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MLOps – An Overview

– xpresso Team

“Enterprises do want to leverage ML, but most don’t have all the necessary competencies. To achieve ML at scale, enterprises must achieve MLOps capabilities,” says a Forrester study[1]. In fact, 98% of respondents believe ML adoption would keep them competitive, 53% believe it would mean higher profitability, and 49% believe it would increase revenue growth.

MLOps, which is basically DevOps for Machine Learning projects, brings together the best of both worlds — software development and Data Science. This enables addressing complex problems through automation that streamlines creating end-to-end ML projects and includes integration, testing, releasing, deploying to production[JC7]. Data Scientists, thus, are empowered to focus on preparing models and do not need to spend time on the nitty-gritties of the entire MLOps process

Deploying ML[KR8] [SG9]  models into production is challenging because accurate data and adequate model training are often unavailable. Tools to identify patterns and train models are not scalable; data quality and versioning may be insufficient, and deploying existing models can cause panic and flawed results. Every time datasets, derived data and models change, architecting a new model from raw data amplifies the overhead. This also happens when experiments need to be repeated for accuracy.

From obtaining data to model deployment, MLOps — broadly, operates in a landscape that comprises six stages:

  1. Data Management — includes identifying use cases and data required, data collection, correction, exploratory analysis, versioning, and preparation.
  2. AutoML — broadly deals with enabling off-the-shelf capabilities for almost anyone to deal with complex methods involved in ML projects, typically, running algorithms. Results achieved can be ranked and the best-fit can be picked.
  3. Model Building Process/Experimentation — involves critical enterprise data and the iterative nature of the model building and experimentation using various parameters and datasets. In the enterprise space, efficient results are only achievable if models and experiments can be easily located, training data is highly traceable and versioned correctly, and experiments are reproducible with exact conditions.
  4. Explainability — addresses the why and how that is involved in a model’s predictions. The higher the interpretability of a model, the easier it is for a human or an organization to comprehend the causes, decisions, and outcomes while addressing a complex real-world problem. A model with higher explainability that is chosen over another can yield more acceptable results.
  5. Deployment — is about efficiently getting a model into a production environment.  This assists in data-driven business decisions while ensuring[JC18] [SG19]  high-performance, reliability, and scalability. Prediction frequency, applications that need access to models, and latency requirements of these applications are some key considerations while deciding the model’s deployment.
  6. Monitoring — involves close supervision of a model’s outcome and employing the right data. Post-deployment, a model built from certain datasets can provide accurate predictions only if it utilizes accurate and current data, which makes monitoring absolutely crucial. By keeping a close watch on the outcome, the frequency of retraining models and the need for obtaining better data can be established, which in turn leads to higher accuracy.

The enterprise MLOps journey articulated by starts off by ensuring a robust data intake and elaborate analysis to frame the problem areas. An engineering mindset evolved over decades of collective experience in developing real-world ML applications is then applied to the problem; enablers required for AI initiatives to be successful at enterprise scale are identified, and cognitive models are prepared. Finally, these models are deployed and managed throughout the lifecycle of the solution. Throughout the lifecycle, pre-built frameworks are used to push the required transformations ahead.

Coupled with a robust uptime for applications we develop, ability to integrate with different tools and platforms, complete safety of critical enterprise data, an intuitive user interface that enables complex operations available with minimal clicks, and near-zero chances of interruption means unwavering support for our customers.  So, what would have typically taken months to deliver can be developed and deployed in weeks, and most importantly — at a fraction of the cost.


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