2 items tagged "MLOps"

  • Helping Business Executives Understand Machine Learning

    Helping Business Executives Understand Machine Learning

    For data science teams to succeed, business leaders need to understand the importance of MLops, modelops, and the machine learning life cycle. Try these analogies and examples to cut through the jargon.

    If you’re a data scientist or you work with machine learning (ML) models, you have tools to label data, technology environments to train models, and a fundamental understanding of MLops and modelops. If you have ML models running in production, you probably use ML monitoring to identify data drift and other model risks.

    Data science teams use these essential ML practices and platforms to collaborate on model development, to configure infrastructure, to deploy ML models to different environments, and to maintain models at scale. Others who are seeking to increase the number of models in production, improve the quality of predictions, and reduce the costs in ML model maintenance will likely need these ML life cycle management tools, too.

    Unfortunately, explaining these practices and tools to business stakeholders and budget decision-makers isn’t easy. It’s all technical jargon to leaders who want to understand the return on investment and business impact of machine learning and artificial intelligence investments and would prefer staying out of the technical and operational weeds.

    Data scientists, developers, and technology leaders recognize that getting buy-in requires defining and simplifying the jargon so stakeholders understand the importance of key disciplines. Following up on a previous article about how to explain devops jargon to business executives, I thought I would write a similar one to clarify several critical ML practices that business leaders should understand.   

    What is the machine learning life cycle?

    As a developer or data scientist, you have an engineering process for taking new ideas from concept to delivering business value. That process includes defining the problem statement, developing and testing models, deploying models to production environments, monitoring models in production, and enabling maintenance and improvements. We call this a life cycle process, knowing that deployment is the first step to realizing the business value and that once in production, models aren’t static and will require ongoing support.

    Business leaders may not understand the term life cycle. Many still perceive software development and data science work as one-time investments, which is one reason why many organizations suffer from tech debt and data quality issues.

    Explaining the life cycle with technical terms about model development, training, deployment, and monitoring will make a business executive’s eyes glaze over. Marcus Merrell, vice president of technology strategy at Sauce Labs, suggests providing leaders with a real-world analogy.

    Machine learning is somewhat analogous to farming: The crops we know today are the ideal outcome of previous generations noticing patterns, experimenting with combinations, and sharing information with other farmers to create better variations using accumulated knowledge,” he says. “Machine learning is much the same process of observation, cascading conclusions, and compounding knowledge as your algorithm gets trained.”

    What I like about this analogy is that it illustrates generative learning from one crop year to the next but can also factor in real-time adjustments that might occur during a growing season because of weather, supply chain, or other factors. Where possible, it may be beneficial to find analogies in your industry or a domain your business leaders understand.

    What is MLops?

    Most developers and data scientists think of MLops as the equivalent of devops for machine learning. Automating infrastructure, deployment, and other engineering processes improves collaborations and helps teams focus more energy on business objectives instead of manually performing technical tasks.

    But all this is in the weeds for business executives who need a simple definition of MLops, especially when teams need budget for tools or time to establish best practices.

    “MLops, or machine learning operations, is the practice of collaboration and communication between data science, IT, and the business to help manage the end-to-end life cycle of machine learning projects,” says Alon Gubkin, CTO and cofounder of Aporia. “MLops is about bringing together different teams and departments within an organization to ensure that machine learning models are deployed and maintained effectively.”

    Thibaut Gourdel, technical product marketing manager at Talend, suggests adding some detail for the more data-driven business leaders. He says, “MLops promotes the use of agile software principles applied to ML projects, such as version control of data and models as well as continuous data validation, testing, and ML deployment to improve repeatability and reliability of models, in addition to your teams’ productivity.”

    What is data drift?

    Whenever you can use words that convey a picture, it’s much easier to connect the term with an example or a story. An executive understands what drift is from examples such as a boat drifting off course because of the wind, but they may struggle to translate it to the world of data, statistical distributions, and model accuracy.

    “Data drift occurs when the data the model sees in production no longer resembles the historical data it was trained on,” says Krishnaram Kenthapadi, chief AI officer and scientist at Fiddler AI. “It can be abrupt, like the shopping behavior changes brought on by the COVID-19 pandemic. Regardless of how the drift occurs, it’s critical to identify these shifts quickly to maintain model accuracy and reduce business impact.”

    Gubkin provides a second example of when data drift is a more gradual shift from the data the model was trained on. “Data drift is like a company’s products becoming less popular over time because consumer preferences have changed.”

    David Talby, CTO of John Snow Labs, shared a generalized analogy. “Model drift happens when accuracy degrades due to the changing production environment in which it operates,” he says. “Much like a new car’s value declines the instant you drive it off the lot, a model does the same, as the predictable research environment it was trained on behaves differently in production. Regardless of how well it’s operating, a model will always need maintenance as the world around it changes.” 

    The important message that data science leaders must convey is that because data isn’t static, models must be reviewed for accuracy and be retrained on more recent and relevant data.

    What is ML monitoring?

    How does a manufacturer measure quality before their products are boxed and shipped to retailers and customers? Manufacturers use different tools to identify defects, including when an assembly line is beginning to show deviations from acceptable output quality. If we think of an ML model as a small manufacturing plant producing forecasts, then it makes sense that data science teams need ML monitoring tools to check for performance and quality issues. Katie Roberts, data science solution architect at Neo4j, says, “ML monitoring is a set of techniques used during production to detect issues that may negatively impact model performance, resulting in poor-quality insights.”

    Manufacturing and quality control is an easy analogy, and here are two recommendations to provide ML model monitoring specifics: “As companies accelerate investment in AI/ML initiatives, AI models will increase drastically from tens to thousands. Each needs to be stored securely and monitored continuously to ensure accuracy,” says Hillary Ashton, chief product officer at Teradata. 

    What is modelops?

    MLops focuses on multidisciplinary teams collaborating on developing, deploying, and maintaining models. But how should leaders decide what models to invest in, which ones require maintenance, and where to create transparency around the costs and benefits of artificial intelligence and machine learning?

    These are governance concerns and part of what modelops practices and platforms aim to address. Business leaders want modelops but won’t fully understand the need and what it delivers until its partially implemented.

    That’s a problem, especially for enterprises that seek investment in modelops platforms. Nitin Rakesh, CEO and managing director of Mphasis suggests explaining modelops this way. “By focusing on modelops, organizations can ensure machine learning models are deployed and maintained to maximize value and ensure governance for different versions.“

    Ashton suggests including one example practice. “Modelops allows data scientists to identify and remediate data quality risks, automatically detect when models degrade, and schedule model retraining,” she says.

    There are still many new ML and AI capabilities, algorithms, and technologies with confusing jargon that will seep into a business leader’s vocabulary. When data specialists and technologists take time to explain the terminology in language business leaders understand, they are more likely to get collaborative support and buy-in for new investments.

    Author: Isaac Sacolick

    Soruce: InfoWorld

  • MLOps in a Nutshell

    MLOps in a Nutshell

    Data is becoming more complex, and so are the approaches designed to process it. Companies have access to more data than ever, but many still struggle to glean the full potential of insights from what they have. Machine learning has stepped in to fill the gap. However, the lifecycle falls apart at the deployment stage more often than not, thanks to heavy reliance on manual processes. MLOps is a methodology designed to solve the challenge of deployment. Here’s how it came about and what you need to know to get started.

    What is MLOps?

    Before understanding MLOps, let’s back up. The term comes from combining “machine learning” and “DevOps.” And DevOps revolutionized the way developers built, deployed, and iterated software by prioritizing automation and continuous improvements. While different companies express the principles slightly differently, it builds on four basic principles:

    • Automation
    • Collaboration and communication
    • Continuous improvement and waste minimization
    • Build with the end (i.e., user need) in mind

    MLOps takes those ideas and builds them into the machine learning lifecycle. Instead of relying on manual processes to drive the lifecycle, MLOps focuses on reducing the number of steps between building and deploying.

    The core concepts of MLOps

    If you didn’t come into the field first through coding and development, some of the core concepts of MLOps may be foreign. These are critical concepts taken from the DevOps world.

    Continuous integration: Merging code changes into a central repository. In DevOps, this triggers automatic validation tests. In MLOps, this concept expands to data and model validation.

    • Continuous delivery: Code changes are automatically built, tested, and prepared for production during short iterations. It ensures code can be released automatically at any time.
    • Continuous training: Automatically retraining models in production so that the end result is ready to deploy without manual interventions.
    • Continuous testing: Evaluating the product at every stage of the lifecycle facilitates continuous delivery.
    • Continuous monitoring: Another automatic part of the development and deployment process, monitoring at all stages ensures models perform in the wild and revert to previous iterations in case of failure.
    • Reusable infrastructure: Standardizing infrastructure prevents starting over from square one each time a new model is necessary. 
    • Reproducible environments: Ensuring versioning, fault tolerance, and governance without sacrificing fast, efficient development.

    Why use MLOps

    When we talk about the machine learning lifecycle, the usual suspects appear—data cleaning, modeling, gathering—but the deployment state often remains elusive. Very few machine learning projects actually make it into production thanks to a combination of:

    • Lack of data engineering skills or support if the data science team doesn’t have explicit training in engineering.
    • Teams working in silos. Even if there’s a recommended ratio of data scientists to developers to data engineers, the teams sometimes work in silos from each other and business teams.
    • Tool and application sprawl.

    Companies often struggle to balance conducting data science and machine learning projects to the best of their ability and to remember the business focus. Companies need to close the loop between extracting insights and turning those insights into actionable steps that turn into business value. 

    MLOps helps bridge that gap by leveraging skills from the IT side and the business side. By fostering a deep sense of collaboration, projects keep business value at the forefront. It also helps keep regulatory considerations at the forefront while ensuring that IT can concentrate on what it does best.

    Plus, MLOps ensures a consistent feedback loop once the solution reaches production. Improvements help create better iterations over time with fewer bottlenecks in between. This critical step allows MLOps to put machine learning into production and then scale.

    Introducing MLOps to your organization

    Beginning MLOps streamlines the machine learning process, but you should ask a few questions before starting.

    • What benchmarks will best serve you as you establish MLOps? A key component of MLOps is the concept of continuous improvement. With strategic KPIs, your data science team will understand the direction and goal of each project, and operations will see where (and when) projects need to pivot.
    • Who will monitor each component? The collaboration component implies that continuous monitoring happens. MLOps requires a clear chain of responsibility as models are built, deployed, and retrained. In addition, monitoring business value ensures MLOps retains its primary goal: to deliver value for customers.
    • What safeguards are in place to ensure compliance? MLOps automates much of the traditional machine learning process, but this does introduce some risk. MLOps should be explainable and open to audits, as well as well-documented. Reproducible environments preserve the state in sensitive cases and ensure version control.

    The end goals of MLOps

    The purpose of MLOps is streamlining and reducing waste, but what does that look like in practice? Some concrete examples of what MLOps can do for an organization include:

    • Reducing both the time and the complexity of putting machine learning models into production. With such a small percentage of machine learning models making it to production, much less scaling, this is a critical goal.
    • Enhancing collaboration: Silos are the death of insights. MLOps can foster deeper cooperation between IT and business users.
    • Automating previous laborious manual processes in machine learning development and deployment.
    • Standardizing the process to ensure compliance with regulations, governance policies, and best practices without increasing time to deployment.
    • Increasing the rate of innovation. Focusing on continuous iterations allows teams to get to delivery without taking months.
    • Managing the entire machine learning lifestyle through automation.

    Building MLOps into your company’s operations

    DevOps changed the software development world, and MLOps is doing the same for machine learning. As more companies turn to ML for business initiatives, MLOps could become the go-to methodology for extracting value and keeping things on track.

    Author: Elizabeth Wallace

    Source: Open Data Science

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