6 sustainability measures of MLops and how to address them

by | Jun 27, 2022 | Technology

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 – 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Register today!

Artificial intelligence (AI) adoption keeps growing. According to a McKinsey survey, 56% of companies are now using AI in at least one function, up from 50% in 2020. A PwC survey found that the pandemic accelerated AI uptake and that 86% of companies say AI is becoming a mainstream technology in their company.

In the last few years, significant advances in open-source AI, such as the groundbreaking TensorFlow framework, have opened AI up to a broad audience and made the technology more accessible. Relatively frictionless use of the new technology has led to greatly accelerated adoption and an explosion of new applications. Tesla Autopilot, Amazon Alexa and other familiar use cases have both captured our imaginations and stirred controversy, but AI is finding applications in almost every aspect of our world.

The parts that make up the AI puzzle

Historically, machine learning (ML) – the pathway to AI – was reserved for academics and specialists with the necessary mathematical skills to develop complex algorithms and models. Today, the data scientists working on these projects need both the necessary knowledge and the right tools to be able to effectively productize their machine learning models for consumption at scale – which can often be a hugely complicated task involving sophisticated infrastructure and multiple steps in ML workflows. 

Another key piece is model lifecycle management (MLM), which manages the complex AI pipeline and helps ensure results. The proprietary enterprise MLM systems of the past were expensive, however, and yet often lagged far behind the latest technological advances in AI.

Effectively filling that operational capability gap is critical to the long-term success of AI programs because training models that give good predictions is just a small part of the overall challenge. Building ML systems that bring value to an organization is more than this. Rather than the ship-and-forget pattern typical of traditional software, an effective strategy requires regular iteration cycles with continuous monitoring, care and improvement.

Enter MLops (machine learning operations), which enables data scientists, engineering and IT operations teams to work togethe …

Article Attribution | Read More at Article Source

Share This