With ML and AI being the buzzwords that they are, it is little wonder that many companies today have begun what they consider their “transformational journey.” However, for the most part, many businesses are yet to experience any significant transformation and are still in the experimentation stage. Even those who’ve already made the leap are finding that after the first flush of success, scaling their success across the organization is not as straightforward as they imagined.

According to a study by NewVantage Partners, only 15% of businesses have been able to widely implement AI into their business processes. This is concerning, as AI that cannot be scaled is merely a costly experiment or a complex technical achievement with little more than bragging rights. Only when it translates into ROI will it truly add value. It is in this context that MLOps plays a critical role. By combining the people, processes, practices, and technologies to automate deployment, monitoring, and management of ML models into production, MLOps provides a technological backbone for managing the ML lifecycle in a fully scalable and governed manner. Laying an MLOps foundation allows various teams–data, development, and production–to work collaboratively, leveraging automation initiatives and achieving organizational goals with AI.


Beyond DevOps

Dubbed the “data-driven cousin of DevOps,” MLOps is essentially DevOps that has been tailored for data science and machine learning. Like DevOps, MLOps integrates design, development, testing, and delivery into a single process in order to improve the delivery of machine learning models. By implementing MLOps, your team will be able to release updated ML capabilities more quickly and confidently.

MLOps borrows a lot from DevOps, but there is a major difference between operationalizing machine learning and traditional software: data. In contrast to software behavior, ML model behavior is the result of a combination of data and code.

By increasing the collaboration and communication between data scientists and operations professionals, MLOps allows you to automate the deployment of machine learning and deep learning models in large-scale production environments and to align models with business needs and regulatory requirements. As a result, MLOps is slowly evolving into an independent approach to ML lifecycle management.


Why Invest In MLops?

Improve Productivity: In ML development, the development team works closely with the data science and operations teams. If the ML software gets delayed at any stage, every team suffers. MLOps helps you avoid these bottlenecks by versioning, documenting, and sharing all your work. As a result, your development team can see and understand the project’s progress. By guiding your team to split work effectively and automating manual work, MLOps shifts the thinking from model-centric to pipeline-centric. When the pipeline is the final product, teams can easily split their work and develop different parts of the pipe independently.

Enable Easier Versioning: With the average Silicon Valley employee’s tenure at a company being only a few years, there is a significant risk of losing work when teammates switch jobs. Managing version control mitigates that risk, and with the right tooling, it’s mostly automatic.

Since humans do not explicitly define machine learning logic, machine learning and AI are subject to tight regulations and ethical scrutiny. In order to validate the predictions served by the model, it is critical to explain how the model was trained. Both the data and the code should be versioned. When your development team changes, version control for data, code, and trained models is essential.

Contribute To Profitability: MLOps best practices can help solve many of the challenges associated with AI production. It increases profitability by:

  • Decreasing time to market through shorter development cycles
  • Enhancing collaboration between teams for greater productivity
  • Enhancing the performance, reliability, scalability, and security
  • Optimizing governance processes and operations
  • Reducing the cost of ML projects and maximizing their ROI

Increase Deployment Speed: MLOps is a powerful tool for deploying and adapting ML models faster, allowing businesses to capitalize on the insights the models provide with less lead time. By leveraging DevOps principles, it speeds up the development, testing, and release of software systems. Additionally, MLOps allows you to track the data sets that are used to train and re-train models and keeps track of drifts in model accuracy.

Machine Learning concepts integrated with DevOps principles can increase the responsiveness, reliability, and speed of ML lifecycles. Therefore, data scientists can work swiftly and collaboratively with data engineers and business leaders to adapt and deploy models that drive business results.

Enhance Accuracy and Adoption Rate: If a model isn’t trusted, it won’t be used by the business and therefore will not drive value. Effective MLOps frameworks feature enhanced auditability, monitoring, and responsiveness to change. Moreover, it will provide an automated monitoring methodology to ensure that the models’ quality and performance do not degrade over time.

Data scientists and business users trust model outputs when MLOps help maintain accuracy. With proper governance and care for your machine learning models, organizations can foster greater trust and foster a culture that embraces data science and machine learning.

Increase Efficiency Through Parallelization: The cloud is often cited as a cheaper alternative to on-premise options, but analytics solutions, in particular, can become resource-intensive if they are not designed purposefully. Data scientists may create highly accurate forecasting models for specific business functions and markets. As data and markets grow, the model may need to be scaled.

The MLOps team can be invaluable in providing knowledge around parallelization options and refactoring code to ensure it runs as efficiently as possible even when more data and markets are added. Parallelization will result in lower end-to-end runtimes, lower compute cluster costs, and, in general, more efficient and sustainable utilization of computing resources.

Boost The Impact Of Your Data Scientists: In a world in which data scientists are in short supply, organizations should magnify the impact of these professionals. More often than not, the data scientist who designed a model is also responsible for overseeing updates, maintenance, and monitoring. As more models are constructed, the hours spent on support and monitoring can become a considerable overhead and limit new analyses. By automating these procedures, data scientists can spend time innovating and resolving other business concerns.

By setting out frameworks and architectures for enterprise-scale machine learning, businesses can enable their data scientists to conduct more meaningful analyses by employing tools that allow greater scale, quicker processing, and broader functionality. Data engineers can re-engineer models with high-run durations to take advantage of enterprise architectures that enable parallelization, resulting in significantly reduced processing times for models with long-run durations. Technological breakthroughs can be fully realized when enterprises combine data science with data engineering.


Wrapping Up: So Why Does MLops Matter?

The MLOps process is fundamental. In addition to creating more efficient workflows, leveraging data analytics for decision-making, and improving customer experience, machine learning helps individuals and businesses unlock previously untapped sources of revenue, save time, and reduce costs. If there is no framework to follow, these goals will be difficult to achieve. Using MLOps to automate model development and deployment will result in faster time-to-market and lower operational costs. By using it, managers and developers can make more strategic and agile decisions—a necessity in an age where everyone creates at least 1.7 MB of data per second (as of 2020).

No matter how restricted the resources are, how limited the budget is, or how sensitive the data is, MLOps can serve as the guide for individuals, small teams, or even businesses to achieve their goals. MLOps practices are not set in stone, so you can always alter them based on your requirements. It is up to you to experiment with different settings and keep only those that work for you.

To find out how MLOps can transform your business, speak to our experts!



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