Years ago, DevOps revolutionized software development by integrating development and operations, and now, MLOps and AI are transforming the AI landscape in the same way.
MLOps, or Machine Learning Operations, is a set of best practices for deploying and maintaining machine learning models in production for maximum reliability and efficiency. As AI technologies such as generative AI continue to evolve, more use cases emerge. The need to manage and scale AI with MLOps is increasingly critical.
Enterprises across various industries leverage AI to automate tasks, derive insights from vast datasets, and create innovative solutions that enhance customer experiences. Integrating AI into business processes drives efficiency, reduces costs, and opens new avenues for growth. However, managing and scaling these solutions presents significant challenges as AI applications become more complex and widespread.
MLOps makes working with AI easier. By building AI with MLOps practices, businesses can streamline AI models’ deployment, monitoring, and management, ensuring they remain robust, scalable, and aligned with organizational goals.
Scaling AI with MLOps optimization
As AI continues to evolve, the need for efficiently managing and scaling AI with MLOps becomes increasingly critical. To understand the value of streamlining AI with MLOps, it helps to know its predecessor: DevOps.
DevOps, or Development Operations, is a set of tools and practices that emerged circa 2007 in response to the inefficiencies of scaling the traditional software development model. Software development and IT operations worked in silos, and this new approach aimed to bridge the gap and build scalable processes.
It worked. DevOps transformed the software development lifecycle (SDLC). The DevOps principles of communication, collaboration, and continuous improvement/continuous development (CI/CD) have become the standard methodology in software development.
In recent years, other operations practices have emerged in attempts to address silos in different areas across the organization. For example, DataOps aims to improve the data lifecycle, and RevOps seeks to improve the sales development process.
If MLOps sounds like a buzzword, that’s why. However, machine learning operations are far from a fleeting trend; they are a vital evolutionary step, just like DevOps. MLOps processes apply DevOps principles to Machine Learning (ML) to improve cross-team collaboration while automating and streamlining processes across the AI lifecycle. Standardizing the development of AI with MLOps optimization essentially puts your AI development program on autopilot.
Note that while the names sound similar, there’s a big difference between AI and MLOps. Ops applies AI techniques, such as deep learning and predictive analysis, to improve the efficiency of IT. Organizations use AIOps to automate processes, enhance incident management, and enable predictive maintenance for IT systems.
To remember the difference, remember the different roles of AI vs MLOps:
- MLOps is a framework for applying DevOps principles to orchestrate the AI product development lifecycle, improving decision-making and cross-team collaboration
- AIOps uses AI products to analyze massive amounts of data from IT systems to streamline, automate, and enhance IT processes
What it looks like to transform AI through MLOps strategies
Without MLOps, organizations often face significant hurdles that impede their ability to fully leverage AI technologies. By transforming AI through MLOps strategies, organizations can overcome these challenges. Just as DevOps enabled more efficient and scalable software development practices, MLOps provides benefits across the machine learning development ecosystem.
From silos to collaboration
Data scientists, machine learning engineers, and operations teams traditionally work in silos. That makes it hard to maintain a cohesive workflow from model development to production and maintenance.
Integration and collaboration ensure that models align with business goals and deploy smoothly. For example, MLOps could uncover the need for different users to access data for multiple use cases. The team can then seek solutions to allow monitoring and reporting simultaneously, such as setting up the platform with a digital twin running in parallel.
From inefficiency to automation
Manual data collection, preprocessing, and model training processes are time-consuming and prone to errors. These tasks can consume valuable resources, diverting attention from more strategic initiatives—inefficiencies in resource utilization lead to increased costs and reduced productivity.
Without MLOps, teams can encounter a variety of challenges:
- Complexity of AI model deployment, monitoring, and scaling
- Struggle to maintain high-quality data and continuous model updates for valuable insights
- Redundant manual tasks slow down iteration
- Inadequate tracking creates logging issues
- Problems with traceability and efficient management and monitoring during model development
All those inefficiencies stack up to form a chaotic ML development cycle. Automating these processes with MLOps ensures more effective resource use, allowing teams to focus on innovation and high-value tasks.
From model degradation to high-performance
AI models require continuous monitoring and retraining to maintain their performance over time. Without MLOps, tracking model performance and implementing updates can be cumbersome and error-prone. Models that are not regularly monitored and updated can degrade in performance, leading to inaccurate predictions and suboptimal outcomes.
MLOps provides the necessary infrastructure to automate monitoring and maintenance, ensuring models remain effective and don’t degrade over time. Precision and accuracy are paramount for some use cases.
When a client needed an ML system that could take in massive datasets from IoT devices and recognize objects like road signs, trees, vehicles, and traffic lights in real-time without fail, we leveraged MLOps to ensure ML model version control, automated retraining, and deployment to containers. We used canary deployment to test the accuracy of the new version with automatic rollback, guaranteeing the system’s reliability and stability.
From guesswork to reproducibility and compliance
Reproducibility is critical in machine learning to verify results and comply with regulatory standards. Without MLOps, achieving consistent and reproducible results can be challenging. Inconsistent results can undermine trust in AI models and complicate compliance with industry regulations. MLOps frameworks enforce standardization and documentation, making it easier to reproduce results and meet compliance requirements
From delays to rapid deployment
The traditional approach to AI model deployment is often slow and labor-intensive, leading to delays in bringing AI-driven solutions to market. Delays in deployment can result in missed opportunities and reduced competitive advantage. MLOps accelerates the deployment process through automation and streamlined workflows, enabling faster time to market for AI solutions.
Understanding the business benefits of MLOps and AI
Whether your company is building AI-driven solutions for internal use or to build AI products, MLOps ensures your AI will be robust, scalable, and aligned with organizational goals. Companies that optimize AI with MLOps raise the value of AI and ML investments.
That’s because MLOps takes the guesswork out of making ML models by establishing collaboration, standardization, and repeatability. Alignment across Data Engineering, DevSecOps, and Machine Learning reduces costs and drives operational efficiency and speed.
The tools and practices of MLOps streamline the whole lifecycle of AI models, from development and deployment through monitoring and management. And since MLOps is a flexible framework for orchestrating the AI development process, you can adapt it to any tech stack. You won’t get locked into any specific cloud provider, data engineering software, or machine learning tools.
At Intellias, we work with all the cloud providers and don’t tell customers what cloud to use. We are happy to work with whatever each client prefers for their business needs and bring deep experience with:
- On-prems and all major cloud providers, including Microsoft Azure, Amazon Warehouse Services (AWS), and Google Cloud Platform (GCP)
- Cloud-native analytics platforms, including Azure Machine Learning, Amazon SageMaker, and Microsoft Fabric
- Independent ML tools, including Databricks, HuggingFace, and TensorFlow
- Kubernetes platform as a code tools, including MicroK8s, Charmed Kubernetes, and RedHat OpenShift
Take a look at the key benefits of some of our recent projects:
Improving product reliability and adaptability
When a client needed a Fleet Management System, Intellias developed an all-in-one platform with real-time fleet data integration. Thanks to MLOps practices, the Data Science and ML Engineering teams are both empowered with access to current and historical data. This makes the team adaptable and gives everyone who needs it insight into facilitating strategic planning and operational improvements.
This platform now supports a range of monitoring and management services with real-time visibility into the activities of fleets, cargo, and drivers. The client, a provider of on-the-road payment solutions, now relies on its insights to fuel business results including lower operational costs, enhanced efficiency, and more timely deliveries.
Enhancing responsiveness to customer needs
A leading European mobility service provider came to Intellias to help boost customer engagement and provide detailed sustainability information. We used MLOps to build a smart AI agent to respond to queries about cargo transportation sustainability and CO2 emissions.
MLOps practices allow for continuous development, testing, and deployment of machine learning models, enhancing the company’s responsiveness to customer needs.
Building scalability into a global employee support tool
Intellias developed another AI-powered chatbot for the global Sales team at a leading car manufacturer in Asia. The tool features a multi-language and multi-course learning environment. It helps salespeople around the world sell products and fill knowledge gaps.
MLOps practices made it possible to support integration with various LMS and an NLP framework for many languages.
How to implement MLOps for advanced AI
MLOps practices are applied across the entire AI application lifecycle, from data collection and preprocessing to model training, deployment, and monitoring.
DevOps facilitates seamless integration for Continuous Integration and Continuous Delivery (CI/CD) of ML models. CI/CD ensures timely, tested, and validated deployments, supporting agile decision-making and improving business outcomes.
MLOps doesn’t stop there; it also adds continuous training (CT). ML models can be prone to data drift, which occurs when input data begins to impact training and results. Continuous monitoring and training make models more resilient, protecting them against data drift and granting them more longevity.
This comprehensive approach ensures that AI models are not only accurate but also maintainable and scalable.
- Data Management: Automated data pipelines ensure that data is collected, cleaned, and prepared consistently, reducing the risk of errors and biases.
- Model Development: Automated training processes allow for the rapid development and testing of multiple models, selecting the best-performing ones for deployment.
- Model Deployment: Model production review leads to model deployment configuration, and the model is ready to deploy for inference.
- CI/CD: Continuous integration, delivery, and training pipelines automate the deployment of models into production and ensure their seamless integration into existing systems.
- Monitoring and Maintenance: Continuous monitoring tools track model performance and alert teams to any issues, enabling quick resolution and ongoing optimization.
MLOps lifecycle by Intellias
At Intellias, we leverage our expertise in AI, ML, and MLOps to help your business realize the full potential of its data, automate processes, and gain a competitive edge through advanced technology solutions.
The CI/CD+CT process, which stands for “continuous integration, continuous deployment, and continuous training,” is a best practice for streamlining development and deployment. CI/CD+CT automates the integration of code changes on a rolling basis, ensuring modifications can be tested and deployed quickly and reliably.
Our approach to MLOps solutions
We’ve designed our approach to MLOps to ensure seamless integration, robust management, and continuous improvement of machine learning models within your business operations. We rely on these four pillars for driving AI success with MLOps:
- Maximizing the potential of AI
- Extracting reliable insights from your data
- Minimizing redundant manual tasks
- Enhancing competitiveness and AI/ML ROI
Here’s how we handle each stage of the MLOps lifecycle:
- Data quality: Our subject matter experts will help your business define the right data for the specific problem you’re currently facing and ingest, validate, and prepare the data needed for your model.
- Experiment: We’ll use the right tools to ensure modeling and experimentation are tracked, registered, and centrally managed.
- Design/deploy: We’ll manage a streamlined ML product development process with version control, logging, and a model registry. Effective iteration management will ensure that the product perfectly suits your needs by the time you deploy.
- Operate: Our team offers a wide range of environments to successfully adapt to different paces, sizes, and levels of deployment.
- Automate: Once a model reaches maturity and its value is quantified, our Ops team will implement automation to ensure it stays relevant throughout its lifecycle.
Applying the MLOps lifecycle
By integrating ML lifecycle management with MLOps, Intellias creates a streamlined, automated workflow that enhances training and production environments. This results in a highly efficient CI/CD+CT process.
We use the CI/CD+CT process to automate the integration of code changes on a rolling basis, ensuring changes are deployed quickly and reliably, and protect against data drift.
Continuous integration, delivery, and training ensure that machine learning models are developed, deployed, and maintained for optimal performance, which keeps them up-to-date and functioning at their best. This ultimately boosts business agility and operational excellence.
Our Three Vs for enhancing AI through MLOps:
- Velocity: Experimentation means prototyping and iterating on ideas quickly
- Validation: Monitor changes, proactively search for bugs, and prune bad ideas
- Versioning: Stay agile with multiple versions to minimize production downtime when something doesn’t go as planned and to run A/B tests
Challenges and issues with AI and MLOps platforms
Implementing AI and MLOps is easier said than done, and simply adopting AI and MLOps platforms won’t guarantee success. A few hurdles can significantly impact AI initiatives’ effectiveness and return on investment. Here are some common challenges businesses face:
- Visible ROI: Achieving visible ROI from AI and ML investments can be challenging. The significant upfront costs and the time required to see tangible benefits often lead to skepticism among stakeholders.
- Solution: Use MLOps to reduce time and cost to develop ML projects and use CI/CD+CT to understand and improve model ROI after launch
- Data Management: Data silos, inconsistent data quality, and lack of centralized governance can hinder the effectiveness of AI models. Without robust data management, the reliability and accuracy of predictions suffer.
- Solution: Form a cross-functional MLOps team and use MLOps principles to automate data quality and logging to get everyone on the same page.
- Talent and Resources: There is a notable shortage of skilled professionals in AI and MLOps. Finding, retaining, and continuously upskilling talent can be resource-intensive and slow down project implementation.
- Solution: Maximize resources by using MLOps to automate tedious tasks and amplify your employees’ capacity.
- Implementation Culture: Adopting AI and MLOps requires a cultural shift towards innovation and collaboration. Resistance to change and lack of executive support can impede the successful integration and scaling of these technologies.
- Solution: Leverage best practices in change management to gain organizational buy-in for the efficiency and business value of implementing AI with an MLOps framework.
Transform AI through MLOps strategies
It’s hard to overstate how competitive today’s AI landscape is. Faster deployment of AI capabilities with MLOps means reduced time to market, improved model accuracy, and new avenues for growth. Integrating MLOps for powerful AI is about change management as much as technology. You’ll want experts to guide you through this transformation.
Our MLOps and AI specialists at Intellias provide tailored solutions to help businesses fully capitalize on their AI and MLOps investments. From the outset, we help businesses identify and track KPIs for their AI investment, provide comprehensive ROI analysis, and set realistic expectations.
Our experts ensure data quality and help set up centralized data governance frameworks. We also provide access to a pool of highly skilled AI and MLOps professionals and continuous training and upskilling programs.
We’ll work closely with your organization to foster a culture of innovation and collaboration. Our change management strategies will support a smooth transition to MLOps and strong executive buy-in through workshops, training sessions, and executive briefings.
Intellias Project Management Office (PMO) consultants offer:
- Strategic project implementation including phased rollout, communication plans, and monitoring benefits realization
- Management framework and documentation including ready-to-use templates and checklists, a robust governance structure, and tailored configuration of Project Portfolio Management (PMM) tools
- Operational Excellence including custom dashboards and workflows, advanced resource management features and collaboration tools, and integrations with existing systems and AI-powered productivity tools
- Team Training Resources including training sessions on Fundamental and Advanced Project Management, Scrum, Kanban, SAFe, LeSS, Hybrid delivery, and PM power skills, as well as in-depth user guides and resources
Whether you need help deploying MLOps on your infrastructure or want to run an MLOps as a Service model in the cloud, we have you covered. Learn more about Machine Learning Operations Services from Intellias.
