If you have not heard the term MLOps yet, you can bet you will soon. MLOps or Machine Learning Operations, has emerged as the missing link between the functions of Machine Learning (ML), Data Science, and Data Engineering. It is the glue that unifies these functions more seamlessly than ever before.
At Bitstrapped we consider MLOps an engineering function, whose people and systems serve to enable the ability for an organization to continuously and consistently deploy machine learning solutions. It is the combination of an operating framework for people and technology, as well as abidance to a set of best practices and proven architecture principles. More simply put, MLOps is the technology and people systems that power production grade machine learning.
The challenges organizations face today in scaling Machine Learning, are challenges of inconsistent quality, model variability which puts stress on deployment systems, and an inability to scale the many activities that go into model development and deployment.
Prior to MLOps, there was a notable functional breakthrough in engineering operations, that today, we call DevOps. MLOps is very much a similar revolution to DevOps and part of this is apparent in the naming convention. But more than that, DevOps introduced new technologies, automation, and people systems to help shorten the software development lifecycle and provide continuous delivery of high quality software. DevOps decreases time to market of software applications, increases developer productivity due to reduction of manual and tedious processes and activities, and through automation reduces human error and improves software quality.
Similarly, MLOps decreases the time it takes to develop machine learning models by making data more accessible and fluid and reducing time to experiment, iterate, and deploy models. Automation in MLOps also removes many tedious activities that create bottlenecks for data scientists — like cases where data science teams spend 80+% of their time doing data and model preparation on local computers. MLOps automation also introduces a plethora of features to do things like model drift detection, model monitoring, model tuning, and model serving, which similar to DevOps, solve a variety of human error and quality issues.
The main difference of MLOps compared to DevOps is that MLOps systems arguably span more engineering functions than DevOps, making MLOps more challenging for a team to take on without supplemental expertise provided by consulting firms like Bitstrapped. Another difference is that the benefits of DevOps are largely concentrated to working faster and conserving resources. MLOps provides the same for machine learning and more. MLOps not only increases efficiency and productivity but also introduces limitless opportunities for competitive advantage, innovation, and ultimately industry disruption through the IP behind models.
Moral of the story is. If you were late on DevOps, don't be late on MLOps.
Over the last 5 years, organizations have invested heavily in hiring talented Data Scientists to work on machine learning model development, recruiting for talent experienced in tools and libraries such as Scikit-learn, TensorFlow, PyTorch, Jupyter, and Pandas. These Data Scientists are programming models in many established programming languages, including Python, R, SAS, and SQL. These technologies are primarily used for feature engineering, assembling data sets, and running experiments, essentially they enable Data Scientists to develop machine learning models.
The problem here is that most Data Science has been limited to model development, not model deployment. This makes it challenging for Data Science and Machine Learning teams to get their work through to production environments where real business value lies. The specific challenge here is the lack of people and technology systems necessary to integrate teams and enable data scientists to share output with the teams who develop and manage the infrastructure that data scientists need to experiment and deploy models.
Evidence through studies have shown that, the problems mentioned, result in approximately 85% of corporate AI and ML initiatives failing to move beyond the experimentation and testing phases.
From the challenges facing machine learning projects emerges many questions:
To address these challenges we consider the obstacles of awareness, business strategy, and technology solutions as priority in order to build a Machine Learning practice. Before addressing this further, there are fundamental challenges that organizations face that anyone serious about MLOps should be aware of.
The market for MLOps solutions is expected to reach $4 billion by 2025. We are in early days, but the strategy to adopt MLOps will take shape over the next few years. A well-structured MLOps practice makes it easier to align ML models with business needs, people, as well as regulatory requirements. While deploying ML solutions you will uncover sources of revenue, save time, and reduce operational costs. The efficiency of the workflow within your MLOps function will allow you to leverage data analytics for decision-making and build better customer experiences.
Depending on which stage your organization sits in its Machine Learning journey, developing a successful MLOps practice will require an upfront investment in people, technology, and operations. Our tips for building a strong MLOps practice are:
Ultimately, the goal of a good MLOps strategy will be to accelerate the use of Machine Learning in your organization improve your products and serve your customers.
Stay tuned, as we share an ongoing series of blog posts on MLOps, how to adopt it, common architectures, and MLOps technical tutorials.
See related: Benefits of MLOps for Your Business
Have questions about MLOps and how it can help your business? Contact us for a free 30-minute discovery call with our experts so we can explore how an MLOps program can benefit your business. Click here to contact us. See also our post on ML Architecture for Three Stages of MLOps.
Here is a breakdown of the MLOps lifecycle and the stages that machine learning developers and data scientists use to deploy a production application.
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