In this article, learn how to help accelerate your financial services business growth through operational excellence with fast, scalable, and measurable efficiencies delivered through MLOps technology.
The thriving financial services industry is no stranger to risk and uncertainty. Whether it's a big boom in the economy, a stock market crash, or even just the random threat of terrorism and crime — financial institutions exist in a world full of unknowns. Moving into 2018, finance executives need to focus more on effectively managing risk than ever before: like cybersecurity threats, fraud, and compliance failures. But as secure as these companies want to make themselves, outside factors such as natural disasters, human error, and widespread privacy breaches are always looming over them.
All of this has led firms to adopt what's known as "MLOps" — otherwise known as Machine Learning (Model-Based) Operations. This method of handling uncertainty brings an exponential level of efficiency to the way businesses operate on the large-scale level: which can have dire positive consequences if executed correctly.
According to Cognilytica, the global MLOps market will be worth $4 billion by 2025. The industry was worth $350 million in 2019, giving a CAGR of about 50%. The industry's size for 2020 is calculated using a CAGR of 50% as $525.29 million. But many firms are unsure how to get started.
In this blog post, you will get an overview on:
Ok, let's get started!
What is MLOps?
MLOps, an acronym for Machine Learning Operations, is a set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently. The word is an amalgamation of "machine learning" and the continuous development practice of DevOps in the software field.
MLOps is a culture and a set of practices and focuses on three primary areas:
- ML technology
The goal of MLOps is to provide companies with a means to rapidly deploy machine learning models into their production environments while ensuring that they can continuously improve these models over time.
In simple terms, MLOps is the combination of machine learning and operations. It encompasses both data science and software operations. At the same time, Operations encompasses all the activities involved in running applications, such as setting up servers, implementing infrastructure, and managing application performance.
For those familiar with DevOps, MLOps represents a natural extension of existing practices such as continuous integration (CI) and automated software deployment (ASD) in machine learning systems.
Watch this video from our Product Manager for HALO, Saifudeen Khan, to understand what is MLOps and why it should be adopted for machine learning systems. Listen to the video and let us know your views or questions in the video comments section.
MLOps, DataOps & DevOps: Why do financial services need them?
At the heart of digital transformation is the idea that technology can and should be used to automate manual tasks and processes.
The rise of data science and machine learning has accelerated this trend, as the ability to use data and algorithms to optimize decisions has become increasingly important.
But what does it all mean for financial services companies?
For starters, financial institutions realize that they can't build these capabilities in-house, as there's a lack of talent with the right skill set, not to mention that building these teams internally would take years and cost millions.
Enter MLOps, DataOps & DevOps — three terms that have emerged in recent years to describe three distinct phases of an organization's infrastructure and IT implementation:
1. MLOps - Machine Learning Operations:
Machine learning is a framework for developing models that make decisions based on data. The management and deployment of machine learning models into production environments in an automated fashion is MLOps.
On the whole, MLOps, involves intelligent programming machines to do the menial tasks that are costly in terms of time and money for humans to perform. It is primarily concerned with automating processes to increase speed, accuracy, and efficiency.
2. DataOps - Data Operations:
At its core, DataOps is all about automation. Here we automate all the ETL processes (Extraction, Transformation, Loading), data wrangling, model training, and deployment. By doing this in an automated fashion, we can save considerable time in our day-to-day jobs.
3. DevOps- Development Operations:
MLOps and DevOps are closely related, but they focus on very different aspects of the development process. The goal of MLOps is to reduce downtime through automation of machine learning systems, while the goal of DevOps is to reduce the manual intervention of software systems.
What's the role of MLOps in Financial Services?
Machine learning in Financial Services has been around for a while, but it has been mostly applied to solve specific problems. These problems have been relatively narrow, such as fraud detection, predictive maintenance of legacy systems, and yield management.
Recently, however, MLOps has emerged as a broad class of solutions that includes machine learning and automation, cloud adoption, and data engineering. These solutions help Financial Services organizations move beyond incremental improvements and into the realm of radical innovation.
As financial services businesses evolve toward digital transformation, they are faced with the challenge of creating a resilient and agile environment for their IT systems. As businesses adopt and leverage new technologies, it is essential to manage them effectively. Management of IT is not a single activity; rather, it requires an approach that can provide a holistic view of the entire environment in addition to ongoing optimization and troubleshooting.
Troubleshooting is key to quickly identifying and fixing issues before they escalate. However, traditional approaches to troubleshooting—such as sending alerts via email or paging administrators—are not an efficient way to manage this process. As a result, organizations are looking for new ways to automate troubleshooting and improve the quality of production environments at scale.
As data volumes continue to grow rapidly with new technologies such as IoT, AI, robotics, and conversational interfaces, MLOps is emerging as a key piece of infrastructure for financial services companies to take advantage of these new opportunities. MLOps enables real-time data analysis from multiple sources across all parts of an organization – business users, IT operations teams, developers, quality assurance/testing, and security analysts. It helps with decision-making at every stage of the application lifecycle. It also allows organizations to take untapped human potential by automating mundane tasks that require skilled resources or subject matter expertise.
What are the benefits of adapting MLOps for financial services?
We're still in the early days of MLOps. But one early benefit is Speed.
Let's say that your bank has 1 million customers and over a few billion customer transaction records. Each one of the transactions is different in nature. Now, imagine, you or your bank's CFO wants to know, on average, how much money each customer spends each month. Also, you want to know who are low-value customers who spend less than $100 each month. And what offer should we provide them to increase the average revenue? In Real-time. That sounds like a tough ask, right?
Though a human can read the balance sheet and interpret the data, it would take a very long time for an analyst to get that information. And if there's more than one product to analyze or more than one type of customer, it could take forever. If you think your "IT Team" will take care. Then you are likely to fail miserably.
The operational difficulty your IT team has to go through to obtain this information is a nightmare for them. In most cases, operations problems usually involve one or more of the following: big data, scalability, reliability, performance, and accessibility. And it could take months and years to get all in place.
But, what we there was a turnkey solution for these problems? So how do you make all of these complex calculations and get answers quickly?
Boom! MLOps, or Machine Learning Operations, is the answer to these problems. MLOps combines three distinct disciplines: data science, machine learning, and operations to simplify data problems.
MLOps can solve these problems by automatically tuning the algorithm, identifying bottlenecks, analyzing log files, diagnosing errors, fixing them, monitoring results, and integrating with existing systems. And lots more.
The first step to successfully adopting MLOps is setting up a proper troubleshooting environment. This means that operations teams need to create a baseline of metrics that will be used as a reference for any upcoming changes before automating any operation. For example, by monitoring CPU usage, memory consumption, number of processes, and running scripts, teams can quickly identify the root cause when an issue occurs at any given time.
Once the environment is set up to measure its state at all times correctly, it's time to implement a mechanism that allows automatic feedback loops between performance metrics and specific business goals. This type of feedback loop creates an almost real-time system that enables continuous monitoring of performance metrics while tracking business goals against them. It also serves as an excellent tool for detecting anomalies in operations and building new automated workflows based on predefined rules.
Here are some of the benefits of adopting to MLOps for your financial institution.
- Allows financial institutions to develop a flexible, agile, and efficient infrastructure that can quickly scale up or down to meet spikes in demand. This allows business users to focus on business-critical tasks with minimal IT involvement. It also ensures that the traditional IT process does not get blocked or slowed down.
- Ease of sharing code and reproducing code with traceable version control by maintaining versions across a wide array of libraries or expanding modeling frameworks
- Automates the integration of AI/ML models into applications across all environments that your customers are digitally transacting
- Reduces cost of implementing AI/ML systems with self-managed environments with consistent code checks, version control, traceability, and app security requirements independent of continuous integration and continuous delivery (CI/CD) pipelines
- Automation of versioning, drift, and reproducibility of results at scale
- Allows banks and financial services to use their own data to train machine learning models. This eliminates the need for them to outsource their data to third-party vendors, who are often unwilling or incapable of providing enterprise-grade data at scale
Your financial institution with the right infrastructure in place can generate data, enrich it, push it out to downstream systems, and then analyze it. All this is to make informed decisions with a centralized command and control system.
What does the future of MLOps hold for financial services?
MLOps is a fast-growing machine learning software practice, and financial services companies have a head start. How you ask?
Well, the financial services industry already has a lot of data. Partly this is because of regulation. Partly this is because of the sheer number of transactions. And partly it is because humans like to spend money. Different divisions of a bank each work on different problems. While they compete for business, they also collaborate, sharing data and ideas.
That said, the future of AI and Machine Learning in banking is just getting started, and we will see more and more of these applications in the near future.
And for these projects to be successful, MLOps has a prominent role in FIs business transformation. Though MLOps has got wide acceptance in the financial services industry, it is likely to become pervasive over time.
But it won't be easy. Data science is a young field, and organizations often lack the infrastructure they need. But it's worth remembering that Hadoop wasn't easy, or Chime & Monzo wasn't easy either.
And like many enterprises, your FIs are going to grapple with the challenge of scaling and maturing data analytics and automation. But the potential payoffs are significant. Start early. Start today.
How to get started with MLOps?
MLOps focuses on unifying three critical areas: people, process, and technology. That said, MLOps provides a way to create repeatable and scalable machine learning practices. How?
First of all, start thinking about implementing a data department within your financial institution like any other department (Finance, marketing, sales, etc.) Meanwhile, get your data team accustomed to MLOps software practices to help your institution implement technical and organizational structures for machine learning. An in-house data department with an understanding of MLOps practices can help organizations identify the technologies that will work best for their financial institution, develop machine learning frameworks on the run, and build strong quality control practices.
Lastly, unifying data across "People, Process & Platform" will help you evaluate your organization's strengths, weaknesses, and opportunities in linking its Data, People, Process, and Platform to achieve any business goals. Real-time. Really fast.
MLOps is the future, and as machine learning becomes more mainstream, MLOps will become a necessity for businesses.
We're an outfit that works with MLOps, DataOps, and DevOps. And we understand that all IT professionals work better in a supportive environment that suits their needs. That's why we work hard to provide the support you need when and where, it makes the most sense.
Ready to become a data-driven financial institution? Book an MLOps discovery service with Zuci Systems today and get ahead of the competition. Make it simple & make it fast.