MLops: the key to bringing AI to the mainstream

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One of the main obstacles preventing the company from taking artificial intelligence (AI) into action is the transition from development and training to production environments. To reap real benefits from the technology, it must happen at the speed and scale of today’s business environment, which few organizations are capable of.

This is why the interest in merging AI with devops is gaining momentum. In particular, forward-thinking enterprises are seeking to combine machine learning (ML) with the traditional devops model, which creates a MLops process that streamlines and automates the way intelligent applications are developed and deployed, and then continuously updated to maximize the value of its business. activities over time.

Problem solver

According to data scientist Aymane Hachcham, MLops helps the company solve a number of important problems when it comes to building and managing intelligent applications effectively. For starters, the data sets used in the training phase are extremely large and are continuously expanded and modified. This requires constant monitoring, experimentation, adaptation and retraining of AI models, all of which become time-consuming and expensive under traditional, manually-driven development and production models.

To implement MLops effectively, the enterprise needs to develop a number of core capabilities such as full lifecycle tracking, metadata optimized for model training, hyperparameter logging, and a solid AI infrastructure that includes not only server, storage, and networking solutions, but also software tools capable of rapid iteration of new machine learning models. And all of this will have to be designed around the two main forms of MLops: predictive, which seeks to map future outcomes based on past data, and prescriptive, which seeks to make recommendations before decisions are made.

Mastering this discipline is the only plausible way for AI to seep from the Fortune 500 company to the rest of the world, say Shay Grinfeld and Itay Inbar of Greenfield Partners. The fact is that over 90% of ML projects fail under current development and implementation frameworks, which is simply unsustainable for the vast majority of organizations. MLops provides a significantly more efficient development pipeline that not only reduces the overall cost of the process, but can turn failures into successes at a rapid pace. The end result is that barriers to AI implementation drop to levels that are comfortable for the vast majority of enterprises, leading to widespread distribution and eventual integration into mainstream data operations.

Early Success

MLops is still an emerging field, so it might be tempting to write it off as another tech buzzword, says Sibanjan Das, a business analytics and data science consultant. But the track record so far is pretty good, provided it’s designed properly and focused on the right goal: maximizing model performance and improving ROI. This requires careful coordination between the various components that create an MLops environment, such as the CI/CD pipeline itself, as well as model serving, versioning, and data monitoring. And don’t forget to build robust security and governance mechanisms to minimize the risk of the ML model’s operations and the chance of it being compromised.

While MLops is designed for automation and even autonomy, the human element should not be overlooked as a key driver for successful outcomes. A recent report from Dataiku noted that in the past year, companies have come to realize that they cannot scale AI without building diverse teams that can implement and benefit from the technology. MLops should be a critical part of this strategy as it supports diversification in the development, implementation and management of AI projects. And judging by Gartner’s MLops framework alone, a broad set of skills is required to ensure that the results are of top value to the company’s business model.

Even the most advanced technology is of little value if it cannot be successfully transferred from the lab to the real world. AI is now at the point where it has to make a valuable contribution to humanity, otherwise it becomes the digital equivalent of the Edsel: flashy and full of gadgets but of little practical value.

MLops, of course, cannot guarantee success, but it can reduce the costs of experimentation and failure, while at the same time putting it in the hands of more people who can figure out how to use it.

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