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Machine Learning Models for Your Business

Products

Cloud Cost Optimization

It is very easy to provision cloud resources, few clicks or few lines of terraform code can deploy cloud resources, but it becomes very difficult to rightsize the cloud resources once provisioned.

Our cloud cost optimization engine uses a powerful recommender system built on accurate forecasts of historical utilization to reduce multi-cloud costs by up to 50%.

Remove Class Imbalance Without adding Bias

The most challenging supervised learning problems are the ones with high-class imbalance. Synthetic minority over-sampling technique (SMOTE) and its variants are the most prevalent methods to remove class imbalance, but these introduce bias - making the learning models of low quality. Our class balancing mechanism outperforms all classifiers built with SMOTE. Use our Imbalance remover as a Service to improve the accuracy of classifier x fold.

Anti Money Laundering

Anti money laundering (AML) is a classic example of class imbalance, with millions of non-fraud transactions and a single fraudulent transaction. Any machine learning system built should take care of the class imbalance. But the traditional methods either do not work and produce over 99% false positives or add bias by lowering the false negatives. Our AML solution tackles this problem by reducing the false-positive rate and improving the false-negative rate.

Cloud Security

With more and more enterprise data moving to the cloud, which in turn attracts applications and traffic to the cloud. This accumulation of data in the cloud and its gravity results in an enormous volume of traffic, which poses new computational challenges to develop anomaly detection systems using machine learning (ML) and artificial intelligence. We tackle cloud security differently. Contact us to learn more.

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Machine learning pipeline and MLOPS Development

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 testsets and the generalization methods/techniques used. To imporove model generalization performance, modles need to be continuously trained on new data due to concept drift in the data. This warrents repeating the entire model development and evaluation process. Versionning 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:

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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.

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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..

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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.

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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.

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Ready to start building?

Let’s get your project moving

We value our customers and consider them our partners in success. If you are ready to get your project moving, get in touch and we will find the best solution for you.

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Not ready to start just yet?

Then maybe you would like to learn some more about what we can offer you.

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ML Solutions Development

Cloud-native systems are built using a microservices architecture, which enables them to be more scalable and resilient than traditional monolithic applications. Cloud-native machine learning solutions offer a number of advantages over traditional on-premises solutions. They're typically much easier to set up and use, and they can be scaled up or down as needed to meet the changing needs of your organization. Developing cloud-native systems requires a different approach than traditional application development. The most important difference is that cloud-native applications are designed to be deployed in a distributed, highly-scalable environment.

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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.

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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.

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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.

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