Businesses that are hesitant to implement AI are at a disadvantage. Machine learning and algorithms are changing the world. From business automation to enhanced customer support, AI’s presence is inevitable. AI is the big leap into the future.
It’s time to accelerate your AI implementations
From start-ups to large enterprises, companies of all sizes are exploring the latest AI solutions to solve real-world business challenges. Both product and service-based companies have discerned the importance of AI in their business journey. It is also evident that there is a tremendous surge in the adoption of AI and Machine Learning (ML) over the last few years. As AI advancements continue to evolve, enterprises look at this future-drive technology as the key to unlock new business opportunities.
Despite the fact that AI implementations are growing in numbers, several product-based companies today are having a rough time when it comes to building AI applications. There are many factors that contribute to building scalable, efficient, and robust AI applications. Hence, enterprises must have a clear roadmap before setting-up their AI app development journey. Given the advantage of being in a digital age, there are various tools and platforms that can be leveraged to build AI applications. Still, it is important to choose the right solution that could help solve your AI app development challenges. Some of the key factors that need to be analyzed while choosing your AI solution are;
- Computing power and speed
- Application build time
- Connectivity and scalability
- Integration with the existing system
- Flexibility and efficiency
- Data privacy and security
- Cost factors and complexity
- Deployment and maintenance
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The product-based companies, in general, do not have the privilege of longer development timeframes like the service-based. Hence, it’s critically important for the product companies to choose a scalable AI framework that allows for an easy and quick build. As businesses continue to grow, the scope for enhancements and future integrations will always be an integral part of the applications. Thus, the need for a flexible and highly-efficient framework must top your priority list for a successful AI app development.
Taking in to account the diverse factors that must be considered while choosing your AI solution, we suggest Microsoft Azure Machine Learning as the right-fit solution to build your AI app.
Let’s take a deeper look at what Azure Machine Learning has to offer.
Microsoft Azure Machine Learning for AI app development
Microsoft Azure Machine Learning offers enterprise-grade machine learning services to build and deploy AI-based models quickly. Typically, a cloud-based service model that enables enterprises to build AI applications on a predictive analysis model. With the aid of Microsoft Azure Machine Learning, it is easy for enterprises to accelerate the end-to-end machine learning lifecycle.
Microsoft Azure empowers enterprises with advanced machine learning capabilities to easily build, train, and deploy models using Azure Machine Learning, Azure Databricks, and ONNX Runtime.
- Build Quickly
- Deploy Anywhere
- Manage Effortlessly
Why is Azure Machine Learning the perfect toolkit for product companies?
The latest advancements in Azure AI solutions enable enterprises to develop AI models faster with the aid of automated machine learning. The use of Azure Machine Learning eliminates the need to build AI models from scratch and save a lot of time and effort. In addition to this, enterprises also needn’t necessarily be aware of the multiple algorithms or optimization techniques to build their AI applications; you have it all preloaded in Azure ML.
Accelerated end-to-end machine learning lifecycle
One of the significant reasons why several product companies stick to Azure ML to build their AI apps is because of their accelerated development cycle. The Azure ML capabilities that include built-in notebooks, automated machine learning enhancements, and machine learning operations (MLOps) will offer you a fast-paced deployment model. The wide range of automated tools and capabilities will help minimize the work risk, lower costs, and save a lot of time. Azure ML’s automated machine learning capabilities will also take care of the intensive feature engineering, algorithm selection, and hyper parameter tuning. This will allow you to build and deploy your AI-based models quickly.
Several product-based companies like Schneider Electric and Lexmark are leveraging the latest Microsoft Machine Learning services to identify the right models for predictive maintenance and make more informed business decisions.
Microsoft Azure ML lets you build and deploy your AI-based models by leveraging your favorite open-source frameworks. Azure ML’s interoperable ecosystem will let you run your AI models anywhere from the cloud to the edge without getting locked in on a specific framework. The popular machine learning frameworks supported by Azure ML are PyTorch, TensorFlow, scikit-learn, MXNet, Chainer, and Keras.
- Switch between frameworks with ease
- Run your AI models efficiently across platforms
- Simple deployment from cloud to the edge
Automated Machine Learning
With the aid of Automated ML, you can easily build highly accurate machine learning models with speed and scale. You can also automate all the time-consuming and iterative tasks of model development with the aid of breakthrough research; it lets you build and deploy predictive models with no-code UI or SDK.
- The time series and deep learning models will aid in forecasting your future business outcomes accurately.
- Allows for easy customization of models and provides complete control over the model building process.
- Supports diverse machine learning tasks
Scale with MLOps
Microsoft Azure Machine Learning offers end-to-end MLOps capabilities to integrate with the current DevOps process and handle the ML lifecycle. Offers efficient workflows and advanced capabilities to design, deploy, and manage model workflows with complete transparency. MLOps enable your IT teams to easily collaborate and accelerate the development and deployment of AI models.
One of the most critical factors that need stringent evaluation while choosing your AI solution is security and governance. The multi-layered, built-in security features of Microsoft Azure will protect your resources from the rapidly evolving threats. Azure ML’s security capabilities – granular role-based access, identity authentication, and network isolation will ensure complete security to your resources and AI models.
A comprehensive analysis of Microsoft Azure Machine Learning capabilities will offer plentiful reasons to choose this intelligent solution to build your AI apps. In addition to the capabilities briefed in this blog, there are several more tools and solutions offered by Azure ML that will let you build and deploy AI models quickly.
Build Scalable and Intelligent Solutions
When it comes to building scalable and intelligent AI applications, the two pivotal choices that would largely determine your AI implementation success are choosing the right-fit AI solution and implementation partner.
Considering the capabilities and benefits of Microsoft Azure Machine Learning, there would hardly be a debate in choosing your AI solution. But, when it comes to your AI implementation partner, several factors might come into play. So, make the best choice!
Being a Microsoft certified partner, our longstanding years of experience in Azure AI services will aid you in building scalable and intelligent AI applications. Our Azure expert team will help you identify the right business areas that can be tapped for your AI implementation. This process will provide you better clarity on your operational model and the key areas for business enhancement. Talk to our Azure AI experts and get to know how our AI implementation services could add value to your business.