ML Ops

Data Management

Data Collection: Gathering relevant data from various sources, such as databases, APIs, or sensors.

Data Cleaning: Identifying and correcting errors, inconsistencies, or missing values in the data.

Model Deployment

Containerization: Package models in containers for easy deployment.

Integration with Production Systems: Integrating deployed models into existing applications or infrastructure.

Model Development

Model Training: Train models on labeled data to learn patterns.

Model Evaluation: Assessing model performance using metrics like accuracy, precision, recall, and F1-score.

Monitoring and Evaluation

Model Drift: Detect model drift to ensure ongoing accuracy.

Anomaly Detection: Identifying unusual patterns or deviations in model predictions.

MLOps

MLOps, or Machine Learning Operations, is a set of practices that aim to streamline the entire lifecycle of machine learning models, from development to deployment and maintenance. It involves automating and standardizing various tasks, ensuring efficient collaboration between data scientists, engineers, and other stakeholders.

  1. Accelerated Time-to-Market: Streamline the process of getting machine learning models into production, enabling faster innovation.
  2. Improved Model Quality: Ensure that models are reliable, accurate, and perform as expected.
  3. Enhanced Collaboration: Facilitate collaboration between data scientists, engineers, and other stakeholders.
  4. Increased Efficiency: Automate repetitive tasks, reducing manual effort and improving productivity.
  5. Scalability: Enable models to handle increasing workloads and scale as needed.

We’re a team of AI/ML innovators who are passionate about cutting-edge ideas, empowering enterprises with intelligent solutions, and crafting data-driven, user-centric experiences

Contact Us

  • Address :  1645 Ladys Mantle Way
                       Prosper Texas
                       75078
  • Phone    :  +1 425.900.44777
  • General Enquiries : info@tekhorizon.ai