1. Whitepaper MLOps: What it is, how it helps, and how to implement it

2. Stay ahead with MLOp Executive summary Technology might not be the panacea for all business problems. But, it is certainly emerging as a strategic differentiator.  The most innovative and advanced businesses, from streaming services to traditional manufacturing plants, are increasingly embedding and optimizing technology across every aspect of their operations and collaborations.  Machine Learning (ML), a powerful subset of Artificial Intelligence (AI), is rapidly engulfing the imagination and everyday conversations of most C-level executives as the strategic implementation of AI and ML capabilities can transform how businesses are run and results are delivered.  Unfortunately, despite the profound results that AI and ML technology promise, embracing the change can be scary. According to McKinsey, only 15% of businesses’ ML projects succeed.  Another study by Gartner found only 53% of AI projects ever make it from prototype to production. There’s certainly some room for improvement. Apart from building AI/ML solutions for problems that are not just ‘nice to solve’ but ‘need to solve,’ what can likely improve the odds of success in any AI/ML project is how comfortable the organization is around the approach called ‘MLOps.’  MLOps is the unification of Machine Learning and Operations within an organization and calls for a profound thrust on collaboration and automation across the entire deployment and post- deployment processes. We will cover every important aspect of MLOps in this whitepaper to help decision-makers truly understand the foundational elements of this approach and whether they need it within their organizations.

3. Stay ahead with MLOp Table of Contents ?? Introduction to MLOps 04 ?? MLOps - It generates massive value for new-age businesses 05 ?? What stops most organizations from embarking on an MLOps journey? 06 ?? 5 essential MLOps best practices 07 ?? Impact of MLOps on modern businesses and industries 08 ?? How to ensure MLOps success? 09

4. Stay ahead with MLOp Introduction to MLOps What is MLOps? MLOps stands for Machine Learning Operations. It is a set of ever-evolving practices that aims to bring all responsible teams on one common platform with the objective of achieving reliable and rapid development and deployment of Machine Learning models into production.  Almost every business and every industry can benefit by embedding the practices and principles of MLOps in their ML projects. Successful implementation of MLOps can help businesses and teams realize multi-dimensional benefits, including? ? More automated and simplified work processe? ? Accelerated Machine Learning deployments? ? More reliable Software Development The MLOps market is projected to grow to nearly US$4 billion by 2025. Businesses in the UK alone are likely to invest more in further building on their MLOps capabilities by an average of 26%. Source: Deloitte The evolution of MLOps: Machine Learning Operations, a term that signifies the unification of Machine Learning and DevOps, has an interesting origin and, over the years, made its way to where it stands now as an independent approach to streamlining Machine Learning deployments and maintenance and management? ? Ad-hoc MLOps: These are the early days of MLOps when a few organizations thought about it and conducted ad-hoc Machine Learning experiments. Often, these projects would run in silos and would not have any significant impact on an organization’s operational processes? ? Experimental ML: Organizations began to see the transformative potential of employing MLOps in their core workflows. They incorporated the practices of Version Control and Team collaboration within their machine-learning projects. However, the leadership and Data teams were still not confident enough to bring it to their work.? ? ML Engineering: This is the stage where MLOps became more widespread, and thus, teams across businesses and industries began to harness its potential to transform their processes. Greater emphasis was placed on automation, CI/CD (Continuous Integration/Delivery), and Containerization? ? MLOps: It is the rise of collaboration and automation in the field of Machine Learning and Artificial Intelligence. MLOps is the advanced stage in which multi-functional teams join hands and introduce new waves of changes to the entire development and deployment pipelines. Using MLOps, Data teams ensure the data is safe and secure and that the deployed models are producing the desired outcomes. The MLOps market is expected to expand to nearly US$4 billion by 2025. In the UK alone, 71% of adopters expect to increase their investment in the next fiscal year by an average of 26%. Source: Deloitte

5. Stay ahead with MLOp MLOps - It generates massive value for new-age businesses Businesses need MLOps to dramatically increase the pace of ML deployments. However, it is not only about increasing the deployment speed, it is also about refining the entire design, development, deployment, and management of AI/ML models in production. Teams applying Machine Learning Operations practices and principles to their ML pipelines experience the following benefits? ? Increased efficiency: Team efficiency goes up as the focus is now on end-to-end process automation. Increased team efficiency then leads to faster development, deployment, and time-to-value. ML models are more robust and scalable than ever before. Team, especially decision-makers across the org, now feel more confident while launching new initiatives? ? Increased scalability and model management: Another important benefit of embedding MLOps philosophy within the ML lifecycle is the increased scalability and ML model management capabilities. Manual interventions give way to intelligent automation, and suddenly ML teams are in a position to build, deploy, and manage thousands of ML models. It becomes more intuitive and simpler to monitor and manage ML model performance and take corrective measures without losing any time. MLOps practices also infuse a culture of strong collaboration across data teams and ensure minimal to no friction with DevOps and IT teams.? ? Risk management: Machine Learning models are all about capabilities that are built using troves of customer and business data. These practices thus need frequent regulatory scrutiny and governance. MLOps tools and technologies enable increased transparency and robust governance and thus help organizations effectively respond to such requests. With MLOps, organizations can supercharge innovation, achieve faster time-to-market, and maintain model quality, all while reducing operational overhead. It's the game-changer that empowers businesses to thrive in the age of AI. “According to McKinsey & Company, MLOps practices enable teams to shelve almost 30 percent fewer models and gain increased value from their AI work - as much as 60 percent.” While the advantages of MLOps are compelling, it's important to recognize that successful implementation is not without its hurdles.  Let's delve into some key challenges and bottlenecks organizations may encounter when striving to implement MLOps effectively.

6. Stay ahead with MLOp What stops most organizations from embarking on an MLOps journey? The practices of MLOps are transformative in nature. By implementing MLOps tools and technologies deep within your teams and processes, companies can streamline and automate their traditional AI/ML projects, especially how AI/ML models are created, deployed, and managed. However, nothing of value comes easy. This is true in the case of MLOps as well. Here are some of the most common bottlenecks that seem to impede the wide adoption of MLOps principles and practices: ?? Managing Data at scale ?? Rethinking model deployment 5 major challenges in MLOps implementation ?? Cultural and organizational barriers ?? Compliance with Data protection laws and regulations ?? The need for continuous monitoring 1. Managing Data at scale: One of the challenges MLOps teams can face while implementing their practices and solutions is ‘handling the massive amounts of data they produce and consume’. In order to put data to work, data teams need to redesign the entire data landscape and build agile and scalable data pipelines. ML models can function properly and yield desired results only when the data is of superior quality. Data integration is another area where data teams must work together so as to ensure all the data trapped across teams and systems is pulled together to generate true insights at scale.   2. Rethinking model deployment: Traditional data and ML teams know it all - the confusion and the struggle in the deployment process. Often, deploying models at scale requires hand-offs and consistent communication with IT and operations teams. Deploying models into production also means working with different technologies and infrastructure and ensuring nothing goes wrong. Team miscommunication, the need for system and app integration, and the complexities of traditional deployment processes must be addressed effectively to pave the way for a new mode of working and ML model deployment. 3. Cultural and organizational barriers: Embracing the concept of Machine Learning Operations requires a radical shift in how teams connect, communicate, and build products. To succeed in the MLOps program, leaders must first build a conducive culture - one where Data Science and IT teams trust each other and build solutions that serve the larger business goals. Resistance to change and differing team priorities can thus pose serious challenges to seamlessly adopting MLOps practices within an organization. 4. Compliance with Data protection laws and regulations: Training ML models require massive amounts of data. Often, this data needs to be protected and utilized as per the data protection rules and regulations such as GDPR and CCPA. Organizations planning to build robust ML pipelines are required to set up a robust Data Management ecosystem in order to comply with all the industry and regulatory requirements surrounding Data protection and storage. This increased pressure on data usage and transmission acts as one more hurdle in the widespread adoption of popular MLOps practices and principles. 5. The need for continuous monitoring: MLOps does not end with deployment. It continues even after that and encompasses the stage of model performance monitoring. Visibility into AI/ML model performance helps teams keep track of any model drift and the need to retain the models to ensure they are producing the most accurate and faster predictions. Continuous monitoring requires more manual efforts and resource allocation and thus can discourage new teams from applying MLOps in their workflows.

7. Stay ahead with MLOp 5 essential MLOps best practices The MLOps journey will look and feel different for different organizations, depending on a series of factors such as in-house talent, whether they engage a reliable MLOps partner, and how agile and collaborative their teams are. Similarly, challenges will vary for these organizations. However, most organizations can make the transition way more smooth and rewarding by embracing a few industry best practices. 03 02 Choice of ML platforms 04 Adopt a standard naming convention Keep costs under control 01 05 MLOps requires a collaborative team culture Effective performance monitoring MLOps best practices ? MLOps requires a collaborative team culture: A collaborative cross-functional team is a fundamental necessity to capture the true value of Machine Learning Operations. When teams work in silos, organizations suffer as there is an increased likelihood of people and process inefficiencies and unreliable deployments, and model performance. MLOps success thus requires leadership and departmental heads to unify the cross-functional teams on one common platform to benefit from increased communication and collaboration? ? Adopt a standard naming convention: Adhering to a naming convention for your ML project is a great way to eliminate any possible confusion within the team regarding the roles of different variables. Establishing a naming convention and sticking to it will help the team as the Machine Learning systems grow within the organization and new members join in to work on the project? ? Choice of ML platforms: The choice of ML tech stack can make or break an AI or ML project. Therefore, MLOps enthusiasts must invest time and effort into exploring the ever-expanding world of ML platforms. Department heads and teams should ideally reflect on the 1) Outcomes they desire after the successful deployment of the MLOps, 2) The expertise and resources available within the organization, 3) Budget and business objectives, and 4) The volume and format of data they will be dealing with? ? Keep costs under control: Building an MLOps stack and/or hiring an MLOps partner requires significant investments. The company must ensure that the expected benefits far outweigh the costs and efforts involved. A clear strategy and the selection of the right tools and processes can help keep the associated costs under control? ? Effective performance monitoring: MLOps streamlines deployment and helps develop ML models that can predict more accurate outcomes to drive business decisions. However, ML models need clean and consistent data to make predictions that enable decision-makers to make informed decisions. Effective application monitoring ensures that the data that is fed into the models is neat and clean and aligns with the data that was initially used to train the models. This reduces the chances of data drift and that models are delivering results that help meet the business goals.

8. Stay ahead with MLOp Impact of MLOps on modern businesses and industries Banking and Financial Services (BFSI) Banks and financial institutions are arguably one of the most heavily regulated industries in the world. They deal with money. And they deal with customer data. So they can’t help but abide by the laws and regulations. However, with more and more stringent regulations, such as GDPR, banks and financial services companies are finding themselves in a stream of challenges. Fortunately, MLOps can help a great deal. By ensuring robust Data Pipelines and Data Management and secure and accelerated ML model deployment to production, the industry players can effectively comply with the relevant laws and regulations. Manufacturing Deloitte confirms that leaders across the manufacturing sector are increasingly investing in the adoption of new and emerging tools and technologies. One of the major challenges that manufacturers are increasingly struggling with is the constant machine failures that cause serious repercussions for the companies, including extensive downtime and reduced employee productivity. However, by embedding MLOps practices within the processes, manufacturing players can gain a competitive advantage by enabling accurate prediction and prevention of machine breakdowns. ML models are trained to analyze troves of data and identify patterns that can help teams on the floor keep track of the health of the machines while reducing breakdowns and employee accidents. Healthcare Modern healthcare thrives on patient data. They already use many sophisticated tools and technologies to improve diagnostic and patient care. However, to thrive on patient data, they must build and deploy highly accurate and reliable Data models. That’s where MLOps can help. As it streamlines the development and deployment of ML models into production and ensures its effective monitoring for performance, it can help healthcare companies build robust and secure ML models that can help them predict a disease (s) or complications a patient can sometimes experience in the future and customize treatment accordingly. MLOps can dramatically improve the reliability and performance of ML models and help the industry leverage technology to deliver world-class medical services while reducing medical costs.

9. Stay ahead with MLOp How to ensure MLOps success? MLOps 6 steps for ensuring MLOPs success 01 03 05 Communicate the changes Build a tech stack Embed continuous integration and continuous delivery (CI/CD 02 04 06 Build more collaboration within teams Build well-organized pipelines Monitor model performance and continuous improvement ?? Communicate the changes Change is scary. So do not surprise ‘teams’ with sudden introduction and implementation. Communicate the need for new tools and technologies. Communicate the advantages. Communicate how the change will benefit them personally and professionally. Train them beforehand. So when new tools and practices are introduced, teams can adopt them seamlessly. It is a good practice to get the shareholder buy-in so the required changes can be implemented faster and more effectively. ?? Build more collaboration within teams Collaboration is a key cornerstone of MLOp's success. If the teams are not aligned with each other, there is little to no chance of improvement in ML model deployment. The very purpose of MLOps will fail in such a scenario. So, driving a collaborative work culture is a key step for teams planning for MLOps. ?? Build a tech stack Fortunately, the MLOps landscape has grown exponentially in recent years. So, MLOps enthusiasts are likely to find tons of resources, tools, and technologies to help them build their MLOps capabilities. However, teams should choose their MLOps tech very strategically. All tools and platforms are not equal and vary in terms of their scalability and affordability. Therefore, the responsible teams must select and build their tech stack with their own strengths, limitations, and budgets in mind. ?? Build well-organized pipelines It is through Data Pipelines (or the technologies and tools that form Data pipelines) that organizations move data and transform it to inform business decisions. Agile Data Pipelines form foundations for successful MLOps. To increase their odds of success, MLOps teams must build on their existing Data Pipelines. Well-organized Data Pipelines ensure that there are no Data silos and that the final output is reliable and consistent. ?? Embed continuous integration and continuous delivery(CI/CD) CI/CD approaches have proved their worth in the DevOps domain. These same practices can be applied to accelerate and streamline the MLOps processes and develop more reliable ML models at the speed of business. ?? Monitor model performance and continuous improvement Monitoring is essential to successful MLOps operations. With effective model monitoring, teams can keep track of model performance and take corrective measures when necessary. With real-time monitoring and feedback (based on performance) implementation, teams can ensure that the models are delivering accurate results. The six steps outlined above provide a high-level overview of how organizations embark on their MLOps journeys.

10. Stay ahead with MLOp Move ahead with Kellton We are Kellton. We have been helping businesses uncover new opportunities with technology for years. Whether it’s your first attempt at MLOps or you already have a seasoned team of experts and need an ally to help you accelerate the MLOps progress, Kellton can help.  We have helped a number of companies - both small and big ones - embark on their MLOps journeys with more confidence and clarity. And we’d love to help you, too! Whether it is about exploring Data domains, developing and deploying AI/ML applications, designing scalable MLOps architecture, or building the right kind of MLOps tech stack, connect with Team Kellton. Europe: +44.203.807.6911 North America: +1.844.469.8900 Asia: +91.124.469.8900 General Inquiries: ask@kellton.com www.kellton.com