ML Engineering Services

Operationalize machine learning lifecycle faster and maximize the impact of advanced analytics

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Build, train and deploy ML models at scale

Extracting maximum ROI from machine learning models remains a major challenge for companies, as more than 50% of models fail to reach production owing to silos complicating ML model deployment. Sigmoid’s MLOps practice combines data science, data engineering, and dataops expertise to build effective AI strategies and deliver business value. 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 lifecycle management and overcoming model drift challenges.

100+

ML pipelines maintained

200+

ML models in production

99.9%

Uptime SLA of ML models

eBook

MLOps best practices to solve AI/ML production hurdles

Gartner states that on average, only 54% of AI projects make it from pilot to production. This is attributed to the impediments that technology and business leaders face in moving ML models to production. The eBook discusses MLOps best practices to overcome challenges of training, deploying and maintaining model accuracy at scale with a proven framework.

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Enhance ML model lifecycle management

Model Build

Expedite model training and testing, enable model repository and provision scalable infrastructure.

Model Deployment

Maximize AI initiatives, leverage open-source and cloud-based solutions, and deploy scalable MLOps frameworks.

Model Serving

Batch or real-time business insights for reports/dashboards and downstream systems.

Model Management

Detect model drift to ensure model accuracy and data drift, and manage model degradation.

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Sigmoid’s MLOps framework

Customer success stories

90% improvement in the pricing and promotions ML model runtime for a top hygiene company

  • Reduction in model run time from 8 days to just 14 hours
  • 87% reduction in cost per run
  • Easy and scalable migration of ML models across geographies
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Automated ML pipelines for 1:1 email marketing for a QSR chain

  • Over 100 MN personalized emails sent on a fortnightly basis
  • 12 MN customer base
  • Delivered 8% sales uplift
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Our other offerings in Data Engineering

Data Pipelines

Automated data pipeline solutions to generate insights faster and make smarter business decisions.

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Cloud Transformation

Modernization, migration, and optimization of cloud performance with agility and reliability for optimal data usage.

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DataOps

Managed services to help you automate end-to-end enterprise data infrastructures for agility, high availability, better monitoring, and support.

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Insights and perspectives

Blog
Top 5 model training and validation challenges that can be addressed with MLOps
Infographic
Productionizing ML models at scale
eBook
MLOps strategies for scaling enterprise AI initiatives

FAQs

A Gartner research shows that 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.

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.

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.

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.

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.

Automate and scale your AI strategy for greater impact.

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