Machine Learning Operationalization (MLOps)2022-05-25T10:24:56+00:00

MLOps

Get a faster MLOps solution to operationalize the machine learning
lifecycle and accelerate your workloads
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Deploy Machine Learning Models Faster

More than 50% of ML models fail to move from proof of concept to production, which remains a major machine learning challenge faced by companies. Various teams often work in silos leading to complexities in creating, managing, and deploying machine learning models.

Sigmoid’s MLOps practice provides the right mix of data science, data engineering, and DataOps expertise, required to operationalize and scale machine learning to deliver business value, and build an effective AI strategy. Our expertise in open-source and cloud technologies enables you to build custom ML solutions and maximize ROI. We help data-driven companies to accelerate time to business value for AI projects by 30% by strengthening ML model life cycle management and overcoming the challenges of model drift.

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ML pipelines maintained
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ML Models in production
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Uptime SLA of ML models

Speed Up Data Science Projects

Sigmoid enables businesses to efficiently build and manage machine learning projects throughout the development lifecycle. Our MLOps practice enables data-driven enterprises to consistently build, deploy and monitor ML models at scale.

Model Deployment

Model Deployment

  • Unlock the full potential of AI investments
  • Leverage open-source and cloud-based solutions
  • Deploy repeatable Machine learning Operations framework
Model Performance Monitoring icon

Model Performance Monitoring

  • Eliminate the last-mile hurdle of ML operational chaos
  • Fast-track the time to market of ML models
  • Monitor production and performance to take
    proactive measures
Model Accuracy and Automation icon

Model Accuracy and Automation

  • Detect Model drift
  • Build ML pipelines
  • Ensure model accuracy and data consistency
  • Focus on innovation over operational issues
End-to-End ML Model Lifecycle Management icon

End-to-End ML Model Lifecycle Management

  • Build and manage ML models
  • Improve ML model lifecycle management
  • Integrate MLOps end-to-end
  • Orchestrate and govern ML models through the
    entire lifecycle

Productionize ML Models At Scale

This webinar with experts from Sigmoid and Yum Brands looks
at the key technological and organizational challenges and best practices
to take models to production

Productionize ML Models At Scale

Since integrating Sigmoid’s real-time platform, we have better visibility into high-volume data sets and have been able to translate these insights into new business opportunities.

Itay Riemer
VP – Programmatic and Data,
Brand Solutions

Frequently Asked Questions

A Gartner research shows only 53% of projects make it from prototype to production and struggle to operationalize machine learning models. This is mainly because most businesses apply traditional software development lifecycle such as traditional databases or data warehouses to manage AI/ML models, from the application layer to the middleware and the infrastructure. Moreover, various stakeholders such as data scientists, IT operations, data engineering, line of business, and ML engineering teams often work in silos. This may result in complexities for creating, managing and deploying ML models. The delay in deployment leads to the failure of ML projects in the enterprise. Read more about taking ML models from PoC to production here.

The three main metrics used to evaluate a classification model are accuracy, precision, and recall. Sigmoid can validate the model quality by using an automated system to inspect before attempting to serve it. Our MLOps practice ensures that new features can be added quickly, as the faster a team can go from a feature idea to the feature running in production, the quicker it can improve the system and respond to external changes. Also, all the input feature code is tested as it’s crucial for correct behavior, so its continued quality is vital.

Model Drift (or model decay) is the degradation of an ML model’s predictive ability over time due to changing dynamics of the digital landscape and subsequent changes in variables such as concept and data. Model drift is prominent in ML models simply by the nature of the machine language model as a whole. Model drift can be broadly classified into two main types based on changes in variables or the predictors — Concept drift and Data Drift. Read more about model drift here.

Apart from the fact that taking a model from PoC to production is slow, there are several problems that companies may face. Some of them are:

  • Model drift and model versioning
  • Challenges with data changes and related model performance
  • No one knows which models exist and who is using them
  • Accurately recreating an ML model is highly complex

MLOps can help companies speed up the time to market ML models, scale ML models to different business units and geographies, ensure continuous production monitoring, build a repeatable framework for deploying and updating future ML models and empower different teams to orchestrate ML models during the entire lifecycle.

Building custom MLOps solution helps deliver bespoke and cost-effective results using the latest open-source and cloud technologies and aligns with your AI strategies and roadmap. Sigmoid provides managed services capabilities to eliminate the last-mile hurdle of ML operational chaos by working closely with the different teams of data scientists, data engineers, and DataOps.

Quick Start

We would be pleased to advise you on your Machine Learning Challenge

Overcome Machine Learning Challenges With Our MLOPs Framework

From identifying the business requirement to training ML models and maintaining them, MLOps services by Sigmoid brings together the best talent in Data Engineering, DataOps, and Machine Learning across the entire lifecycle to drive analytics success.

  • Setting up the right data lake and enabling scalable computing resources from the start
  • Choosing a Tech stack that ensures interoperability in modeling techniques and model Re-trainability
  • Adopting the right coding practices and following the right integration approach
  • Monitoring the model performance, carrying out periodic health checks, and automating model updates to eliminate model drift
MLOPs Framework
Productionizing the Digital Demand Forecasting ML Models

Productionizing the Digital Demand Forecasting
ML Models

Created a scalable and production-ready end-to-end system that covered new products, categories and countries for a leading CPG Cosmetics firm

Automation of ML data pipelines using Spark

Automation of ML data pipelines using Spark

Sigmoid productionized the MAB model for a major restaurant chain that delivered tailored emails to over 12Mn customers resulting in 8% sales uplift

E-Book: MLOps Best Practices to Solve AI/ML Production Hurdles

ML Production

Taking ML models from PoC to production with MLOps

Gartner predicts that through 2022, only 20% of analytic insights will deliver business outcomes. This is attributed to the impediments that technology and business leaders face in moving ML models to production. The eBook discusses how to effectively approach MLOps using tried and tested methods.

Learn more about the data engineering services

data warehousing

ETL and Data Warehousing

Productionize ML Models

Cloud Migration

data devops

DevOps

Want to know more about our Data Engineering service offerings?

We help Fortune 500 companies leverage data to drive decision-making. Our solutions in ETL, Data Pipelines and Cloud Data warehouse have helped them modernize their data infrastructure to enable enterprise-wide data & analytics at scale.

Let’s discuss how you can leverage our Data Engineering services to build efficient operations. Rest assured, it’s an absolute non-binding initial consultation with our Machine Learning Experts.

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