{"id":10984,"date":"2022-05-10T08:43:41","date_gmt":"2022-05-10T07:43:41","guid":{"rendered":"https:\/\/ee.yelkdev.site\/?p=10984"},"modified":"2024-12-10T02:59:40","modified_gmt":"2024-12-10T02:59:40","slug":"challenges-of-mlops","status":"publish","type":"post","link":"https:\/\/www.equalexperts.com\/blog\/tech-focus\/challenges-of-mlops\/","title":{"rendered":"5 Major MLOps Challenges \u2013 and how to solve them"},"content":{"rendered":"

If you\u2019re struggling to realise the full potential of machine learning in your organisation, the good news is that you\u2019re not alone. According to industry analysts VentureBeat, 87% of AI projects will never make it into production.<\/p>\n

MLOps emerged to address this widespread challenge. By blending AI and DevOps practices, MLOps promised smooth, scalable development of ML applications.<\/p>\n

The bad news is that MLOps isn\u2019t an immediate fix for all AI projects. Operationalsing any AI or machine learning solution\u00a0 will present its own challenges, which must be addressed to realise the potential these technologies offer. Below we\u2019ve outlined five of the biggest MLOps challenges in 2022, and some guidance on solving these issues in your organisation.<\/p>\n

You can read about these ideas in more detail in our new MLOps playbook<\/a>, \u201cOperationalising Machine Learning\u201d, which provides comprehensive guidance for operations and AI teams in adopting best practice around MLOps.<\/p>\n

Challenge 1: Lack of user engagement<\/h2>\n

Failing to help end users understand how a machine learning model works or what algorithm is providing an insight is a common pitfall. After all, this is a complex subject, requiring time and expertise to understand. If users don\u2019t understand a model, they are less likely to trust it, and to engage with the insights it provides.<\/p>\n

Organisations can avoid this problem by engaging with users early in the process, by asking what problem they need the model to solve. Demonstrate and explain model results to users regularly and allow users to provide feedback during iteration of the model. Later in the process, it may be helpful to allow end users to view monitoring\/performance data so that you can build trust in new models. If end users trust ML models, they are likely to engage with them, and to feel a sense of ownership and involvement in that process.<\/p>\n

Challenge 2: Relying on notebooks<\/h2>\n

Like many people we have a love\/hate relationship with notebooks such as Jupyter. Notebooks can be invaluable when you are creating visualisations and pivoting between modelling approaches.<\/p>\n

However, notebooks contain both code and outputs, along with important business and personal data, meaning it\u2019s easy to inadvertently pass data to where it shouldn\u2019t be. Notebooks don\u2019t lend themselves easily to testing, and cells that can run out of order means that different results can be created by the same notebook based on the order that cells are run in.<\/p>\n

In most cases, we recommend moving to standard modular code after creating an initial prototype, rather than using notebooks. This results in a model that is more testable and easier to move into production, with the added benefit of speeding up algorithm development.<\/p>\n

Challenge 3: Poor security practice<\/h2>\n

There are a number of common security pitfalls in MLOps that should be avoided, and it\u2019s important that organisations have appropriate practices in place to ensure secure development protocols.<\/p>\n

For example, it\u2019s surprisingly common for model endpoints and data pipelines to be publicly accessible, potentially exposing sensitive metadata to third parties. Endpoints must be secured to the same standard as any development to avoid cost management and security problems caused by uncontrolled access.<\/p>\n

Challenge 4:\u00a0 Using Machine Learning inappropriately<\/h2>\n

Despite the hype, ML shouldn\u2019t always be the default way to solve a problem. AI and ML are essentially tools that help to understand complex problems like natural language processing and machine vision.<\/p>\n

Applying AI to real-world problems that aren\u2019t like this is unnecessary, and leads to too much complexity, unpredictably and increased costs. You could build an AI model to predict whether a number is even or odd \u2013 but you shouldn\u2019t.<\/p>\n

When addressing a new problem, we advise businesses to try a non-ML solution first. In many cases, a simple, rule-based system will be sufficient.<\/p>\n

Challenge 5: Forgetting the downstream application of a new model<\/h2>\n

Achieving ROI from machine learning requires the ML model to be integrated into business systems, with due attention to usability, security and performance.<\/p>\n

This process becomes even longer if models are not technically compatible with business systems, or do not deliver the expected level of accuracy. These issues must be considered at the start of the ML process, to avoid delays and disappointment.<\/p>\n

A common ML model might be used to predict \u2018propensity to buy\u2019 \u2013 identifying internet users who are likely to buy a product. If this downstream application isn\u2019t considered when the model is built, there is no guarantee that the data output will be in a form that can be used by the business API. A great way to avoid this is by creating a walking skeleton or steel thread (see our Playbook for advice on how to do this).<\/p>\n

Find out more about these challenges and more in our new Operationalising Machine Learning Playbook, which is available to read here<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

From model training to validation, we\u2019re sharing some of the biggest challenges associated with MLOps in our latest playbook. You\u2019ll find expert guidance on how to identify these challenges, and \u2013 crucially \u2013 how to solve them.<\/p>\n","protected":false},"author":164,"featured_media":10986,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"categories":[3],"tags":[99,266,276,265],"location":[397],"class_list":["post-10984","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tech-focus","tag-ai","tag-devops","tag-mlmodels","tag-mlops"],"acf":[],"yoast_head":"\n5 Major MLOps Challenges \u2013 and how to solve them | Equal Experts<\/title>\n<meta name=\"description\" content=\"What are the 5 biggest challenges of MLOps in 2022\u2013and how do organisations begin to solve them? 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