Breaking Down Barriers in Medical Diagnosis: The Power of Deep Learning
When it comes to cancer, discoloration, and other complex medical conditions, the survival rates are often dismal. Patients with advanced cancer have a survival rate of less than 20%, while those with early cancer can be cured with a success rate of up to 90%. However, the early stages of cancer are often insidious, making diagnosis a challenging task. The existing medical standards, coupled with inconsistent diagnoses and a lack of information, make it difficult for patients to receive timely and accurate treatment.
The limitations of human power have led to the emergence of artificial intelligence (AI) and its role in medical diagnosis. The power of science and technology has enabled the development of complex 3D medical imaging deep learning models, which can improve patient diagnosis and treatment outcomes. This has, in turn, underscored the importance of deep learning in accelerating and simplifying the development and deployment of AI solutions.
The Inevitable Choice: Embracing AI
As the saying goes, “life-changing technology” can solve even the most intractable problems. In recent years, AI has been rising rapidly, driven by the success of deep learning. The vast amounts of training data and strong performance computing architecture have made deep learning an area of major focus for companies and even human society. With its ability to solve practical problems and create endless possibilities for new businesses, deep learning has become the most popular technology in AI.
More and more enterprises are entering the deep learning space, especially those that have invested heavily in big data. These companies have witnessed the mass data processing and integration of computing power and large storage capacity required for these data, which implies a number of areas ranging from healthcare, manufacturing, and financial services. The era of big data has presented an opportunity for companies to seize the pulse of the times and build innovative applications and services using AI.
The Road to AI: Full of Thorns and Challenges
However, entering the AI space is not an easy task for businesses. The vast amounts of data require proper handling, which can become a burden on business if not managed correctly. Many companies have thought of using AI technology to provide new data use, but they have had to face many difficulties and challenges, especially those with local infrastructure or using a hybrid cloud model.
To overcome these challenges, businesses need to research, select, deploy, and optimize infrastructure to promote efficient use of resources and expand as needed to meet changing business requirements. They also need to deploy AI programs in a simple way, and when deploying AI programs, many enterprises lack sufficient internal expertise and infrastructure, especially for understanding deep learning and deployment. To know the depth of learning deployment in a production environment is not only time-consuming and complicated.
The Challenge of Data Management
Artificial intelligence, data management plan is also a challenge, difficult for enterprises to extract value from the “Data swamp” because the data analysis process is complex and requires a lot of resources from local move to the cloud, many difficulties and challenges, put in a these front companies urgently need the help of artificial intelligence technology to data “turning waste into treasure”, then how should they meet this challenge?
From Challenges to Opportunities
In the face of mass data storage problems, Apache Spark provides large data storage and computing standardization of scalable, help solve the difficulties depth learning, data and expertise, and its scalability allows adding hundreds of nodes without degrading performance, without changing the infrastructure. The depth of distributed learning library BigDL is the icing on the cake, to strengthen again Apache Spark of storage and computing power, providing efficient, scalable, and optimized deep learning development.
BigDL provides new deep learning model training and development services for the same data on a large cluster. It also supports TensorFlow, Keras, and other models from other frameworks. For BigDL running on Apache Spark selection of Intel solution, then again accelerate and simplify the development and deployment of deep learning. The solution will be scalable Intel Xeon processors, Intel SSD and Intel Ethernet network adapters combine to help companies quickly take advantage of reliable and comprehensive solutions.
A Scalable and Optimized Solution
Through scalable storage and computing, the solution is ready to meet the future needs of machine learning (ML) / DL infrastructure investment. It provides excellent total cost of ownership (TCO) through multi-purpose hardware, IT organizations have become accustomed to manage the solutions tried and tested and simplify deployment. Accelerate time to market through a one-stop solution, the solution contains a rich set of development tools, and optimized for critical software library.
For the data storage solution that can run analysis, BigDL running on Apache Spark selection of Intel solutions help companies overcome the challenges, and thus more quickly and easily achieve its artificial intelligence program. With this solution through pre-testing and tuning, companies and individuals do not need to study and optimize the infrastructure manually, it can effectively implement its artificial intelligence work.
Not only reduces deployment and management of artificial intelligence infrastructure requirements for in-house expertise, it also can help IT organizations improve infrastructure utilization, while ensuring scalability to meet the growing needs of the company. Big Data era, the wave of AI hit, some started his company in the field, you want to enjoy the dividends wave technology, the key is an easy to use deep learning framework, based on this depth can quickly develop their own learning applications, in order to benefit from it.
A Subversive Solution
BigDL Intel’s selection of homeopathic solutions come not only greatly reduce the cost of learning AI, but also to help enterprise customers succeed and relaxed door into artificial intelligence. The future, we believe Intel can create a more subversive solutions, leading companies continue to depth learning artificial intelligence technology on behalf of, at the same time to create more value play value, help in different areas, but also do for human society more contributions.