MLOps Solutions

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 also focuses on model optimization, ensuring that new features can be added quickly. 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 ML 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.

Machine learning applications unlock a plethora of fresh prospects for your business. Leveraging machine learning models and effective model selection enables you to tailor the customer experience, streamline operations through automation, access deeper insights via advanced analytics, and introduce digital innovations that revolutionize customer interactions with your offerings.

The adoption of machine learning has a profound impact on business challenges, trimming expenses and amplifying customer satisfaction. The application of Machine learning algorithms can be used for every domain or sector – ranging from eCommerce to finance, healthcare to education, and manufacturing to oil and gas industry.