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Ganit MLOps Capabilities

Helping organizations compete on data & AI by focusing on consumption over creation

Last updated:
3 years ago
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Ganit’s Mastery in MLOps: Driving Efficiency and Innovation in Data and AI

Ganit, a profitable, pre-Series A funded company, is redefining the landscape of Machine Learning Operations (MLOps) with its innovative solutions. With a clientele spanning five geographies and a robust team of over 350 data scientists, engineers, and consultants, Ganit is at the forefront of driving digital transformation through data and AI.

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Ganit’s Mastery in MLOps: Driving Efficiency and Innovation in Data and AIOh hi there 👋It’s nice to meet you.Sign up to receive awesome content in your inbox, every week.

Ganit’s MLOps Expertise: Ganit’s proficiency in MLOps has delivered tremendous value across various industries. By leveraging industry-standard ML-Ops and big-data frameworks, Ganit has efficiently streamlined the deployment, monitoring, and maintenance of machine learning models in production environments.

  1. Faster Deployment: Automating deployment processes to accelerate the transition from development to production.
  2. Improved Collaboration: Providing standardized frameworks for development, testing, and deployment.
  3. Enhanced Reproducibility: Ensuring consistency in machine learning models through automated tracking of versions and components.
  4. Scalability: Facilitating integration with existing systems and infrastructure.
  5. Continuous Monitoring and Improvement: Enabling real-time performance monitoring and necessary corrective actions.

Impactful Projects and Solutions: Ganit has successfully implemented MLOps in various projects, including:

  • An end-to-end ML-Ops system for a pet retailer on GCP, Kubernetes, and Databricks, reducing model development effort by 17%.
  • A zero-touch order-placement solution for a grocery chain, leading to a 20% reduction in waste and 15% decrease in stockouts.
  • A forecasting solution for a CPG company, reducing time and effort required from data scientists by 85%.

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