machine learning are relentlessly revolutionizing marketplaces and ushering in radical, disruptive changes that threaten incumbent companies with obsolescence. To maintain a competitive edge and gain entry into new business segments, many companies are racing to build and deploy AI applications.