\u00a0<\/span><\/h3>\nThe complete\u00a0MLOps\u00a0process has three phases – Designing the ML powered application, ML experimentation and development, and ML operations. The first phase, simply called \u201cDesign\u201d,\u00a0involves identifying the potential users, broadly defining the ML solutions\u00a0to solve the\u00a0problem and assessing the further development of the project. This is devoted to business understanding, data understanding and designing the software. The next phase, \u201cModel development\u201d involves assessing the feasibility of ML for the problem by implementing a\u00a0PoC\u00a0to identify the suitable algorithm, polish the algorithm to refine the accuracy, and then arrive at\u00a0a stable model,\u00a0which delivers expected accuracy. This involves model engineering and data engineering. The next phase, \u201cML Operations\u201d involves productionizing the stable model developed in the second phase by using established DevOps practices such as testing, version control, continuous delivery and monitoring. All the three phases influence and tie in at every stage of the\u00a0MLOps\u00a0process.<\/span>\u00a0<\/span><\/p>\n![\"MLOPS](\"https:\/\/www.aspiresys.com\/blog\/wp-content\/uploads\/2021\/12\/MLOPS-Process.jpg\")
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The Common Situation<\/span><\/b>\u00a0<\/span><\/p>\nLet\u2019s assume a company has a team of data scientists and their demos of the model KPIs have\u00a0overwhelmed their customers\u00a0with their solutions that were intractable for many years. So naturally, the question arises, when can these models be put into production. If you are\u00a0not familiar with\u00a0MLOps, you might think this is easy. All that is left to do is routine IT work – you might think. Turns out you are wrong. According to Deeplearning.ai, only 22 percent of companies using ML have successfully deployed their models. This low number indicates the type of thinking prevalent in the other 78 percent of companies. To understand this situation, it is illustrative and helpful to look into its root cause.<\/span>\u00a0<\/span><\/p>\nThe Root Cause<\/span><\/b>\u00a0<\/span><\/p>\nThere is a fundamental difference between ML and software engineering. While software engineering is just code crafted in a carefully controlled environment, ML is more than that. ML is code plus data. The ML model (the entity put into production) is created by applying an algorithm on a mass of\u00a0training data using code that\u00a0will affect its behavior during production.\u00a0<\/span><\/p>\n![\"ML](\"https:\/\/www.aspiresys.com\/blog\/wp-content\/uploads\/2021\/12\/ML-Model-1024x458.jpg\")
More importantly, the model predictions depend on the incoming data during production and that\u2019s something that cannot be known in advance. This is in contrast to software engineering where the output is completely deterministic. As they evolve, the code and data lines are two lines in different planes sharing only one dimension (time). This fundamental disconnect between the evolution of code and data causes several challenges which must be solved before putting a model into production.\u00a0<\/span>\u00a0<\/span><\/p>\nChallenges of Productionizing ML Models\u00a0<\/span><\/b>\u00a0<\/span><\/h3>\nAccording to a survey by\u00a0NewVantage\u00a0Partners, of around 70 top enterprises, only 15 companies managed to successfully deploy AI features in their applications. Although,\u00a0a substantial majority of them invested heavily in AI, this dismal percentage of success seems to point to an issue. The problem is that the unsuccessful companies faced several hurdles due to their manual process of deployment and production which are outlined here.\u00a0<\/span>\u00a0<\/span><\/p>\n\n- Dataset dependency:\u00a0<\/span><\/b>Steps carried out during the training and evaluation stage in the development environment vary widely in the real-world scenarios. Data depends on the use case,\u00a0which does not remain the same and changes in data fed to the model during retraining leads to poor accuracy in its predictions.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n
\n- Pipeline complexity:\u00a0<\/span><\/b>In the real world, models should be retrained on new data in order to maintain the relevance and regularity of their predictions with changing trends in\u00a0business use cases. A common strategy would be to establish a pipeline between the model and a\u00a0DataLake\u00a0where the model has access to the latest data. Human approval is needed to match the models to the relevant features and data sources. This may work in case of a single model but imagine the case where there are ensemble models and their relevant pipelines and it gets worse with federated pipelines. According to a study by Gartner\u00a0Inc, the number of models handled per company implementing AI and ML\u00a0averaged globally is 20 in 2021.This number is expected to rise to 35 by 2022. This is the pipeline complexity problem with the manual process.\u00a0<\/span>\u00a0<\/span><\/li>\n<\/ul>\n
\n- Scalability issues:\u00a0<\/span><\/b>In a manual process of developing ML models, models are typically coded by a data scientist using tools like\u00a0jupyter\u00a0notebooks. Also, the focus is on getting the right algorithm and good accuracy using different frameworks like\u00a0scikit-learn for python, caret for R etc. Each of these libraries and frameworks have their own limitations regarding scalability. The code written in such libraries does not scale in a production environment such as Hadoop where a different library such as\u00a0pyspark\u00a0for python and\u00a0sparkR\u00a0for R would be best suited.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n
\n- Monitoring limitations:\u00a0<\/span><\/b>The risk of ML models not performing well always exists and needs continuous monitoring and evaluation. Unlike in the development environment, incoming real-world data does not have parameters such as accuracy, precision, recall etc. Instead, methods such as data deviation detection, drift detection, canary pipelines, production A\/B tests need to be used which can be served well by implementing an automated\u00a0MLOps\u00a0system.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n
\n- Process Limitations:\u00a0<\/span><\/b>Handling ML systems in production will require the skills of different branches of an organization each of which will have different skills and goals. Data scientists focus on accuracy and measure data deviation, business analysts will want to enhance KPIs and production operations engineers will want to see the uptime and resources. Production teams working individually cannot handle the complexity of the environment which has complex entities such as models, algorithms, objects, pipelines\u00a0etc\u00a0without working together. Also, there is a need to version control these entities together and keep track of the associated parameters and pipelines for each version of the model with time. This is the process challenge in the manual process without\u00a0MLOps.<\/span>\u00a0<\/span><\/li>\n<\/ul>\n
Need for\u00a0MLOps<\/span><\/b>\u00a0<\/span><\/h3>\nML models which are not deployed are costly technical experiments at best but do not bring ROI. In the majority of companies, businesses don\u2019t realize the full benefit of AI as models don\u2019t make it to deployment. With manual and time-intensive processes, the timeline of the lifecycle is not in alignment with the speed and scale of the business. The models\u2019 performance degrades due to randomness and entropy, thus being out of alignment with the original business need. Even if organizations identify model decay, manually updating models in\u00a0production is a resource intensive and risky process with a high chance of outages. With\u00a0MLOps, companies can deploy, monitor and update ML models efficiently, thus paving the way for AI with ROI.<\/span>\u00a0<\/span><\/p>\nMLOps- Compelling reasons to adopt\u00a0<\/span><\/b>\u00a0<\/span><\/h3>\nAdopting\u00a0MLOps\u00a0helps you get the most out of your data and capitalize on the data strategy, thus bringing about increased ROI and deriving deeper business value from ML operations and processes. Another reason\u00a0for\u00a0using\u00a0MLOps\u00a0is that it generates new revenue streams and improves customer experiences from the increased accuracy of ML models in production. Adopting an effective\u00a0MLOps\u00a0culture brings about standardized processes across model development, testing, deployment and management. It brings about centralization of data and with the introduction of feature stores, model versioning becomes a bit less complicated and easier. Also, it helps the ML models best fit the organization and increases reusability on cloud, on-premises and hybrid data storage and model deployment.\u00a0<\/span>\u00a0<\/span><\/p>\nMLOps- The Benefits\u00a0\u00a0<\/span><\/b>\u00a0<\/span><\/h3>\nAmong many\u00a0aspects of\u00a0MLOps, the benefits directly relate to the organization’s ability to stay relevant and grow in the\u00a0sphere of business. The\u00a0benefits of\u00a0MLOps\u00a0are outlined here.<\/span>\u00a0<\/span><\/p>\n\n- Rapid innovation through robust\u00a0ML lifecycle\u00a0<\/span><\/li>\n
- Create reproducible workflow and models\u00a0<\/span><\/li>\n
- Easy deployment of high precision models\u00a0<\/span><\/li>\n
- Effective management of the entire ML lifecycle\u00a0<\/span>\u00a0<\/span><\/li>\n
- Automated and efficient ML resource management system and control<\/span>\u00a0<\/span><\/li>\n<\/ul>\n
From data processing to\u00a0analysis and auditing – when\u00a0all\u00a0done correctly-\u00a0MLOps\u00a0is one of the most valuable practices an organization can\u00a0have.\u00a0MLOps\u00a0can add\u00a0a\u00a0more valuable impact towards the growth of an enterprise,\u00a0by improving\u00a0quality and performance\u00a0over time.\u00a0<\/span>\u00a0<\/span><\/p>\nConclusion<\/span><\/b>\u00a0<\/span><\/p>\nThe benefits of\u00a0MLOps\u00a0are numerous and bring substantial increases in ROI for investments in ML projects. This is one of the most cutting-edge engineering disciplines that produces and transforms the business operations of any organization using it for their projects.<\/span>\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"Machine Learning (ML) has transitioned from a research novelty from its beginnings in 1952 to an applied business solution with…<\/p>\n","protected":false},"author":9,"featured_media":39008,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4838,4641],"tags":[3380,3381,3382,3383],"practice_industry":[4519],"coauthors":[179],"class_list":["post-22927","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-science-ml","category-enterprise-ai","tag-benefits-of-mlops","tag-ml-lifecycle","tag-mlops-culture","tag-mlops-process","practice_industry-data-and-ai-solutions"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.aspiresys.com\/blog\/wp-json\/wp\/v2\/posts\/22927","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.aspiresys.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.aspiresys.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.aspiresys.com\/blog\/wp-json\/wp\/v2\/users\/9"}],"replies":[{"embeddable":true,"href":"https:\/\/www.aspiresys.com\/blog\/wp-json\/wp\/v2\/comments?post=22927"}],"version-history":[{"count":2,"href":"https:\/\/www.aspiresys.com\/blog\/wp-json\/wp\/v2\/posts\/22927\/revisions"}],"predecessor-version":[{"id":40751,"href":"https:\/\/www.aspiresys.com\/blog\/wp-json\/wp\/v2\/posts\/22927\/revisions\/40751"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.aspiresys.com\/blog\/wp-json\/wp\/v2\/media\/39008"}],"wp:attachment":[{"href":"https:\/\/www.aspiresys.com\/blog\/wp-json\/wp\/v2\/media?parent=22927"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.aspiresys.com\/blog\/wp-json\/wp\/v2\/categories?post=22927"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.aspiresys.com\/blog\/wp-json\/wp\/v2\/tags?post=22927"},{"taxonomy":"practice_industry","embeddable":true,"href":"https:\/\/www.aspiresys.com\/blog\/wp-json\/wp\/v2\/practice_industry?post=22927"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.aspiresys.com\/blog\/wp-json\/wp\/v2\/coauthors?post=22927"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
![\"MLOPS\"](\"https:\/\/www.aspiresys.com\/blog\/wp-content\/uploads\/2021\/12\/MLOPS-1-300x239.jpg\")
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