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AI/ML DevOps and Cloud Services AI/ML DevOps and Cloud Services

AI/ML Services

From Data Insights to Deployed Models, Seamlessly.

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Services

Our AI/ML Services Built to Match Your Every Need

Our AI/ML Services support you in every step of your journey. We work with you to help you achieve your AI & ML goals.

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Data Strategy and Preparation

Capture, catalogue, integrate , prepare , Exploratory Data Analysis (EDA) , Feature engineering and visualization. We support you in each and every aspects.
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Model Training

Select suitable algorithms based on the problem type and data, defining parameters , training , hyperparameter tuning , model metrics and validation.
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Deploying Model

Develop deployment strategies for real-time or batch predictions , setting up production environment, choosing the right capacity , auto-scaling approach and deploy model.
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Monitor and Manage

Creating model and data quality baselines, setting up monitoring systems to track model performance metrics. Regular retraining and updating models to maintain relevance and performance.
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MLOps and Automation

Define and deploy MLOps covering the complete lifecycle including train, test, deploy , monitor and updates.

Clozon AI/ML Services

Supporting  you from EDA to Deployed Models. Seamlessly at lightning speed

Exploratory Data Analysis
Training and Tuning
Performance
Deployment
Monitor & Manage
MLOps
AIOps

Data Strategy and Preparation

Collecting data from one or more sources, cataloguing, integrating  and preparing for analysis .
Exploratory Data Analysis  : Discover  patterns, spot  anomalies , trends and feature correlation
Selecting the most relevant features. Ensure high quality input data.
Feature Engineering : Performing  one or more transformations like imputation , normalization ,etc
Develop a data pipeline to clean, preprocess, and transform raw data.
Visualization using various charts and graphs

Model Training and Tuning

Understanding business requirement.
Based on the problem type and data select suitable algorithms.
Algorithm can be decision trees, deep learning, ensemble methods ,etc
Fit the model to the training data using initial parameters
Hyperparameter tuning to improve model performance and accuracy

Evaluating Model Performance

Evaluate model to assess its performance like overfit, underfit , acuracy, etc.
Refine hyperparameters or algorithm settings based on validation results
Add or remove features based on their impact on model performance
Based on the business case, select the right tools and measurement creteria.
Metrics include model accuracy, precision, rmse , AUC and others.

Deploying Model

Develop deployment strategies for real-time or batch predictions
Setting up the right model production environment
Choosing the right capacity for hosting the model .
Scaling model infrastructure to handle high-demand situations using auto-scaling.
Containerizing ML models for flexible and scalable deployment.

Monitor and Manage

Creating model quality baselines and data quality baselines
Set up monitoring systems to track model performance metrics
Performance metrics like data drift, accuracy, latency , etc.
Ensure model performance metrics are within desired limits
Model optimization by regular retraining and update
Post deployment support to scale and improve models as required.

ML Ops – Automate ML lifecycle

MLOps involves automating train, test, deploy , monitoring and update steps.
Finalizing and setting up the MLOPS toolset covering each of the steps in the ML lifecycle.
Building a pipeline covering the series of steps to be executed
Integrating data sources , acess controls , test and rollout.
Scheduling runs and monitoring the status

AIOps to enhance your IT operations

We help you implement the right tools to leverage AI/ML for your IT operations.
Deploy tools for security operations, self healing, etc.
Implement real-time anomaly detection using AI ML.
Identification of hidden trends that signal potential issues.
Predictive analytics to forecast resource usage and avoid capacity bottlenecks.
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