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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.
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.
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.
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 014 hpurs
- 87% reduction in cost per run
- Easy and scalable migration of ML models across geographies
Automated ML pipelines for 1:1 email marketing for a QSR chain
- 50% faster data collection and enrichment
- 2.5x faster time to insights for marketing team
- Automatically ingested data from 30+ sources
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.
Explore more >
DataOps
Managed services to help you automate end-to-end enterprise data infrastructures for agility, high availability, better monitoring, and support.
Explore more >Insights and perspectives
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FAQs
Why do most ML projects fail to move from PoC to production?
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.
What challenges can ML Engineering help overcome when deploying ML models and how?
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.
Which metrics do you use to measure the success of the model?
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.
What are the benefits of building a custom MLOps solution over using an MLOps platform?
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.
What is model drift in machine learning?
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.
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