We make your
AI explainable

Explainable AI models provide insights into how the model works and why it makes the predictions. This can help users and regulators trust the model and make better decisions based on its predictions. Artificial Intelligence (AI) is rapidly evolving and growing more sophisticated every day. With this increase in power and capability comes an increased responsibility to ensure that AI is ethically sound.

Why Choose
MLSoft?

We help you make best sense of your data

Monetize your data and get leading edge on your competitors

We help you develop AI/ML strategy for your business and create roadmap

Our MLOPS pipeline saves you time and effort to deploy AI/ML solutions

Get your AI/ML solution health checked by our experts

Let our experts build your MLOPS practice to keep the bar high

Verticals

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%.

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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.

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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.

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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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Healthcare

The healthcare community and CMS are exploring a potential transition to FHIR-based quality measurement beginning with research and testing. Currently used quality standards, Quality Data Model (QDM), Clinical Quality Language (CQL), Health Quality Measure Format (HQMF), and Quality Reporting Document Architecture (QRDA), remain the backbone of electronic clinical quality measure (eCQM) development and reporting.

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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 test sets and the generalization methods/techniques used.

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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 to 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

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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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 that improve the 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.

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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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Machine Learning
Model Deployment

We use CD/CI to deploy your machine learning models in the public or private cloud of your choice. Deploy your models in scalable cloud in a week.

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Average satisfaction rating in the past year .

24/7

Availability for our support team. Just a quick chat or email away.

135M+

Saved by clients in direct and indirect costs.

Faster growth starts with MLSoft

Our technology stack includes