MLOps – Applying DevOps to Competitive Advantage
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The document discusses the concept of MLOps, which integrates DevOps principles to enhance the development, operationalization, and scaling of AI/ML models, streamlining collaboration across various roles. It highlights significant data management challenges that hinder AI initiatives, reveals that only 1% of AI/ML projects succeed in production, and emphasizes the importance of using MLOps for effective AI implementation. The presentation also identifies the necessity of automation, governance, and collaboration in achieving successful machine learning outcomes.
MLOps – Applying DevOps to Competitive Advantage
- 1. MLOps: Applying DevOps to Competitive Advantage Presented by: William McKnight President, McKnight Consulting Group linkedin.com/in/wmcknight www.mcknightcg.com (214) 514-1444
- 2. 8th December, 2022 Put AI Into Action And Boost Productivity with MLOps Abhilash Mula Senior Manager, Product Management
- 3. 2 © Informatica. Proprietary and Confidential. New World of Cloud AI & Analytics Situation: Unprecedented volume/type of data, on multiple clouds, leveraged by multiple user profiles, with exploding AI/ML usage 500 million business data users 64.2 zettabytes of data per year 1 billion workers assisted by AI/ML 80% of organizations store data in multi-hybrid Data in the Multi Cloud, Hybrid 46 billion connected devices New Users Machine Learning/AI New Data Types (mobile, social, IoT) Explosion in Data Volume
- 4. 3 © Informatica. Proprietary and Confidential. Data Management Challenges Are Derailing AI & Analytics Initiatives Cost Overruns 75% of organizations using cloud data management will encounter budget overruns resulting in their questioning the value of using cloud services Resource Constraints 96% of IT and engineering decision-makers say no- code/low-code will be a priority because of the lack of software engineers Complexity 72% of organizations are still struggling to operationalize within their enterprise 96% 75% 72% Source: 1– Aptum cloud impact study | 2– Advanced Global Research, May 28, 2020 | 3- Venturebbeat.
- 5. 4 © Informatica. Proprietary and Confidential. AI/ML Projects Rarely Make It into Production Only 1% of AI/ML projects are successful *Source: Databricks research 2018
- 6. 5 © Informatica. Proprietary and Confidential. MLOps Streamlines the Development, Operationalization, and Execution of AI/ML Models MLOps covers all the key phases of AI/ML Prepare Data Build Model Deploy, Consume and Monitor Understanding the objectives and requirements of the project and preparing the data needed for the model. Build and assess various models based on a variety of different modeling techniques. Operationalize and monitor the models to deliver business value and performance.
- 7. 6 © Informatica. Proprietary and Confidential. MLOps is a Team Sport Cross-functional collaboration is key Business Expert Data Scientist Data Engineer Data Steward Data Analyst Citizen Integrator
- 8. 7 © Informatica. Proprietary and Confidential. One-click, serverless deployment of ANY AIML Model Only with Informatica, data scientists and ML engineers can operationalize AI/ML models @ scale with ModelServe • Simple, easy-to-use, wizard-driven approach for data scientists and ML engineers to deploy and operationalize any AI/ML models at scale • Provide flexibility for data scientists and ML engineers to build their AI/ML models in any framework and consume them in any application • Enable data scientists to accelerate AI/ML initiatives with high-quality, trusted, and governed data • Improve the productivity of data science teams by streamlining and automating the process of building, deploying, and monitoring machine learning models • Enhance model performance with timely delivery of trusted data using integrated DataOps
- 9. 8 © Informatica. Proprietary and Confidential. Call to Action Sign up for Informatica ModelServe Public Preview Download the MLOps White Paper to Put AI Into Action
- 10. Thank you
- 11. William McKnight President, McKnight Consulting Group • Frequent keynote speaker and trainer internationally • Consulted to Pfizer, Scotiabank, Fidelity, TD Ameritrade, Teva Pharmaceuticals, Verizon, and many other Global 1000 companies • Hundreds of articles, blogs and white papers in publication • Focused on delivering business value and solving business problems utilizing proven, streamlined approaches to information management • Former Database Engineer, Fortune 50 Information Technology executive and Ernst&Young Entrepreneur of Year Finalist • Owner/consultant: Research, Data Strategy and Implementation consulting firm 2
- 12. McKnight Consulting Group Offerings Strategy Training Strategy Trusted Advisor Action Plans Roadmaps Tool Selections Program Management Training Classes Workshops Implementation Data/Data Warehousing/Business Intelligence/Analytics Big Data Master Data Management Governance/Quality Implementation 3
- 13. McKnight Consulting Group Client Portfolio
- 14. ML Uptake is Strong 5
- 15. Use Cases for ML Flow optimization Modeling and analytics Predictive insights Threat and risk analysis Public Sector Traffic flow management Smart city planning Autonomous routing Situational Awareness Oil and Gas Pipeline modelling Drilling patterns and asset utilization Intelligent planning Safety assurance Manufacturing Supply chain optimization Production optimization Predictive maintenance Fault identification Retail Supply chain optimization Customer experience Segmentation analysis and forecasting Fraud and theft identification Healthcare Patient care pathway optimization Disease research and drug creation Early diagnosis of conditions Patient safety Technology Operational efficiency Log analysis Capacity planning Cybersecurity and zero-day detection 6
- 16. Drivers to MLOps • Senior management does not always see ML as strategic, and it can be difficult to measure and manage the value of ML projects. • ML initiatives can work in isolation from each other, resulting in difficulties aligning workflows between ML and other teams. • To be effective, ML training requires large quantities of high-quality data, which creates significant overheads across data access, preparation, and ongoing management. • ML/data science work requires a large amount of trial and error, making it hard to plan the time required to complete a project. 7
- 17. What is MLOps? • MLOps is a practice for collaboration between data science and operations to manage the production machine learning (ML) lifecycles. • As an amalgamation of “machine learning” and “operations,” MLOps applies DevOps principles to ML delivery, enabling the delivery of ML- based innovation at scale to result in: – Faster time to market of ML- based solutions – More rapid rate of experimentation, driving innovation – Assurance of quality, trustworthiness, and ethical AI 8
- 18. From ML to MLOps • Many companies have built strong ML capabilities • Few businesses have been successful in putting the majority of their ML models into production, leaving a sizable amount of value untapped. • Machine learning operations, also known as MLOps, are a set of standards, tools, and frameworks that are used to scale ML to reach its full potential. • Three main objectives of MLOps, which concentrates on the entire life cycle of ML model design, implementation, testing, monitoring, and management, are as follows: – To create a highly repeatable procedure for the entire life cycle of a model, from feature exploration to model deployment in production. – Data scientists and analysts should be shielded from the complexity of the infrastructure so they can concentrate on their models and plans. – Develop MLOps so that it scales without a horde of engineers, along with the number of models and modeling complexity. 9
- 19. MLOps Operations • For modern enterprises, use of ML goes to the heart of digital transformation, enabling organizations to harness the power of their data and deliver new and differentiated services to their customers. Achieving this goal is predicated on three pillars: • Development of such models requires an iterative approach so the domain can be better understood, and the models improved over time, as new learnings are achieved from data and inference. • Automated tools and repositories need to store and keep track of models, code, data lineage, and a target environment for deployment of ML-enabled applications at speed without undermining governance. • Developers and data scientists need to work collaboratively to ensure ML initiatives are aligned with broader software delivery and, more broadly still, IT-business alignment. 10
- 20. Why not DevOps? • Connect data and services. DevOps success depends on how well platforms of data and existing/new services can be integrated, adapting to changing circumstances. • Automate deployment. Automation needs to be considered in the context of the above, to ensure constant, consistent delivery of business value. • Operate and orchestrate resources. A commoditized, flexible platform is table stakes: as platform efficiency increases, so does DevOps effectiveness. 11
- 21. The goal is to assure the delivery of value to the business, its customers and other stakeholders. 12
- 22. Terminology • Pipeline. Each development iteration of an ML-based application will follow a planned and automated series of steps. The pipeline itself can be put under configuration control, such that the steps can be repeated. • Datasets store/Datasets. MLOps relies on an easily accessible and scalable source of data, both during training and inference. While data may come from several places, it will be prepared, cleaned and accessed as a single resource. • Repository. A common, version-controlled storage resource (e.g. Git, Artifactory, Azure Artifacts) for data, model and configuration schemas, managing dependencies between models, libraries and other resources. • Registry. A logical picture of all elements required to support a given ML model, across its development and operational pipeline. 13
- 23. Terminology • Workspace. Model and application developers conduct their activities within individual workspaces, accessible graphically or via code (e.g. written in Python), with access control over data sets, models and insights • Target. A deployment environment for ML models and code, packaged for example as containers/microservices that is often cloud- based, but can include on-premises and edge-based environments. • Experiment. Outputs of a given iteration or run need to be stored so they can be assessed, compared and monitored for audit purposes. • Model. Packaged output of an experiment which can be used to predict values or built on top of (via transfer learning). • Endpoint. Internet-capable computer hardware device on a TCP/IP network. 14
- 25. Stakeholders 16
- 26. Applying MLOps in Practice • Configure Target – Set up the compute targets on which models will be trained. • Prepare data – Set up how data is ingested, prepared and used • Train Model – Develop ML training scripts and submit them to the compute target • Containerize the Service – After a satisfactory run is found, register the persisted model in a model registry. • Validate Results – Application integration test of the service deployed on dev/test target. • Deploy Model – If the model is satisfactory, deploy it into the target environment • Monitor Model – Monitor the deployed model to evaluate its inferencing performance and accuracy 17
- 27. For iterative pipelines to continue to deliver results, we need • Reproducibility – as with software configuration management and continuous integration, ML pipelines and steps, together with their data sources and models, libraries and SDKs, need to be stored and maintained such that they can be repeated exactly as previously. • Reusability– to fit with principles of continuous delivery, the pipeline needs to be able to package and deliver models and code into production, both to training and target environments. • Manageability – the ability to apply governance, linking changes to models and code to development activities (for example through sprints) and enabling managers to measure and oversee both progress and value delivery. • Automation – as with DevOps, continuous integration and delivery require automation to assure rapid and repeatable pipelines, particularly when these are augmented by governance and testing (which can otherwise create a bottleneck). 18
- 28. MLOps scenario: Customer Churn • Prepare Environment: Create and configure data stores, in this case CRM data • Normalize, transform and otherwise prepare datasets for training and inference • Point algorithms and code to the data • Enforce transparency (e.g. through audit trails) to build confidence in results 19
- 29. Create Pipelines for Training and Inference 20
- 30. Monitor Results for Applicability and Effectiveness of Insights 21
- 31. Azure Machine Learning (example) 22
- 32. Azure Solution Architecture (example) • With security controls in place, a user can provision a workspace private link, customer managed keys, and role-based access control (RBAC) using AML python SDK, CLI, or UX. ARM templates can be used for automation. • Compute instance is used as a managed workstation by data scientists and is used to build models. IT Admin can create a compute instance behind a VNet if there are restrictions in place to not use a public IP. • Compute Cluster is used as a training compute to train ML models. IT Admin (not shown) can create a compute cluster behind a VNet or enable a private link if there are restrictions in place to not use a public IP. • Once a model is created it can be deployed on AKS cluster. A private AKS cluster with no public IP can be attached to the AML workspace and an internal load balancer can be used so that the deployed scoring endpoint is not visible outside of the virtual network. All the scoring requests to the deployed model are made over TLS/SSL. 23
- 33. MLOps Features • Ease of Setup and Use – Create ML Managed Endpoints – Create Compute Resources – Manage Compute Resources • MLOps Workflow – Model Orchestration – Data Orchestration 24
- 34. MLOps Features • Security – Network – User – Data • Governance – Monitoring – Control • Automation – Experiments – Workflow – Code and App Orchestration – Event-Driven 25
- 35. MLOps Features • Experiment Management • Scheduling • Accuracy Management • Retraining 26
- 36. MLOps Features • Model Explainability • A/B Model Testing • Granular Data Preparation 27
- 37. Midsize Organization MLOps Costs Category Type Price Per Time Time Units Per Year Subtotal Units Amount ML1 Compute E8 v3 $0.504 8,760 $4,415 16 $70,641 Service included $0.000 8,760 $0 16 $0 ML2 Model Training Per node per hour $19.32 8,760 $203,092 0.2 $33,849 Batch prediction Per node per hour $1.160 8,760 $10,162 16 $162,586 ML3 Compute ml.r5.2xlarge $0.504 8,760 $4,415 16 $70,641 Service ml.r5.2xlarge $0.101 8,760 $885 16 $14,156 28
- 38. Maturity Levels 29 1 Just gaining an understanding of using machine learning. No data scientists hired. Early data models built without much success. There is a belief that whatever DevOps processes are in place will handle ML. 2 The data architecture serves most data that would be necessary for ML. A cloud commitment and direction is present, providing scale for ML. A first data scientist is hired and prototyping is done. A full lifecycle ML is accomplished with manual processes. MLOps is still an afterthought. 3 This company is actively looking to deliver the benefits of ML across the company. There is recognition of ML at the executive level. However, early processes in use resemble DevOps and will not scale. Company begins forking their DevOps for ML. 4 There is company-wide embracement of ML. Benefits have been produced and realized. There are numerous and ample data scientists and the data architecture has matured so that more ML benefits can be realized. Although there still isn’t full consistency in processes, the company has embraced MLOps and is rapidly adapting it. 5 The business has fundamentally changed due to ML and it could not have done so without MLOps. ML is applied to initiatives wherever possible. MLOps is nurtured as much as ML and includes model sharing, reusability and reproducibility, model diagnostics and a strong path to production. Governance has become central to ML strategy, ensuring outcomes that are explainable and transparent. As featured in
- 39. In Conclusion • ML Uptake is Strong • A MLOps workspace is a cloud-based development environment that enables you to collaboratively develop, test and deploy machine learning models • Develop iterative pipelines to continue to deliver result • Automation is a key differentiator in MLOps platforms • Embrace Transparency and Predictability 30
- 40. MLOps: Applying DevOps to Competitive Advantage Presented by: William McKnight President, McKnight Consulting Group linkedin.com/in/wmcknight www.mcknightcg.com (214) 514-1444