Artificial Intelligence innovation continues apace – with explosive growth in virtually all industries. So what did the last year bring, and what can we expect from AI in 2021?
Copyright by www.forbes.com
In this article, I list five trends that I saw developing in 2020 that I expect will be even more dominant in 2021.
MLOps
MLOps (“Machine Learning Operations”, the practice of production Machine Learning) has been around for some time. During 2020, however, COVID-19 brought a new appreciation for the need to monitor and manage production Machine Learning instances. The massive change to operational workflows, inventory management, traffic patterns, etc. caused many AIs to behave unexpectedly. This is known in the MLOps world as Drift – when incoming data does not match what the AI was trained to expect. While drift and other challenges of production ML were known to companies that have deployed ML in production before, the changes caused by COVID caused a much broader appreciation for the need for MLOps. Similarly, as privacy regulations such as the CCPA take hold, companies that operate on customer data have an increased need for governance and risk management. Finally, the first MLOps community gathering – the Operational ML Conference – which started in 2019, also saw a significant growth of ideas, experiences, and breadth of participation in 2020.
Low Code/No Code
AutoML (automated machine learning) has been around for some time. AutoML has traditionally focused on algorithmic selection and finding the best Machine Learning or Deep Learning solution for a particular dataset. Last year saw growth in the Low-Code/No-Code movement across the board, from applications to targeted vertical AI solutions for businesses. While AutoML enabled building high-quality AI models without in-depth Data Science knowledge, modern Low-Code/No-Code platforms enable building entire production-grade AI-powered applications without deep programming knowledge. […]
Read more: www.forbes.com
Artificial Intelligence innovation continues apace – with explosive growth in virtually all industries. So what did the last year bring, and what can we expect from AI in 2021?
Copyright by www.forbes.com
MLOps
MLOps (“Machine Learning Operations”, the practice of production Machine Learning) has been around for some time. During 2020, however, COVID-19 brought a new appreciation for the need to monitor and manage production Machine Learning instances. The massive change to operational workflows, inventory management, traffic patterns, etc. caused many AIs to behave unexpectedly. This is known in the MLOps world as Drift – when incoming data does not match what the AI was trained to expect. While drift and other challenges of production ML were known to companies that have deployed ML in production before, the changes caused by COVID caused a much broader appreciation for the need for MLOps. Similarly, as privacy regulations such as the CCPA take hold, companies that operate on customer data have an increased need for governance and risk management. Finally, the first MLOps community gathering – the Operational ML Conference – which started in 2019, also saw a significant growth of ideas, experiences, and breadth of participation in 2020.
Low Code/No Code
AutoML (automated machine learning) has been around for some time. AutoML has traditionally focused on algorithmic selection and finding the best Machine Learning or Deep Learning solution for a particular dataset. Last year saw growth in the Low-Code/No-Code movement across the board, from applications to targeted vertical AI solutions for businesses. While AutoML enabled building high-quality AI models without in-depth Data Science knowledge, modern Low-Code/No-Code platforms enable building entire production-grade AI-powered applications without deep programming knowledge. […]
Read more: www.forbes.com
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