Engineering

Machine Learning Operations (MLOps): Enabling Operationalization of ML at Scale

Machine Learning Operations, or MLOps, is the process by which operations teams employ machine learning models. MLOps automates and tracks the machine learning lifecycle and facilitates cross-team cooperation, resulting in significantly faster production and excellent results.

Challenges in MLOps

Artificial Intelligence (AI) and machine learning (ML) are now ubiquitous in the commercial world. Organizations are aware of these two technologies and the benefits they may reap if they implement them in their operations. Most companies fail to deliver AI-based applications because they are stuck transforming data science models and feature engineering logic.

Organizations place such a high value on building machine learning models that they overlook it. Some of the difficult issues are accessing data in production, connecting models with online business apps, monitoring model performance, and delivering continuous improvement. The data science and model development process is a crucial aspect of any application development that cannot, and we must not overlook.

One of the issues is that the data science team does not collaborate with the engineering and DevOps teams. They use manual development methods, which are only apt for production-ready machine learning pipelines, and increase the costs by requiring machine learning engineers, data engineers, developers, and more time and resources.

Developing models is only the first stage in a development pipeline. The most effort goes into making each part ready for production. It includes data collection, preparation, training, serving, monitoring, and enabling each piece to operate repeatedly with minimal user intervention.

MLOps Stages

MLOps combines AI and machine learning with DevOps practices for developing, integrating, and distributing machine learning programs. MLOps isn't about running code in the production environment, but creating an automated ML production environment that includes everything from data preparation to model deployment and governance. The following are a few commercial advantages of implementing MLOps:  

·      Increase team productivity

·      Developed products are reliable and reproducible

·      Faster production process

·      Improved model behavior and accuracy


Stage 0: Data Gathering and Preparation

Data is the most critical aspect of every machine learning program. Data from multiple sources structured appropriately allows for quick and easy analysis. Before an ML model can use data, it must go through a processing process. Here are a few reasons raw data is not suitable for ML algorithms:

·      Raw data has low quality

·      Data must translate into a form that algorithms can handle

·      To make data useful, aggregate it and make it organized

·      Data must be relevant to the application or model reference.

The machine learning method begins with minor data extractions and features engineering on that part of the data. ML teams typically work with large datasets to improve the accuracy of production models. ML teams make separate data pipelines for ETL, SQL queries, and batch analytics. Because more than 80% of data is unstructured today, creating an operational pipeline that turns unstructured data into machine learning-friendly is critical.

MLOps systems must include a feature store, which defines data collection and transformations for batch and real-time scenarios, processes feature without human interaction, and provides features from a shared catalog to training, serving, and data governance apps. Along with standard analytics, feature stores must be able to handle advanced transformations on unstructured data and complex layouts.

Stage 1: Pipeline for Model Development

When creating ML models, data scientists go through the following steps:

·      Manually extracting data from external sources

·      Data labeling, exploration, and enrichment to uncover patterns and characteristics

·      Model training and validation

·      Model evaluation and testing

·      Repeat the process until the desired result is reached

To achieve maximum accuracy, try experimenting with various settings and methods. Machine learning teams create pipelines that collect data, prepare it, select correct features, train models using different algorithms, evaluate models, conduct automated system checks, and log all executions and outcomes. Also, they allow for a rapid display of findings and comparisons with previous results to ensure that only relevant data is used for creating each model.

ML pipelines:

·      Use versioning of all data used throughout the pipeline

·      Store code and configuration in versioned repositories

·      Use continuous integration (CI) to automate the pipeline initiation, review, and approval process

·      Built using microservices

·      Have all their inputs and outputs tracked for every step in the pipeline

ML pipelines have various maturity stages:

Maturity Stage 0:

·      Simple spreadsheets to give an idea of the results

·      Algorithm testing to discover the solution

·      Fundamental data storage and encryption decision-making

·      The pipeline's infrastructure finalization

·      Development environment completion

 

Maturity Stage 1:

·      Model finalization

·      The collected data for creating alerts

·      Automation begins

·      Testing and debugging begins

·      Decisions on how to visualize the findings

·      Continuous integration and development  

·      This phase is completed in the staging environment

Maturity Stage 2:

·      Automated pipeline triggering

·      Model continuous delivery

·      Model Monitoring

·      Model retraining

Pipelines should be running over scalable services or functions that can span several servers or containers elastically. Jobs finish faster this way, and computed resources are available once it does, resulting in significant cost savings. The produced models, with metadata, performance metrics, needed parameters, statistical data, and other information, are maintained in a versioned model repository. Models are loaded into a batch or real-time microservices or functions later.

Stage 2: Developing Machine Learning Services for the Web

After creating an ML model, it must integrate with the business application. The entire application must be deployed without causing any service disruptions. The deployment will be difficult if machine learning components aren't considered an integrated part of the production pipeline.

Production pipelines consist of:

Real-time data collecting, validation, and engineering logic

API services

Model serving services

Feature logging services

Model monitoring services

Resource monitoring

All of these services rely on one another. Having a flexible mechanism for building the pipeline network is critical regarding deep learning, natural language processing, or model ensembles. Because production pipelines are connected by fast streaming or messaging protocols, they must be elastic to accommodate variations in traffic and demand and non-disruptive upgrades to one or more pipeline elements.

Production pipeline development and deployment flow are as follows:


Stage 3: Regular Monitoring, Governance, and Retraining

AI and ML have become an integral part of any business. ML teams must include data, code, and experiment tracking; monitor data for quality issues; check models for concept drift, and increase model accuracy with Auto ML approaches and ensembles, among other things. ML teams must react fast to continually changing patterns in real-world data. Monitoring machine learning models is essential to MLOps since it guarantees that the models are accurate and provide long-term benefits.

 

 

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