Machine Learning Pipeline once Deployed in Production

We work with our clients to address these challenges in operationalizing machine learning using MLOPs solution frameworks.

One of the biggest challenges that organizations are facing with AI and machine learning is moving beyond the development and deployment phase to operationalize or automate their machine learning pipelines in production.

Our AI Engineering Approach

Based on our clients’ priorities we focus on the following aspects of operationalization 

Machine Learning Pipeline Automation

Machine Learning Pipeline Automation

Model Cataloguing for Shareability and Reusability

Model Cataloguing for Shareability and Reusability

Model Governance

Model
Governance

Faster Deployments to Production

Faster Deployments to Production

Automate Training and Testing

Automate Training and Testing

Security and Life Cycle Management

Security and Life Cycle Management

We have not only helped developed frameworks and solution accelerators around these challenges for our clients but also helped them implement these solutions on platforms of their choice. We have in-depth knowledge of different automation frameworks and are aware of the strength and weaknesses of each platform. We can guide you to choose the right automation solution for your needs. We also train our clients in managing and refining automation throughout the data and AI lifecycle.

Our Expertise

At Eclipsys, we specialize in the AI Engineering, MLOPs or AutoML space includes tools like Azure Machine Learning, Google AI Platform, AWS Sagemaker and Databricks.

AI Engineering

FREE AI ENGINEEING/MLOPS STORYBOOK DOWNLOAD

With the help of a short story we illustrate how we helped a Toronto based entertainment organization automate their machine learning cycle using the latest cloud technology solutions.

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