We build, train, optimize and deploy
Machine Learning Models for Your Business
Social Media Analytics
Are you looking for a way to track your brand’s social media performance? Are you curious about what people are saying about your company online?
Remove Class Imbalance Without adding Bias
The most challenging supervised learning problems are the ones with high-class imbalance. Learn why SMOTE does not work.
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.
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.
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
Healthcare
The HL7 FHIR standard is a great way to exchange healthcare information between different systems, but there are a few potential problems that could arise.
W6H Viewpoint Concerns
Building a System?
Auto Generate Viewpoint-Concerns of Enterprise Stakeholders using Natural Language Generation
ETL automation
We use ML to automate and scale ETL (Extract, Transform, and Load) processes.
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 roadmaps
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
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.
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
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.
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.
Not ready to start
just yet?
Then maybe you would like to learn some more about what we can offer you.
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.
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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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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.
98%
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.
Satisfied Customer
Faster growth starts with MLSoft
Our technology stack includes
