In this special guest feature, Aparna Dhinakaran, Chief Product Officer at Arize AI, explains five of the biggest misconceptions surrounding machine learning observability. Arize AI is a startup focused on ML Observability. Aparna was previously an ML engineer at Uber, Apple, and Tubemogul (acquired by Adobe).
"Over the last year, we've spent countless hours with ML engineers to understand and improve the results of their machine learning initiatives.
From upstarts that are building their entire businesses around the use of ML to some of the world's largest financial institutions, ML techniques are increasingly powering crucial pieces of technology that people interact with daily..."
The discipline of predictive analytics is likely to increase in importance as it is complemented by artificial intelligence. NHS and National Express case studies prove the point
"While in data analytics terms, tools for activities such as data extraction and exploration are quite mature and well adopted, the situation for predictive and prescriptive analytics is quite another story.
Predictive capabilities make it possible to forecast future events based on past and present performance, while prescriptive, or instructive, analytics offerings examine data to enable organisations to answer questions such as 'what should we do?'..."
Among the techniques to speed AI deployment, companies can deploy ready-made AI software, including pre-trained models and scripts.
"IT plays a critical role in setting up companies for success in artificial intelligence. Learning from early adopters' best practices can help enterprises sidestep common pitfalls when starting new AI projects.
A few predictable issues are often at play when new AI initiatives stall out. Some of the most common challenges are hitting snags that delay projects from getting started, not having the right AI infrastructure and tools, workflow bottlenecks that stifle data scientist productivity, and failing to control costs..."
How do you decide which Artificial Intelligence projects matter most? When was the last time you re-prioritized your AI projects? Consider this advice
"On-demand, scalable, and economic cloud storage and computation has enabled the efficient processing of huge data sets to draw critical insights using Artificial Intelligence (AI). Launching multiple AI initiatives is par for the course today. After all, some will not succeed. But how do you choose where to devote your resources?..."
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