Blog Posts

• MLOps for Accelerating Enterprise AI Solutions

Requirements, scope, purpose, and the very nature of data are some key factors that influence the design & development of AI/ML models. As these factors vary widely from one organization to another, design and deployment processes, too, tend to vary drastically. There's truly no single way to design and operationalize ML models and, given the inherent complexity of models, machine learning SDLCs become exceptionally convoluted & confusing.

That is where MLOps comes in. First coined in the “Hidden technical debt in machine learning systems” paper in 2015, the seed of the idea germinated from the inherent challenges of dealing with vast volumes of data, model complexity, and how DevOps can be used to streamline ML workflows. When implemented tactfully, the central principles of DevOps can speed up and simplify developments and delivery of AI workloads. Incorporating continuous integration and continuous delivery methodologies and automation to ML SDLCs leads to a streamlined assembly line with well-defined stages.

• AIOps for Enhancing Enterprise IT Operations

Machine learning operations tailors & streamlines ML software development lifecycles & workflows according to DevOps philosophies. It streamlines entire machine learning projects end-to-end. On the other hand, AIOps or Artificial Intelligence operations, involves integrating AI approaches such as machine & deep learning, natural language processing, computer vision, etc., to improve efficiency & productivity of IT service management and operations. AI models enable improved automation & collaboration, better monitoring & predictions, and obviously, higher optimization & productivity.

AIOps harnesses the power of appropriate ML models (neural networks, support vector machines, decision trees, etc.), big data, and analytics to :

➤ Ingest vast volumes of information produced by myriad tools, components & processes in an IT tech stack and uncover valuable knowledge through systemic analysis.

➤ Identify vital events and important patterns pertaining to workflows, application & infrastructure performance, security,

➤ Analyze and diagnose root causes of events, automate reporting, responses, remediation, incident management, as well as resolve issues with minimal to zero human intervention.

➤ Mine historical & real-time data to anticipate critical issues before they can impact performance, thereby, allowing IT operations to make timely interventions

➤ Powerful predictive capabilities enable accurate & timely prediction of future resource demands and, consequentially, efficient provisioning of resources.

From a business perspective, the most worthwhile benefit of AIOps is it employs tools & techniques that can learn on the fly & do so continuously. AI-powered operations systems perform proactive incident management, identifying false positives and prioritizing the most urgent alerts. This gives IT teams the ability to focus on the most potent & relevant issues before they can hamper performance, cause outages, or lead to poor customer experiences.

• How AIOps Aids Generative AI Governance?

Ethical concerns and misunderstandings regarding generative AI usage have risen substantially recently. While they can be easily dismissed as natural human tendency to mistrust the unknown & unfathomable, much of the alarm is now been voiced by researchers and designers at the forefront of AI technology.

Technologists, scientists, and policymakers are voicing their concerns and highlighting the importance of strict AI governance through a well-defined code of ethical conduct and regulatory framework. When it comes to generative AI, their rapid adoption, widespread availability, and susceptibility to bias pose significant risks, hence making a robust governance strategy vital.

AIOps unique capabilities can be leveraged to monitor, manage, and ensure compliance with ethical, regulatory, & organizational standards. Below is an overview of AIOps-empowered governance framework --

➤ Lifetime Governance

AIOps can be used to formulate pre-defined governance policies that adhere to ethical guidelines, organizational principles, and regulatory requirements. Operations teams can follow AIOps practices to design automated risk evaluation mechanisms for identifying & quantifying risks during deployment & usage.

➤ Continuous Monitoring

AIOps tools can monitor generative AI systems & endpoints continually for performance issues & output validation. They can monitor latencies, anomalies as well as generated output for adherence to quality & ethical standards.

➤ Automated Compliance & Bias Management

Operations teams can run automated checks to ensure system compliance with data protection & regulatory laws. Furthermore, AIOps tools can analyze outputs for bias, flag common bias & fairness issues, biased training data, and associated model behavior.

➤ Quick Incident Response

AIOps can detect unusual activity or suspicious outputs and generate alerts for quick response. ITOps teams can define automated remediation procedures to resolve common issues quickly. This helps cut downtime, overhead costs, and system integrity.

➤ Ethical Oversight

Whether it’s maintaining data privacy & integrity, managing user permissions, or flagging generated content, AIOps makes undertaking all ethical oversight responsibilities simple & streamlined.

➤ Lifecycle Management

AIOps facilitates generative AI deployments and can integrate seamlessly with MLOps lifecycles. It becomes easy to track and manage any changes to models, control integration & model versioning, and ensure policy compliance throughout.

➤ Improved Collaboration

Intuitive dashboards allow users & even different stakeholders to monitor generative AI systems. AIOps systems can generate automated reports on model performance, compliance status, and potential risk assessment.

• The Biggest Benefits of a Well-Defined Enterprise MLOps Strategy

MLOps defines the standardized best practices for automating and streamlining the development & deployment of machine learning models. Integrating DevOps practices & philosophies with pipelines for building & running ML models allows enterprises to speed up deployments and automate tasks easily. MLOps ensures close collaboration among everyone involved (data scientists & ML engineers to IT operations), automated integration & delivery, and continual model improvement.

Like DevOps, MLOps defines a culture and a set of best practices that unify machine learning development with ML system deployment and operations. The central idea behind the concept is to automate and standardize the intrinsic workflows/processes along the entirety of the ML software development life cycle.

The benefits of implementing machine learning operations are way too prominent for any enterprise to ignore.

- Improved Efficiency :

Automating manual & tedious tasks saves up loads of valuable time & resources. Data science and machine learning engineering teams can work much more closely with one another and focus on higher-level aspects of the workflow such as feature engineering and model development.

MLOps makes the entire machine learning solution life cycle much more efficient by automating and simplifying stages like data cleaning & preparation, deployment, performance tuning, and monitoring. This saves time, reduces errors, and makes scaling & easier.

- Better Model Accuracy & Performance :

Continuous integration and monitoring are central to MLOps. Consequentially, this allows for faster identification & rectification of issues, quick improvements, and higher accuracy & reliability. MLOps facilitates automated data analysis and ensures optimal quality & relevancy.

- Quicker Time to Market :

MLOps streamlines machine learning production cycles and enables organizations to deploy solutions faster. Traditional approaches can take weeks or even months to ensure impeccability across all steps of the process.

- Improved Scalability, Performance & Governance :

A proper MLOps strategy ensures systematic & scalable development. Consistency, reproducibility, and governance are assured throughout a lifecycle. Continuous automation accelerates design and delivery, and centralized monitoring ensures optimal performance & accuracy throughout.

• How NatWest Built a Secure & Scalable MLOps Platform with AWS? A Case Study

One of the largest business and commercial banks in the United Kingdom, the NatWest Group supports millions of individuals throughout the UK and beyond. The group’s decision to integrate MLOps into its operational processes was focused on realizing the value of the vast volumes of diverse data and data science activity perpetuating its myriad business processes. The adoption of MLOps provided the standards, tools, and frameworks for supporting data science and ML engineering teams & helped translate their ideas from the whiteboard to production in a timely, secure, and streamlined manner.

As the NatWest Group looked to scale up their advanced analytics capabilities, they realized that the time & effort required to design, develop, deploy, and operate ML models & solutions were becoming substantial. This led to the partnership with Amazon Web Services, wherein the enterprise aimed to leverage AWS resources to design, build, and launch a secure, scalable, and sustainable platform for developing & deploying ML-based services.

Experts from AWS Professional Services worked closely with NatWest Group for accurate & accelerated implementation of Amazon SageMaker, a unified platform for building production-ready ML models & solutions, in line with relevant best practices. The joint AWS-NatWest Group expert team followed a flexible five-step process to design, build, test, & deploy a new ML-model production platform.

• The MLOps Process

- Discovery

The teams conducted multiple brainstorming sessions to identify the biggest pain points in every ML lifecycle the company developed. Typical pain points included the myriad challenges involved in discovering relevant data, infrastructure procurement & management, model designing & tuning, route-to-live, and overall governance. The sessions helped both teams identify core requirements, priorities, dependencies, and success criteria for the envisioned MLOps platform.

- Design

The NatWest & AWS teams began working on the final design by correlating & converging their respective best practices. This ensured complete compliance with all the different security & governance requirements critical within the financial services domain.

- Develop

Based on the information accrued during the discovery phase & subsequent design plans, teams began iterating toward the final design. Together, the Terraform and AWS CloudFormation templates defined the overall MLOps platform architecture.

Feedback was gathered from all possible end-users & stakeholders such as data scientists, machine learning & data engineers, operations & support teams, security & governance teams, etc. to ensure alignment with original goals.

- Test

Critical testing of the platform’s ability on real data analytics and machine learning use cases was conducted. NatWest identified three specific use cases that covered a range of different business and data science challenges of varying complexity. The AWS and NatWest groups created the baseline templates and SageMaker MLOps pipelines from these use cases to test platform scalability, flexibility, and accessibility.

- Launch

Once every aspect of the platform was checked & tested, it was launched and integrated into the organization. Bespoke training plans and support were provided to end-users & authorized teams to ease onboarding of different use cases.

The federated MLOps platform, so designed, was based on a scalable framework that accelerated data discovery, quickened data access across the enterprise, defined a secure & controlled environment & a managed toolset for easy innovations & quick deployments, and an automated, federated, DevOps-driven approach to infrastructure & application coding. Below is a quick overview of the MLOps pipeline.

• The NatWest MLOps Platform – Features & Highlights

In its simplest form, the NatWest MLOps framework will aid a data consumer (data scientist or ML engineer) to pore through data repositories, discover relevant data for model training, testing, and validation, designing & tuning models, and put them to production. Through the strategic implementation of the framework with AWS SageMaker, the enterprise aims to reduce computation costs, operational overhead, and time to scale as well as simplify route-to-live.

A granular look at the lower levels of the MLOps framework reveals the following features --

• Self-Serviceable Infrastructure Deployment - Lowers dependency.

• A Central Python Package Management System - Quickens access to pre-approved packages for development 

• CI/CD Pipelines for Development & Production - Reduces time-to-live by integrating CI/CD pipelines through IaC templates.

• Model Testing - Unit, integration, model, and all kinds of end-to-end testing facilities are available automatically.

• Model Decoupling & Orchestration - Reduces resource requirements and time to deploy and ensures effective orchestration.

• Code Standardization - Ensure code quality & standards validation

• Generic ML Templates - AWS Service Catalog templates allow quick and easy instantiation of ML modeling environments and associated pipelines nearly instantly.

• Quality Monitoring - Easy monitoring of drift in data quality and model performance

• Bias Monitoring - Automated checks for any data imbalances and changes that will introduce any kind of bias into the model

To prove the capability of the MLOps pipeline so designed, the joint NatWest and AWS team selected three use cases from different divisions.

• Key Highlights

• The 3 use cases from different divisions were then made available in a local Amazon S3 data bucket associated with the case development account. The predictions generated by the deployed ML models were written back, along with use case data, to NatWest’s cloud-hosted cloud data lake.

• The case development account is NatWest’s SageMaker MLOps platform for developing ML models. It includes SageMaker Studio and automates solution deployment as well as implements MLOps capabilities. Alongside the development accounts, there are the testing & model production accounts for the testing and development teams respectively. All three modules are key elements of the MLOps pipeline.

The three-account structure, along with restricted access, and data encryption, ensured airtight security and scalability.

• The SageMaker Studio serves as an IDE within the development accounts and facilitates access to pre-configured MLOps pipeline templates through SageMaker projects. These templates were used to set up CI/CD-aligned infrastructures quickly, along with all necessary services and capabilities. They were then customized and extended alongside specific workflow needs and design requirements of specific use cases.

• The Cloud Data Explorer is an upstream module of the ML architecture that was designed. It enables users (data scientists & ML engineers) to browse, discover, and pull relevant pre-approved data from NatWest’s data lakes. It is primarily a data discovery and preparation tool & works before ML model development & MLOps pipeline, during the critical initial stages of the data pipeline.

• SageMaker pipelines orchestrate the entire ML workflow by integrating CI/CD, managing experimentation, testing, & validation, and automating monitoring, infrastructure management, & operations.

• The Result

The NatWest and AWS teams were able to make the MLOps solution production-ready within 9 months. The overall achievements included :

• MLOps capabilities were scaled enterprise wide. Over 300 data scientists & data engineers were trained to work with the platform.

• Federated teams were able to use the AWS Service Catalog to deploy secure, cost-efficient, sustainable, and managed SageMaker infrastructure on demand.

• ML model development and deployment processes were standardized centrally.

• Reduced technical debt, creation of reusable artifacts & refactorable codes, lower idea-to-value time, lower ML use case environment creation time, and consequentially lower time-to-live – these were some of the most prominent achievements that transformed how NatWest leveraged AI & operated.

• Conclusion:

Developing a robust MLOps strategy is crucial for enterprises to deploy game-changing ML solutions quickly and cost-effectively. Meticulously planned and well-defined MLOps strategies are the backbone of successful machine learning development initiatives. A good strategy bridges the gap between development and production, improves collaboration, and ensures scalability, flexibility, & efficiency, all the while making sure models are compliant, secure, and manageable.

• How Can We Help?

ITPN has leading-edge capabilities, top-class expertise, and pioneering experience in tailoring business-specific MLOps strategies. We have top-tier domain experts who will optimize your business’s machine learning development and operations and enhance productivity and efficiency. Please contact us with your queries or if you need any assistance regarding our services.