Machine learning typically delves into building models and relevant statistical concepts. Until recently, it made sense as several organizations, in their exploratory phase, have been acquiring data while being unsure about what they can do with it. Recruiting ‘data scientists’ became a common practice to acquire the data, assess it, create charts, and extract insights. However, a new field called machine learning operations (MLOps) is steadily gaining traction and reshaping the way of modern businesses.
The time has come for bringing together issues related to model deployment in a methodical way and educate the new generation of professionals in data science. The emerging discipline concerned with technologies and tools is necessary for supporting the progression of MLOps, from ideation to development. One of the most excellent frameworks for machine learning operations is thinking through the complete lifecycle of machine learning projects.
Why is MLOps Important for Businesses?
Businesses are deriving significant outcomes by implementing machine learning operations. Most organizations are putting efforts to incorporate technologies like artificial intelligence (AI) and machine learning (ML) in their products & services. Newer developments are on the rise that ensures improved results, provided with increased utilization of disruptive technologies.
MLOps is nothing but a discipline of delivering artificial intelligence models, including capabilities of IT operations, development, and data science. This increasingly secures ML and other touted models in production environments. Combining DevOps principles with practices of AI/ML, MLOps represents the machine learning lifecycle vis-à-vis the software development lifecycle. Moreover, it promotes a well-structured, collective view of metrics, logs, and parameters used, apart from workflows.
Benefits of Machine Learning Operations
Removing Communication Silos
One of the most common issues among organizations is communication silos between business and technical teams. A common language is essential for collaborating these forces together and achieving seamless operations between the two. MLOps, while ensuring efficient, timely deliveries, bridges the communication gaps effectively.
Machine learning operations help organizations in building capabilities for systematic management of computation resources across myriad models in place. This empowers them to ensure cost-effective operations while meeting their business goals. To add to the convenience, MLOps is compatible with the cloud, on-premises, and hybrid environments.
Utilizing role-based access controls, MLOps is powerful enough to maintain the security & compliance of organizations. It enables security across various platforms for resources, models, data, and users for ensuring measurable outcomes.
An important aspect to consider in running a business is to assess the risks and failure costs. MLOps intercepts the damages caused in terms of finance as well as prevents potential losses that might occur in the future.
Reducing time, removing hurdles in operations, and providing clear roles to users are key USPs of MLOps, which it does by integrating tooling systems and business workflows. Also, it provides the users with continued accessibility for monitoring and notifying on running projects, in turn enabling insightful decision making.
MLOps vs. AIOps
Algorithmic IT Operations or Artificial Intelligence for IT Operations (AIOps) is an industry category of analytics technology for machine learning that improves IT operations analytics. It is completely different from MLOps, although both share the capability to make systems more efficient. These two fields intercept but do not convey the same idea, neither based on reason nor the approach.
The primary objective of machine learning operations is to bridge the gap between operations teams and data scientists. Therefore, between the model building of ML and its execution, algorithmic IT operations focus on the automation of root cause analysis and incident management.
Key best practices of MLOps include
- Maintaining perpetual processing functions that span across serving pipelines.
- Coming up with a reproducible workflow for data training and preparation.
- Establishing transparent naming conventions.
- Building for scale from the start.
- Achieving a more centralized tracking system.
- Building clear business objectives.
- Getting the infrastructure done right and creating a simplified model, to begin with.
- Opening the communication lines almost instantaneously.
To Sum Up
As machine learning technology matures, starting from research to solutions of applied business analytics, organizations need to increase the operation processes’ maturity. They must focus on extending several practices from key disciplines that emerged before ML did. With new ML disciplines coming to the fore, trailed by fast-evolving tools and best, immense opportunities exist for developing and implementing production techniques to machine learning.