MLOps is a new trending topic these days due to its benefits and useful features. Organizations using AI/ML-powered apps are extensively using MLOps to increase their business leads and optimize their operations. Let’s explore how MLOps helps modern businesses to achieve their business goals.
What is MLOps?
Machine Learning Operations (MLOps) refers to the collection of practices that help in deploying and maintaining ML models effectively and reliably in production. MLOps enables development/operations (DevOps) teams to automate & monitor the complete machine learning lifecycle and collaborate with other teams, which helps in achieving faster and reproducible results.
MLOps mainly deals with the operationalization of ML models. With the help of the end-to-end machine learning process, MLOps has made it possible to design, create, and maintain testable, reproducible, and evolving ML-powered applications or software.
Need for Using MLOps
Modern business applications have Artificial Intelligence (AI) and Machine Learning (ML) as their integral part due to the benefits they bring to the business. Organizations often fail to deliver AI-based applications that can perform well in real time because of varying business requirements, feature engineering logic, and data-science models.
The process of developing data science and ML models is considered an essential part of modern application development. This makes organizations concentrate more on developing ML models, overlooking crucial challenges like data preparation in production, aligning the ML models with online business applications, monitoring the performance of the models, and providing continuous improvements.
The data science team is often detached from the DevOps and engineering teams. The data-science team uses manual processes for developing ML models which must be again converted manually into ML pipelines that are ready for the production environment. All these processes require multiple smaller teams of data scientists, ML engineers, developers, and DevOps to invest more resources and time than they anticipated earlier.
While developing ML models, the data science logic is usually refactored into production-based frameworks or code. The data science and engineering team must then package the code, scale it out, tune it for performance, instrument it, and automate it. Since these tasks are performed manually, they typically can even take months. Moreover, the complete cycle should be repeated whenever there is a model training logic or data preparation change. MLOps allows IT teams (Infra and Software engineers) and data science teams (ML engineers and data engineers) to collaborate and perform the development, deployment, scaling, monitoring, and retraining of ML models at a faster scale.
Code or model development is only an initial step in the development flow. However, consistent efforts must be put into making every element of this development lifecycle production-ready as well as enabling the elements to run repeatedly without or less human intervention. The elements of the development lifecycle include collecting, preparing, training, delivering, and monitoring data. MLOps helps organizations to take care of the above-mentioned areas and enables them to optimize workflows and prevent implementation issues.
MLOps Allows Continuous Delivery of AI Apps
Various challenges related to the ML development lifecycle, including the ones mentioned above can be addressed effectively using MLOps. MLOps unifies the DevOps practices with AI/ML practices for continuous integration/continuous deployment (CI/CD) of data and ML-based apps. It assists in creating an automated ML production environment where everything (from data collection and preparation to the deployment and monitoring of ML models) is automated in a disciplined way.
Benefits of MLOps
Some benefits offered by MLOps are listed below:
- Helps teams to increase their productivity and reduce silos.
- Enables IT teams and data science teams to collaborate for delivering business value faster.
- Offers reproducible and reliable results.
What is ML Works?
ML Works refers to an end-to-end ML model management solution that serves as an accelerator for MLOps. It takes care of all the functions of ML model development lifecycle, including ML model generation, orchestration, deployment, governance, and business metric tracking. This solution ensures model transparency, source review, and explainability by enabling white-box deployment and monitoring.
With modern-day challenges, organizations need to use AI/ML models to drive their business decisions. As business requirements never stay the same always, organizations must ensure that the ML models they use to drive their business decisions are relevant all the time. To keep them relevant to the present day’s business needs, organizations must monitor these models in real time.
Failing to monitor the models in real-time may make them outdated, making them unable to deliver business values. If ML models fail to work as expected in production, organizations require their data scientists to fix and redeploy them. This will not only waste their valuable time and efforts but also affect their routine operations and may make them fail to comply with regulatory requirements.
Features Offered by ML Works
Organizations can use ML Works to address modern-day challenges as well as to improve their MLOps with the help of the following features:
- MLOps Graph: Get end-to-end visibility and pipeline traceability with its Visual Provenance graph that allows root cause analysis and easy troubleshooting of production issues.
- Persona-Based Dashboards: Use persona-based dashboards to check relevant metrics for various personas such as Data Engineers, ML Engineers, Data Scientists, and Business users.
- Model and Data Drift Analysis: With auto-triggered alerts, analyze data and model drift and continuously monitor production models for their relevancy and accuracy.
- Lineage Tracking: Get complete visibility into model operations and track issues from dashboard metrics to the base models.
- Governance and Support: Manage several user personas and comply with regulatory requirements across various platforms using centralized access control, audit logs, and traceability. Also, get uptime governance and maintenance via an SLA-driven response and resolution process.
- Platform-Agnostic Advisory and Development: Take care of numerous business requirements with the platform-agnostic and open-source setup that supports countless models.
Industries that Find MLOps Useful
Banking
AI/ML applications are widely being used in the banking sector to provide bank users with enhanced user experience, drive revenue, improve risk management efficiency and effectiveness, and reduce operational costs. With ML, it is easy to analyze large datasets to make investment decisions for defined goals using powerful algorithms without human intervention.
Use AI/ML in the Banking Sector for
- Customized and enhanced customer experience
- Easy escalation management
- Improved data security and fraud detection
Fintech
AI/ML finds its use in multiple aspects of Financial Technology (Fintech) organizations, from payments and investments to lending and managing personal finance. Modern Fintech organizations prefer using AI/ML to increase their revenue, attract more customers (by offering them a better user experience), manage risk, and improve efficiency.
Use AI/ML in the Fintech Sector for
- Process optimization
- Fraud detection
- Credit risk forecast and credit scoring
Consumer Packaged Goods
AI/ML helps organizations producing consumer packaged goods to retain their wallet share and competitiveness and stay relevant in the market. By using AI/ML, these organizations can better connect with their customers, improve their product range to deliver products anywhere the customers want, and improve operational efficiency. ML enables these organizations to understand customers better by utilizing the customers’ data available to them and offering them what they are interested in via ML algorithms.
Use AI/ML in the Consumer Products Sector for
- Improving customer engagement
- Predicting sales and the time of the next purchase
- Reducing out-of-stock or over-production scenarios
Capital Markets
AI/ML is highly relevant in this tough time when financial institutions are in an uncertain situation due to financial crisis and market volatility, especially due to Covid-19. Financial organizations can leverage AI/ML to study market trends, make predictions, and deliver their services as per demand.
Use AI/ML in Capital Markets for
- Reducing operational costs and calculation speed
- Real-time risk management
- Market-consistent data generation
Marketing
AI/ML is most widely used in the marketing industry to analyze customer data and generate valuable insights. Marketing firms can use ML-based applications to convert digital advertising and customer data into predictions that can improve sales, improve customer satisfaction, and help make effective marketing programs. Since traditional methods of attracting and retaining customers are no longer useful, marketing firms are increasingly opting for AI/ML to make their businesses grow.
Use AI/ML in the Marketing Sector for
- Optimized advertising activities
- Customer engagement to retain market share
- Identifying drivers for customer satisfaction
Manufacturing
AI/ML is equally useful for manufacturing firms that aim at increasing their customer base and making customers happy. Manufacturing companies can leverage ML algorithms to understand the current market trends and discover customer insights, allowing them to align their products and services as per customer expectations.
Use AI/ML in the Manufacturing Sector for
- Preventive maintenance in machines
- Improved business productivity
- Better market insights and customer relationship
Wrapping Up
Companies across different verticals now use AI/ML-based apps to stay ahead in business competition. MLOps surely helps them achieve this goal by helping them know more about their customers and make better business decisions. With the rise of cloud-based software development, MLOps can become more relevant than ever. Know more about cloud computing in our blog.