Fuse the ML Development and Deployment Gaps With our Machine Learning Services
Implementing ML has many obstacles to overcome such as time-to-deployment, dependency on the technical team, and many other. MLOps helps you make the most out of machine learning by allowing seamless processing of data, deployment of ML pipeline, training of developed ML model, monitoring and scaling of the model by bridging development and operations.
SoftmaxAI, machine learning development company India, largely focuses on developing AI-based solutions and a greater deal of it encompasses around MLOps. Over time, we have been well-known custom machine learning solutions provider that has helped diverse enterprises streamline their machine-learning model implementation with our custom machine learning app development services.
Our ML Deployment Strategy
Integrating the team is vital for the effective deployment of the model. The resources required need to be determined beforehand depending on the scope of the project.
As a machine learning development company, we ensure your business data keep flowing from all the sources in the pipeline to make your process smoother.
Our ML Practices
CI (Continuous Integration)
By integrating testing and validating data and models, CI expands the relevant code and components.
CT (Continuous Training)
It is a special feature of machine learning systems that automatically and continually trains ML models for re-deployment.
CD (Continuous Delivery)
It focuses on delivering a machine learning training pipeline that instantly launches a second ML model prediction service.
CM (Continuous Monitoring)
It is associated with the monitoring of operational data as well as model effectiveness indicators that are linked to company metrics.
Our Machine Learning capabilities
Machine Learning has benefitted numerous businesses and that has led to a surge in its demand
AI Infrastructure Setup
We offer cloud-based infrastructure solutions for ML model deployment. Our data science consultants are experts at building integrated ML pipelines that include data extraction, processing, model training, model deployment, and fine-tuning.
You can automate and optimize procedures throughout the ML lifecycle by using the MLOps tools that Amazon SageMaker offers. To boost the efficiency of data professionals and ML professionals while maintaining prediction accuracy in production, you can quickly train, analyze, debug, implement, and control ML models at scale with SageMaker MLOps tools.
Want a custom machine learning solutions model? Google AutoML has the right tools to extend the capabilities of your ML model with easy customization that mitigates your business needs. It provides the capability to automatically train models on visual (pictures and videos), textual, and structured data as part of Vertex AI, Google's unified ML platform.
Azure Machine Learning facilitates fast development, deployment and management of superior ML models by data scientists and developers. It greatly accelerates time to value with top-notch machine learning operations (MLOps), open-source interoperability, and high-end integrated tools.
Technology we use
Data scientists and developers may deploy machine learning models straight into a hosted environment that is ready for use owing to the machine learning service known as Amazon Sagemaker.
The machine learning toolkit built on top of Kubernetes is called Kubeflow. It gives your ML pipelines, frameworks, notebooks, and libraries a cloud-native interface while translating different steps in the developed data science workflow into Kubernetes actions.
Data engineers utilize Apache Airflow, an open-source application for building, scheduling, and tracking processes, to coordinate processes or pipelines. It helps them to quickly understand the dependencies, developments, code, activities, and performance levels of their data pipelines.
Large data sets can be processed quickly using Apache Spark, a framework for data processing. It can operate alone or spread out data processing across several machines.
A central model registry, testing, production, installation, and the management of the entire machine learning ecosystem are all managed by the open-source platform MLflow.
It is a free and open-source Python framework for writing modular, reusable data science code.
An open-source Python tool called AutoKeras was developed in the Keras deep learning framework. An adaptation of Neural Architecture Search's most recent and effective version, ENAS, is used by AutoKeras.
They have an impressive commitment to making the product effective and simple to manage, as well as a willingness to consider new ideas.