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MLOps- A KEY LEVER IN REVOLUTIONIZING AI/ML ADOPTION FOR INDUSTRIES

March 15, 2022 3252 0 AI

MLOps- A KEY LEVER IN REVOLUTIONIZING AI/ML ADOPTION FOR INDUSTRIES

Artificial Intelligence and Machine Learning are being counted among the top technologies that are driving digital business transformation as the world slowly but surely emerges out of the Global Pandemic situation. The impetus to move to the cloud and the growing number of ML models which saw phenomenal growth during the Pandemic, is expected to sustain over a period going forward. However, while operationalizing Artificial Intelligence, it has been found that merely 27% of the projects piloted by organizations successfully move to production. This anomaly arising out of challenges related to model development, iteration, deployment and monitoring need to be addressed if AI/ML has to revolutionize the global business landscape. Organizations that have already started their journey to operationalize AI/ML or are developing Proofs of Concept (PoC) can pre-empt some of the challenges by proactively integrating best-practices in MLOps to ensure the smooth development of models and address issues of scalability. This compendium compiled by NASSCOM in association with Genpact and EY encapsulates the best practices to guide organizations on effective set up, management, and scaling of ML operations.

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Key Highlights:

  1. What is MLOps?  
    1. MLOps is a set of practices and methodology that helps in automating ML model development, achieve automated and reliable ML model deployment, consistent model training, model monitoring, rapid experimentation, reproducible models, and accelerated models
    2. It typically starts with Data Engineering, followed by Algorithm Development, Model Deployment, Model Monitoring and Model Hpercare
    3. It has four main pillars – Model Lifecycle Management, Model Versioning & Variation, Model Monitoring & Management and Model Governance
  1. Need for MLOps?
    1. Just as DevOps came in around 2007-2008 to make SDLC more streamlined and compliant, MLOps holds the promise of ensuring smooth deployment of ML solutions
    2. MLOps combines the best of automation, IT operations and management and Continuous Development & Continuous Integration (CI/CD) in Machine Learning and Artificial Intelligence
    3. Helps to industrialize ML Models and thrive towards a DevOps culture
    4. Addresses key challenges around data quality, model decay, and data locality
    5. Helps to pivot from merely developing Data Science capabilities towards moving the model to production
    6. The rise of AutoML tools and platforms have started to democratize data mining and decision sciences
  1. What are the benefits of MLOps?
    1. Reduced time-to-market for ML products
    2. Improved RoI on AI/ML initiatives
    3. Advanced Data Management
    4. Speedier innovation with ML-driven products
    5. Improved transparency and model governance

 

  1. Dimensions of MLOps – 6 Key Pillars: The compendium brings out 6 key pillars of MLOps best-practices divided in to two broad categories. Each of these pillars have been discussed in detail in the handbook
    1. Implementation Pillar which includes Data, Training Model and Deployment
    2. Business & Operations Pillar which includes Innovation and Future, Control & Governance, and Investment & Change Management
  1. A four-stage MLOps implementation framework has been proposed with automated ML platforms, data pipelines, continuous model monitoring, automated deployment, and reorganized team structures
    1. Define & Design – defining project charter, understanding the business problem and the context, design architecture, and data pipeline design
    2. Data pre-processing – Collecting data sources, configuring data ingestion, data transformation and creating data storage
    3. Model Development – Model training and model validation, model testing and model packaging
    4. Model Deployment & Monitoring – The final stage of an ML project involves three steps; model serving, model performance monitoring and model performance logging
  1. ML Model Management and Model Testing are critical components while operationalizing MLOps
  1. The Future: MLOps promises standardization of processes and methodologies and is expected to boost efficiencies in terms of cost, quality, and time to value. Regardless of the industry or use-case, MLOps processes are the common thread that enables data teams (and more broadly, entire companies) to scale. Going forward, the compendium predicts that companies will eventually pivot to centralized ML Operations as against the current decentralized model. However, the caveat is that MLOps is still unchartered territory for a lot of companies. To derive value from Artificial Intelligence, it is important to make AI work at scale with MLOps.

 

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