AI/ML DevOps

We Build, Train, Optimize And Deploy Machine Learning Models
For Your Business

AI/ML DevOps​

We Build, Train, Optimize And Deploy Machine Learning Models
For Your Business

The Future Has Moved From Smart To Intelligent

With products and services rapidly adopting Artificial Intelligence and Machine Learning into their very core, consumers expect their updates and service upgrades to be at the same speed, too. If your DevOps backing futuristic products/services, built on emerging technologies such as AI and ML, does not offer well-oiled pipelines, your business runs a risk of a major operational setback.

Ariginal Insights Leading To AIOps Platforms

Our Products

Over two decades of experience allows Ariginal to offer comprehensive solutions to our customers and leverage the full potential of blockchain technology.

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.

Cloud Native Solutions

User demand for new features grows quicker than your development methods can keep up with. You’ll need a platform, techniques, application services, and tools that can keep up without forcing you to abandon your consumers’ favorite apps.
Cloud-native development combines well-known development best practices of continuous integration and deployment and enables strategic flexibility and agility to build and execute apps on any cloud. Enterprise middleware is also being modernized by businesses. A cloud-native platform is a cutting-edge app platform that may be used to replace aging and costly application servers.

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

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.

Why AI/ML DevOps ?

There’s more than one reason why contemporary solutions should look for investments in AI/ML DevOps. Some challenges include expansive datasets and changing end-customer dynamics and behavior. With AI/ML Ops, businesses can focus on delivering features faster without sacrificing continuity of operations. DevOps in an AI/ML environment includes training of ML models in an offline environment, which are eventually integrated into the live production systems. Benefits of Ariginal AI/ML DevOps are given below

Optimize Team Productivity



Improve Governance and Compliance