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Machine Learning Soft
We automate building, optimization, deployment and maintenance of Machine Learning Models for your business
Machine Learning Soft
We automate building, optimization, deployment and maintenance of Machine Learning Models for your business
Machine Learning Pipeline and MLOPS
Machine Learning model development is an iterative process; good model building depends on suitable preceding tasks which include: data cleansing, data balancing, data normalization, feature engineering, and hyper-parameters selection. The quality of models depends upon the hyper-parameter optimization and model performance (based on selected metrics) on validation and test sets and the generalization methods/techniques used. To improve model generalization performance, models need to be continuously trained on new data due to concept drift in the data. This warrants repeating the entire model development and evaluation process. Versioning the models and its parameters is part of MLOPS. At MLSoft we version the datasets as well without creating copies. Following are the essential elements in MLOPS pipeline, and at MLSoft we automate these
Machine Learning Pipeline and MLOPS
Machine Learning model development is an iterative process; good model building depends on suitable preceding tasks which include: data cleansing, data balancing, data normalization, feature engineering, and hyper-parameters selection. The quality of models depends upon the hyper-parameter optimization and model performance (based on selected metrics) on validation and test sets and the generalization methods/techniques used. To improve model generalization performance, models need to be continuously trained on new data due to concept drift in the data. This warrants repeating the entire model development and evaluation process. Versioning the models and its parameters is part of MLOPS. At MLSoft we version the datasets as well without creating copies. Following are the essential elements in MLOPS pipeline, and at MLSoft we automate these
Data Balancing and Normalization
The data normalization depends upon the task at hand; for example, we may mean center each feature and, at some other times, distributes ranges. But the balancing is not trivial; the up and down sampling methods like SMOTE and its variants add new bias in the data. We have invented new techniques which do task-specific balancing to achieve highly generalized models.
Feature Engineering
Feature engineering plays a vital role in most machine learning tasks, even for deep learning for image recognition, where the deep neural nets auto-learn features. Creating a feature engineering pipeline and versioning is essential for revising the models in the life cycle.
Data Balancing and Normalization
The data normalization depends upon the task at hand; for example, we may mean center each feature and, at some other times, distributes ranges. But the balancing is not trivial; the up and down sampling methods like SMOTE and its variants add new bias in the data. We have invented new techniques which do task-specific balancing to achieve highly generalized models.
Feature Engineering
Feature engineering plays a vital role in most machine learning tasks, even for deep learning for image recognition, where the deep neural nets auto-learn features. Creating a feature engineering pipeline and versioning is essential for revising the models in the life cycle.
Hyper-Parameter Selection
Hyper-parameters optimization is a field in itself, where methods ranging from Bayesian optimization to grid search are used, with no clear winner. We have created versioning systems of hyper-parameters to do continuous optimization.
Metrics and Measures
The selection of model evaluation metrics depends upon many factors of the problem domain. With the change in data or concept-drift, these measures need to be updated. We have created automated and semi-automated methods to optimize metrics which improve generalization of the models.
Hyper-Parameter Selection
Hyper-parameters optimization is a field in itself, where methods ranging from Bayesian optimization to grid search are used, with no clear winner. We have created versioning systems of hyper-parameters to do continuous optimization.
Metrics and Measures
The selection of model evaluation metrics depends upon many factors of the problem domain. With the change in data or concept-drift, these measures need to be updated. We have created automated and semi-automated methods to optimize metrics which improve generalization of the models.
Managed Machine Learning Ops
We create customized Continuous Delivery (CD), Continuous Integration (CI), Continuous Optimization (CO) and Continuous Training (CT) for your Machine Learning developments.
Managed Machine Learning Ops
We create customized Continuous Delivery (CD), Continuous Integration (CI), Continuous Optimization (CO) and Continuous Training (CT) for your Machine Learning developments.
Data Visualization
We create D3 visualizations of your data to reveal latent trends and serve as an exploratory step for your Machine Learning projects.
Join to create free visualizations for your data.
Machine Learning Model Development
We have over 75 years of combined machine learning models building experience. We use automated as well as in-house developed model parameters selection methods, data normalization methods, performance metrics and generalization techniques.
Data Visualization
We create D3 visualizations of your data to reveal latent trends and serve as an exploratory step for your Machine Learning projects.
Join to create free visualizations for your data.
Machine Learning Model Development
We have over 75 years of combined machine learning models building experience. We use automated as well as in-house developed model parameters selection methods, data normalization methods, performance metrics and generalization techniques.
Machine Learning Model Optimization
We use automated as well as inhouse-developed model parameters selection methods, data normalizations, performance metrics and generalization techniques.
We make sure your Machine Learning model perform the best on unforeseen data.
Machine Learning Model Deployment
We use CD/CI to deploy your machine learning models in public or private cloud of your choice.
Deploy your models in scalabel cloud in a week.
Machine Learning Model Optimization
We use automated as well as inhouse-developed model parameters selection methods, data normalizations, performance metrics and generalization techniques.
We make sure your Machine Learning model perform the best on unforeseen data.
Machine Learning Model Deployment
We use CD/CI to deploy your machine learning models in public or private cloud of your choice.
Deploy your models in scalabel cloud in a week.
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Average satisfaction rating in the past year
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Our support team is a quick chat or email away
135M+
Saved by clients in direct and indirect costs
Faster growth starts with MLSoft
Our technology stack
Let's Start Building
Would you like to starto a project with us?
We value our customers and consider them our partners in success, if you want to start a project with us feel free to contact us.