MLOps is a technique that makes use of automated model training techniques across the machine learning pipeline. MLOps is helpful in making the shift from manually running a few ML models to rapidly scaling ML models throughout the whole business process. In this post, we’ll go over some of the major ideas behind how MLOps may improve business workflow and increase revenue.
What are the Benefits of MLOps?
All activities in the ML lifecycle are more productive thanks to MLOps because:
Creation of Automated Model Training Pipelines
The ML lifecycle involves a lot of labour-intensive and repetitive procedures. For instance, about half of the time that the data scientists spent was preparing the data for the model. Manual data preparation and collecting are ineffective and might produce unsatisfactory results.
MLOps stands for automating the whole ML model workflow. This includes each step in the modelling process, including data gathering, model construction, testing, retraining, and deployment. MLOps procedures help teams save time and reduce human error. Teams may then focus on activities that bring greater value rather than doing the same thing over and over again.
Standardizing ML processes for effective teamwork
Collaboration between IT and business personnel, as well as data scientists and engineers, is necessary for the company-wide adoption of ML models. Businesses can standardise ML operations and establish a shared language for all stakeholders thanks to MLOps principles. This reduces compatibility problems and quickens the overall model generation and deployment process.
Automating ML workflows enables consistency and repeatability in a variety of processes, including the development, testing, and deployment of ML models. Because of this, continually trained models become dynamic and adapt to change:
- Data versioning: MLOps makes care to save snapshots of various versions of data sets as well as various versions of data that were produced or modified at particular points in time.
- Versioning the model with several hyperparameters and model types is a method of MLOps that involves establishing feature stores for various types of model characteristics.
MLOps improves the dependability of ML pipelines by introducing CI/CD concepts from DevOps into the machine learning workflows. Automatic ML lifecycle reduces human error while providing businesses with accurate data and insights.
Scaling an ML development project from a small model to a large production system is one of the toughest hurdles. For reliable scalability, MLOps simplifies model management procedures.
Models drift over time as the environment changes, therefore it is crucial to keep an eye on their behaviour and performance. Businesses may use MLOps to systematically evaluate model performance and get insights by:
- Constantly retraining the model ML models are kept under surveillance and automatically retrained on a regular basis or following a specific incident. Retraining a model is done to make sure it continually produces the most accurate results.
- Automated staff notifications in the event of model drift: MLOps provides the company with real-time data and model status updates and notifies the appropriate staff members when the model performance falls below a predetermined threshold. This makes it possible for you to intervene quickly to stop model deterioration.
Throughout the course of the full machine learning lifecycle, MLOps may drastically save costs:
- Automation reduces the need for manual management of machine learning models. Employee time will be freed up as a result, and it may be put to better use.
- It makes it possible for you to methodically identify and minimise mistakes. Reduced model management mistakes will also result in lower expenses.
How MLOps can Save Your Company Money
Machine learning operations (MLOps) are a critical component of modern businesses, enabling companies to leverage machine learning (ML) for critical decision-making and process automation. However, MLOps can also be resource-intensive and costly, with many businesses struggling to manage the costs associated with the technology. In this blog post, we will explore 5 ways that MLOps can save your company money while improving operational efficiency.
Automated ML Model Development
MLOps automates several tasks involved in ML model development, including data preprocessing, feature engineering, model training, and deployment, reducing the time and resources required for model development. Automated model training using automated machine learning (AutoML) solutions eliminates the need for expensive data scientists, enabling businesses to develop ML models faster and more efficiently.
Reduction in Manual Labor Costs
MLOps reduces the need for manual labour, reducing costs associated with hiring and training data scientists, and analysts. MLOps automates low-value and repetitive tasks, freeing staff to focus on high-value tasks, improving productivity and efficiency, and reducing labour costs.
Improved Model Accuracy and Reduced Errors
MLOps ensures model accuracy and reduces errors using automated testing and validation tools. By automating the testing process, businesses can identify errors and fix them quickly, reducing the risk of incorrect decisions and associated costs. Improved model accuracy can also reduce the cost of manual interventions required to correct errors in the model.
Enhanced Resource Utilization
MLOps optimizes resource allocation and utilization, ensuring efficient and effective use of resources. Businesses can save costs by using fewer infrastructure and computing resources, reducing the need for expensive hardware and software. By optimizing resource utilization, businesses can reduce operational costs and improve overall efficiency.
Improved Business Agility
MLOps improves business agility, enabling businesses to respond quickly to changing market conditions and emerging opportunities. Automated model development and deployment enable businesses to react faster and capitalize on market trends, reducing time to market, and increasing revenue. The ability to make data-driven decisions quickly enables businesses to maintain a competitive edge while saving costs associated with delays in decision-making.
In conclusion, MLOps can save your company money and improve operational efficiency. By automating tasks involved in ML model development, reducing manual labour costs, improving model accuracy, optimizing resource utilization, and enhancing business agility, businesses can reduce costs and gain a competitive edge. MLOps is an essential tool for businesses looking to leverage ML for critical decision-making and process automation while minimizing costs.
MLOps: A Guide For Your Enterprise AI Strategy
Payal is a Product Marketing Specialist at Subex, who covers Augmented Analytics. In her current role, she focuses on CIO challenges with data management, and potential solutions to these challenges. She is a postgraduate in management from Symbiosis Institute of Digital and Telecom Management, with analytics as her majors, and has prior engineering experience in the Telecom industry. She enjoys reading and authoring content at the intersection of analytics and technology.