The Rise of MLOps

What is MLOps?

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.
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Moral of the story is. If you were late on DevOps, don't be late on MLOps.

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Why Invest in a MLOps Strategy?‍

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:

• What are the reasons machine learning is still in the experimental stages for most organizations? 
• What are the ways to improve the success rate of deploying Machine Learning in production?
• How can we quickly build models, deploy them, adjust results and repeat?

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.

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Technical and Process Challenges in Failed Machine Learning Projects

• Machine learning investments made by organizations are heavily skewed towards data science hires. Although they are important, other critical areas like infrastructure and automation are under-represented.
• Organizations are capable of developing models. However they lack an operating framework that integrates the people culture and technology necessary to commercialize these models.
• Due to the large supply of data science expertise in the market, hiring is cost effective, relatively fast, and approachable. Consequently, there is a strong understanding of data science amongst organizations, leading to a bias towards data science projects. This has caused somewhat of an organizational tunnel vision, with the blindspots being on the infrastructure and systems side.

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Fundamental Challenges of MLOps

Data Management and Dependencies: One of the central challenges in MLOps is the management of data and its dependencies. Data, a vital fuel for machine learning models, changes so frequently over time, presenting challenges in tracking, versioning, and ensuring its quality over time. Handling these dependencies and maintaining data lineage is essential for reproducibility and reliability in machine learning pipelines.

Skill Divergence: Data scientists are masters in statistics, mathematics, and problem-solving with data, yet they may lack the engineering skills required for deploying and maintaining production-ready ML systems. Bridging this skill gap between data science and software engineering is crucial for the successful implementation of MLOps practices.

Escalating Data Volumes: The exponential growth in data volumes, with a tenfold increase in just a year, poses a significant challenge. As data becomes more extensive and complex, extracting meaningful insights becomes akin to finding a needle in a haystack. Effectively separating signal from noise in such data deluges is a formidable task, demanding advanced data processing techniques and infrastructure.

Real-time ML Processing: Achieving real-time machine learning data processing is a costly endeavor that necessitates specialized skills and resources. Processing data in real-time demands not only robust infrastructure but also a rare set of talent proficient in stream processing, distributed computing, and low-latency systems design.

Data Literacy and Alignment: Establishing a common data language and fostering data literacy across an organization can be a challenging endeavor. Misaligned terminologies, differing interpretations of data, and varying levels of data proficiency among teams can lead to misunderstandings and hinder effective collaboration. Aligning data understanding and fostering a shared data culture are essential for MLOps success.

Addressing these fundamental challenges in MLOps requires a strategic approach that combines technical solutions, skill development, and cultural alignment. Organizations must invest in data infrastructure, provide training opportunities for data scientists to acquire engineering skills, scale their systems to handle growing data volumes, allocate resources to real-time data processing initiatives, and promote a shared data lexicon to ensure that the promise of MLOps can be fully realized in the ever-evolving landscape of data and machine learning.

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Get familiar with the key activities of MLOps

Data gathering: MLOps begins with the crucial step of collecting data. This involves identifying relevant data sources, acquiring the data, and ensuring its quality and consistency. Data gathering often involves working with databases, APIs, or other data storage systems to fetch the necessary information.

Data analysis: Data analysis includes exploring the dataset to understand its structure, identifying patterns, visualizing data distributions, and performing statistical tests. This step provides insights into the data's characteristics and helps in making informed decisions about feature engineering and modeling.

Data transformation/preparation: Raw data is rarely suitable for machine learning models. Steps need to be taken to make the data understandable for ML models. Data transformation and preparation involve tasks such as cleaning data (handling missing values and outliers), encoding categorical variables, scaling or normalizing features, and creating new features through feature engineering. The goal is to make the data ready for modeling.

Model training & development: Model training is the heart of machine learning. During this phase, data is used to train machine learning models. Different algorithms and techniques are applied to learn patterns from the data and build predictive models. Hyperparameter tuning and cross-validation are common practices to optimize model performance.

Model validation: After training a model, it's crucial to assess its performance accurately. Model validation involves splitting the data into training and testing sets or employing techniques like k-fold cross-validation to evaluate how well the model generalizes to new, unseen data. Various metrics, such as accuracy and precision, are used to measure model performance.

Model serving: Once a model is deemed suitable, it will be deployed for use in real-world applications. Model serving involves making the trained model accessible via APIs or other interfaces so that it can provide predictions or classifications in real-time. Scalability, latency, and reliability are some of the important considerations in this phase.

Model monitoring: After deployment, models need continuous monitoring. This entails tracking their performance, identifying concept drift (changes in data distribution), and detecting anomalies. Model monitoring ensures that the model remains effective and accurate over time and enables timely updates or retraining as needed.

Model re-training: Data is not static, and models can become outdated. Model re-training involves periodically updating models with fresh data to keep them relevant and accurate. It's an iterative process that ensures the model's continued usefulness and effectiveness.

Read more at A Comprehensive Guide to Machine Learn Infrastructure.

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The Future of MLOps

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:

• Bring together Data Science, Data Engineering, DevOps and business teams to draft a framework and architecture to uniquely support machine learning success within the organization.
• With a solid framework in place, automate model development and deployment with MLOps for faster go-to-market time and lower operational costs
• Build metrics to measure success of Machine Learning deployments built on MLOps infrastructure. Consider productivity of model development, number of models that reach production, tools in place to analyze results of analysis and redeployment. 
• Leverage open-source tools and products available on the public cloud to build the right MLOps workflow for your ML needs.
  

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

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Have questions about MLOps and ML Strategy?

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.

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