Machine Learning is no longer a differentiator for an organization, but a necessity for them to increase productivity & an enabler to achieve business goals faster and better. MLOps enables organizations to streamline the end-to-end machine learning lifecycle, including data preparation, model training, deployment, and monitoring. One of the reasons why companies adopt MLOps is to reduce time-to-market for new products & services. It also gives them a way to iterate & improves the models faster, leading to better business outcomes. MLOps also allows organizations to leverage the right tools & frameworks to manage large volumes of data and complex pipelines while distributing workloads across compute resources.
MLops, thus, is a backbone for any efficient ML adoption roadmap. But while designing an MLOps adoption strategy, it’s critical to define clear goals, depending on the larger organizational goals & C-level strategy.
While the actual goals might differ by business, some of the most viable goals for MLOps adoption are
- Model performance improvement: While building the right model for a business problem is the goal that gives the right output to solve the business problem, one of the organization’s significant concerns is the optimum performance of such models in production. MLOps aims to streamline and automate the process of deploying machine learning models into production and ensure their optimal performance with production-level data. These are secured by automating model training, testing, deployment, and monitoring, resulting in faster and more efficient model deployment cycles.
- Faster turnaround time for outcomes: MLOps implemented correctly will help organizations reduce their release cycle times, making their products/services market faster. It is achieved by streamlining the complete ML lifecycle, automating these processes & reducing the time to develop and deploy machine learning models.
- Achieving scale for ML adoption: Organizations today struggle with ever-growing data, delivering real-time predictions & handling an increased user base. MLOps implement practices, such as version control, containerization, and infrastructure orchestration, to help manage models across different environments consistently and reliably, helping businesses develop scale & deliver predictions in real-time for their increasing user base.
- Better Governance practices & improved collaboration: Implementing ML-based use cases within an organization requires collaboration across multiple stakeholders, each with different goals & priorities. A robust MLOps implementation helps set up smooth workflows, shared best practices, and effective communication channels, promoting collaboration and cross-functional communication among data scientists, developers, operations teams, and other stakeholders. In addition, it introduces governance practices, such as model explainability, fairness, and privacy considerations.
- Reduce ML adoption cost: MLOps automates resource provisioning, dynamic scaling and workload scheduling, optimizing resource utilization. It also continuously monitors model performance, reducing inefficiencies to optimize resource allocation and avoid unnecessary expenses.
- Reduced Operational Risks: MLOps implements standardized processes, version control, and reproducibility techniques, reducing operational risks associated with model development and deployment, ensuring consistency in model outputs, reducing errors, and increasing the robustness & reliability of ML systems.
- Continuous Improvement: MLOps emphasizes a culture of constant improvement and iteration. Businesses can iterate on their models by gathering feedback, analyzing performance metrics, leveraging techniques such as A/B testing and making data-driven improvements to deliver better outcomes over time.
At goML, we help businesses scale their MLOps efforts by 10X with our Speed & Efficiency enablers. Set up a 30-minute call to understand how we could help scale your MLOps journey. https://calendly.com/d/z7d-sqp-c3x/30min?month=2023-06
