If you are a data scientist or work with machine learning (ML) models, you are equipped with tools to label data, technological environments to train models, and a fundamental grasp of MLops and models. If you have machine learning (ML) models in production, you likely employ ML monitoring to discover data drift and other model hazards.

Data science teams employ these fundamental ML methods and platforms to collaborate on model creation, configure infrastructure, deploy ML models to various settings, and manage models at scale. Those that wish to grow the number of models in production, enhance the quality of predictions and decrease the costs associated with ML model maintenance will likely also require similar ML life cycle management solutions.

However, it is difficult to convey these methods and technologies to corporate stakeholders and budget decision-makers. Leaders who want to understand the return on investment and business effect of machine learning and artificial intelligence initiatives but prefer to remain out of the technical and operational weeds will find this to be technical jargon.

What is the life cycle of machine learning?

As a developer or data scientist, you have an engineering process for providing commercial value from novel concepts. This method involves establishing the issue statement, designing and testing models, deploying models to production settings, monitoring models in production, and enabling maintenance and enhancements. We refer to this as a life cycle process, recognising that deployment is the initial step in achieving business value and that, once models are in production, they are not static and will require continual maintenance.

The word life cycle may not be understood by business leaders. Many continue to see software development and data science efforts as one-time investments, which is one reason why so many firms struggle with tech debt and data quality challenges.

A corporate executive’s eyes will glaze over if you use technical jargon to describe model development, training, deployment, and monitoring to describe the life cycle. Marcus Merrell, vice president of technology strategy at Sauce Labs, advocates using a real-world comparison when communicating with executives.

“Machine learning is somewhat akin to farming: The crops we know now are the result of prior generations seeing trends, experimenting with combinations, and exchanging information with other farmers to make better variants using accumulated knowledge,” he explains. When an algorithm is trained, machine learning involves a similar process of observation, cascade conclusions, and information accumulation.

This model highlights generative learning from one crop year to the next, and it may also account for real-time modifications that may be necessary throughout a growing season due to weather, supply chain, or other variables. Whenever feasible, it may be advantageous to discover comparisons within your sector or an area that your company executives are familiar with.

What exactly is MLops?


Most developers and data scientists view MLops as the machine learning version of DevOps. By automating infrastructure, deployment, and other engineering processes, cooperation is enhanced and teams are able to devote more effort to achieving business objectives.

Much of this, however, is in the weeds for corporate leaders who want a straightforward definition of MLops, particularly when teams require funding for tools or time to build best practices.

“MLops, or machine learning operations, is the discipline of cooperation and communication between data science, IT, and the business to manage the whole life cycle of machine learning initiatives,” explains Alon Gubkin, CTO and co-founder of Aporia. “MLops is the process of coordinating many teams and departments inside an organization to guarantee that machine learning models are deployed and maintained efficiently.”

Thibaut Gourdel, manager of technical product marketing at Talend, recommends including additional information for data-driven business executives. “MLops advocates the use of agile software practices applied to ML projects, such as version control of data and models as well as continuous data validation, testing, and ML deployment to enhance the repeatability and dependability of your models and the productivity of your teams,” he says.

What is data drift?


It is much simpler to relate a phrase to an example of a tale if you can utilize descriptive language. An executive may struggle to apply the concept of drift to the realm of data, statistical distributions, and model accuracy due to analogies such as a boat being blown off course by the wind.

“Data drift happens when the data the model observes in production no longer resembles the previous data it was trained on,” explains the chief AI officer and scientist at Fiddler AI, Krishnaram Kenthapadi. “It can be sudden, as the shift in buying habits caused by the COVID-19 epidemic. Regardless of how the drift occurs, it is crucial to immediately recognize these movements to preserve model accuracy and minimize the commercial effect.”

Gubkin gives a second instance in which data drift is a steady departure from the data used to train the model. Data drift is analogous to a company’s products losing popularity over time due to shifting consumer tastes.

The CTO of John Snow Laboratories, David Talby, presented a generalized comparison. “Model drift occurs when the model’s accuracy diminishes owing to a fluctuating manufacturing environment,” he explains. “Similar to the depreciation of a brand-new automobile the moment it is driven off the lot, a model loses value when the research environment it was trained on acts differently in production. As the world evolves, a model will always require maintenance, regardless of how effectively it operates.”

Leaders in data science must convey the notion that models must be assessed for the correctness and retrained on more recent and relevant data since data is not static.

What is ML surveillance?
How does a company assess quality before packaging and shipping its items to merchants and customers? Manufacturers utilise a variety of methods to spot problems, including when an assembly line’s output quality begins to deviate from acceptable levels. If a machine learning (ML) model is compared to a tiny factory creating forecasts, it makes logical that data science teams want ML monitoring tools to check for performance and quality concerns. Katie Roberts, data science solution architect at Neo4j, explains, “ML monitoring is a set of approaches used during production to identify issues that may severely influence model performance, leading to low-quality insights.”

The parallel between manufacturing and quality control is simple, and here are two ideas for ML model monitoring specifics: “As firms raise their investment in AI/ML efforts, the number of AI models will skyrocket from tens to thousands. According to Hillary Ashton, chief product officer of Teradata, “each must be stored safely and regularly checked to assure correctness.”

What exactly is modelops?


MLops emphasizes the collaboration of diverse teams in designing, implementing, and maintaining models. Yet, how do leaders choose which models to invest in, which models require maintenance, and where to promote openness on the costs and advantages of artificial intelligence and machine learning?

These are governance issues that model techniques and platforms intend to address. Corporate executives desire modelops, but unless it is substantially deployed, they will not completely see their necessity and benefits.

This is problematic, particularly for businesses seeking to invest in modelops platforms. The CEO and managing director of Mphasis, Nitin Rakesh, propose discussing modelops in this manner. By concentrating on modelops, businesses can guarantee that machine learning models are deployed and maintained to optimize value and provide governance across many versions.

Ashton proposes presenting an illustration of a practice. “Modelops enables data scientists to identify and mitigate data quality concerns, recognize automatically when models decline, and plan model retraining,” she explains.

There are still more new ML and AI capabilities, algorithms, and technologies with baffling nomenclature that will infiltrate the vernacular of business leaders. When data professionals and technologists take the effort to explain the terms to business executives in a language they can comprehend, they are more likely to receive collaboration and buy-in for new initiatives.

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