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Machine learning operations, or MLOps, is the term given to the process of creating, deploying, and maintaining machine learning models. It’s a discipline that combines machine learning, DevOps, and data engineering with the goal of finding faster, simpler, and more effective ways to productize machine learning. When done right, MLOps can help organizations align their models with their unique business needs, as well as regulatory requirements. Keep reading to find out how you can implement MLOps with your team.
What Is MLOps + How Does It Work?
A typical MLOps process looks like this: a business goal is defined, the relevant data is collected and cleaned, and then a machine learning model is built and deployed. Or maybe we should say that’s what a typical MLOps process is supposed to look like, but many organizations are struggling to get it down.
Productizing machine learning, or ML, is one of the biggest challenges in AI practices today. Many organizations are desperate to figure out how to convert the insights discovered by data scientists into tangible value for their business—which is easier said than done.
It requires unifying multiple processes across multiple teams—starting with defining business objectives and continuing all the way through data acquisition and model development and deployment.
This unification is achieved through a set of best practices for communication and collaboration between the data engineers who acquire the data, the data scientists who prepare the data and develop the model, and the operations professionals who serve the models.
Why Do You Need MLOps?
Businesses are dealing with more data than ever before. In a recent study, the IBM Institute for Business Value found that 59% of companies have accelerated their digital transformation. This pivot to digital-first enterprise strategy means continued investments in data, analytics, and AI capabilities have never been more critical.
Leveraging data as a strategic asset can lead to accelerated business growth and increased revenue. According to McKinsey, companies with the greatest overall growth in revenue and earnings receive a significant proportion of that boost from data and analytics. If you’re hoping to replicate this growth and set your business up for sustainable success, ad hoc initiatives and one-off projects won’t cut it. You’ll need a well-planned data strategy that brings the best practices of software development and applies them to data science—which is where MLOps comes in.
MLOps bridges the gap between gathering data and turning that data into actionable business value. A successful MLOps strategy leverages the best of data science with the best of operations to streamline scalable, repeatable machine learning from end to end. It empowers organizations to approach this new era of data with confidence and reap the benefits of machine learning and AI in real life.
In addition to increased growth and revenue, benefits include faster go-to-market times and lower operational costs. With a solid framework for your data science and DevOps teams to follow, managers can spend more time thinking through strategy and individual contributors can be more agile.
What Problems Does MLOps Solve?
Let’s dig into specifics. Applying MLOps best practices solves a variety of the problems that plague businesses around the globe, including:
No matter how your company is organized, it’s likely that your data scientists, software engineers, and operations managers live in very different worlds. This silo effect kills communication, collaboration, and productivity.
Without collaboration, you can forget about simplifying and automating the deployment of machine learning models in large-scale production environments. MLOps solves this problem by establishing dynamic pipelines and adaptable frameworks that keep everyone on the same page—reducing friction and opening up bottlenecks.
As VentureBeat reports, 87% of machine learning models never make it into production. In other words, only about 1 in 10 data scientists’ workdays actually end up producing something of value for the company. This sad statistic represents lost revenue, wasted time, and a growing sense of frustration and fatigue in data scientists everywhere. MLOps solves this problem by first ensuring all key stakeholders are on board with a project before it kicks off. MLOps then supports and optimizes every step of the process, ensuring that each model can journey its way toward production without any lag (and without the never-ending email chains).
We already talked about the silo effect, but it rears its ugly head again here. Creating and serving ML models requires input and expertise from multiple different teams, with each team driving a different part of the process. Without communication and collaboration between everyone involved, key learnings and critical insights will remain stuck within each silo. MLOps solves this problem by bringing together different teams with one central hub for testing and optimization. MLOps best practices make it easy to share learnings that can be used to improve the model and rapidly redeploy.
Lengthy development and deployment cycles mean that, way too often, evolving business objectives make models redundant before they’ve even been fully developed. Or the changing business objectives mean that the ML system needs to be retrained immediately after deployment. MLOps solves these issues by implementing best practices across the entire process—making productizing ML faster at every stage. MLOps best practices also build in room for adjustments, so your models can adapt to your changing business needs.
Misuse of Talent
Data scientists are not software engineers and vice versa. They have different focuses, different skill sets, and very different priorities. Expecting one to perform the tasks of the other is a recipe for failure. Unfortunately, many organizations make this mistake while trying to cut corners or speed up the process of getting machine learning models into production. MLOps solves this problem by bringing both disciplines together in a way that lets each use their respective talents in the best way possible—laying the groundwork for long-term success.
The age of big data is accompanied by the age of intense, ever-changing regulation and compliance systems. Many organizations struggle to meet data compliance standards, let alone remain adaptable for future iterations and addendums. MLOps solves this problem by implementing a comprehensive plan for governance. This ensures that each model, whether new or updated, is compliant with original standards. MLOps also ensures that all data programs are auditable and explainable by introducing monitoring tools.
How Do You Implement MLOps In Your Organization?
Now that you’re sold on the benefits of MLOps, it’s time to figure out how you can bring the discipline to life at your organization.
The good news is that MLOps is still a relatively new discipline, which means even if you are just now getting started you aren’t far behind other organizations. The bad news is that MLOps is still a relatively new discipline, which means there aren’t many tried-and-true formulas for success readily available for you to replicate at your organization. However, ModelOps platforms with ready-to-deploy models can accelerate the MLOps process.
That being said, if you are ready to invest in machine learning there are a few ways you can set your organization up for success. Let’s dive into how to achieve MLOps success in more detail:
Start by looking at your teams to confirm you have the necessary skill sets covered. We’ve already established that productizing ML models require a set of skills that, up until now, organizations have considered separate. So, it’s likely that your data engineers, data scientists, software engineers, and operations professionals will be dispersed throughout various departments.
You don’t need to alter your entire organizational structure to create a MLOps team. Instead, consider creating a hybrid team with cross-functionality. This way you can cover a wide range of skills without too much disruption to your organization. Alternatively, you may choose to use a solution like aiWARE that can rapidly deploy and scale AI within your applications and business processes without requiring AI developers and ML engineers.
Your MLOps team will need to cover 4 main areas:
The first stage in a typical machine learning lifecycle is scoping. This stage consists of scoping out the project by identifying what business problem(s) you are aiming to solve with AI.
This stage usually involves collaborators with a deep understanding of the potential business problems that can be solved with AI such as d-level managers and above. It also usually includes collaborators that are intimately familiar with the data such as senior data scientists.
The second stage in a typical ML lifecycle is data. This stage starts with acquiring the data and continues through cleaning, processing, organizing, and storing the data.
Stage two usually involves both data engineers and data scientists along with product managers.
Stage three in the typical ML lifecycle is modeling. In this stage, the data from stage two is used to train, test, and refine ML models.
This third stage usually involves both data engineers and data scientists (and even ML architects if you have them). It also requires feedback and input from cross-functional stakeholders.
The fourth and final stage in the typical machine learning lifecycle is deployment. Trained models are deployed into production.
This stage usually involves collaborators that have experience with machine learning and the DevOps process, such as machine learning engineers or DevOps specialists.
The exact composition and organization of the team will vary depending on your individual business needs, but the essential part is ensuring that each skillset is covered by someone.
In addition to having the right team, you’ll also need to have the right tools in place to achieve MLOps success. MLOps is a relatively new, rapidly growing field. And, as is often the case in such fields, a large variety of tools have been created to help manage and streamline the processes involved.
When putting together your MLOps toolkit, you’ll need to consider a few different factors such as the MLOps tasks you need to address, the languages and libraries your data scientists will be using, the level of product support you’ll need, which cloud provider(s) you’ll be working with, what AI models and engines to utilize, etc.
Once you build models, you can easily onboard them into a production-ready environment with aiWARE. This option allows you to rapidly deploy models that solve real-world business problems. And flexible API integrations make it easy to customize the solution to your business needs.
How Do I Learn MLOps?
As we’ve already mentioned, MLOps is a rapidly growing field. And that massive growth is only expected to continue—with 60% of companies planning to accelerate their process automation in the next 2 years, according to the IBV Trending Insights report.
This increased investment has made MLOps, or DevOps for machine learning, a necessary skill set at companies in nearly every industry. According to the LinkedIn emerging jobs report, the hiring for machine learning and artificial intelligence roles grew 74% annually between 2015 and 2019. This makes MLOps the top emerging job in the U.S.
And it’s experiencing a talent shortage. There are many factors contributing to the MLOps talent crunch, the biggest being an overwhelming number of platforms and tools to learn, a lack of clarity in role and responsibility, a shortage of dedicated courses for MLOps engineers and an overwhelming number of platforms and tools to learn.
All that to say, if you’re looking to get your foot in the MLOps door there’s no better time than right now. We recommend checking out some of these great resources:
This course, currently available on Coursera, is a great jumping-off point if you’re new to MLOps. Primarily intended for data scientists and software engineers that are looking to develop MLOps skills, this course introduces participants to MLOps tools and best practices for deploying, evaluating, monitoring, and operating production ML systems on Google Cloud.
This course, currently available on Coursera, is for those that have already nailed the fundamentals. It covers deep MLOps concepts as well as production engineering capabilities. You’ll learn how to use well-established tools and methodologies to conceptualize, build and maintain integrated systems that continuously operate in production.
This book, by Mark Treveil and the Dataiku Team, was written specifically for the people directly facing the task of scaling ML in production. It’s a guide for creating a successful MLOps environment, from the organizational to the technical challenges involved.
This seminar series takes a look at the frontier of ML. It aims to drive research focus to interesting questions and stir up conversations around ML topics. Every seminar is live-streamed on YouTube, and they encourage viewers to ask questions in the live chat. Videos of the talks are available on YouTube afterward as well. Past seminars are available for viewing on YouTube as well.
This book, by Andriy Burkov, offers a “theory of the practice” approach. It provides readers with an overview of the problems, questions, and best practices of machine learning problems.
We also highly recommend joining the MLOps community on slack. An open community for all enthusiasts of ML and MLOps, you can learn many interesting things and broaden your knowledge. Both amateurs and professionals alike are welcome to join the conversation.
Want to Learn Even More About MLOps?
Ready to dig into other MLOps resources, follow the blog listings below and/or check out this on-demand webinar: MLOps Done Right: Best Practices to Deploy. Integrate, Scale, Monitor, and Comply.