Why Learn Machine Learning Course???. We noticed a dramatic rise in the platforms, tools, and applications based on Machine learning and artificial intelligence in the time of 2018. These technologies influenced the software and Internet industry and in the same way explore the other domains such as healthcare, manufacturing, automobile, and agriculture.
Now, we will carry on with the advancement of Machine Learning and AI-related technologies in 2019 and beyond. The Tech Giants such as Amazon, Apple, Facebook, Google, IBM, and Microsoft are investing in research and development of various technologies in AI, which will benefit the ecosystem in bringing AI closer to consumers.
Tech Giants in AI
The 10 largest tech companies have acquired 50 AI companies in the last 5 years, focusing facial recognition startups, chatbots, chip makers, and more.
Artificial intelligence is the future of big technology — and is already an intrinsic part of the consumer and enterprise products at Facebook, Apple, Microsoft, and Google.
Now, network connectivity providers like Cisco and Oracle are merging advanced machine learning algorithms into their IT and cloud infrastructures. For example, Google was one of the earliest corporations to start obtaining private companies to forward their own AI research. Amazon‘s 2013 asset of Evi Technologies played a huge role in the development of the now popular Amazon Alexa.
Although the venture arms of Google and Intel have infused in a wide range of AI applications, their direct investments and assets have been more tactically focused. Most Importantly, top 10 tech companies in the US have invested in 80 artificial intelligence startups
Here are some Machine learning/AI trends to watch out for this year:
1) AI- Chips
AI heavily relies on exclusive processors that complement the CPU. In fact, the fastest and most advanced CPU may not improve the speed of training an AI model. The model needs extra hardware to perform complex mathematical computations to speed up tasks such as object detection and facial recognition.
In 2019, chip makers such as Intel, NVIDIA, AMD, ARM and Qualcomm will ship specialized chips that speed up the execution of AI-enabled applications. Most Importantly, these chips will be developed for specific use cases and scenarios related to natural language processing and speech recognition. Moreover, next triggering applications from the healthcare and automobile industries will be an impact for delivering intelligence to end-users.
Based on the technical growth, we can say that in 2019 will also be the year where high-scale infrastructure companies like Amazon, Microsoft, Google, and Facebook will increase the investments in custom chips in application specific integrated circuits (ASIC). Some of these chips will also support the next-generation databases to speed up predictive analytics and query processing. In other words, we can say that all these tech giants are in the technical path of AI.
2) Connection of Artificial Intelligence and IoT
Most Importantly, we can say that AI meets IoT at the edge computing layer in 2019. In addition, the top use case of Industrial IoT is an artificial intelligence that can perform outlier detection, Machine Monitoring, root cause analysis and predictive maintenance of the equipment.
Advanced Machine learning models based on deep neural networks will be boost to run at the edge. Based on the aspects of technical results, IoT is all set to become the biggest driver of artificial intelligence in the enterprise.
3) Automation for Machine Learning
AutoML is the one trend that’s going to fundamentally change the face of Machine Learning-based solutions. In addition, it will qualify the business analysts and developers to evolve machine learning models that can address composite scenarios.
In AutoML platform, business analysts stay concentrated on the business problem instead of getting lost in the process and workflow.
AutoML ideally fits in between cognitive APIs and custom ML platforms. In addition, it delivers the right level of customization without pressuring the developers to go through the elaborate workflow. Like API, AutoML exposes the same degree of flexibility but with custom data combined with portability.
4) Neural Networks
Choosing the right framework will be the censorious challenge in developing a Neural Network model. Moreover, Data scientists and developers have to select the right tool from plenty of choices. For example Caffe2, PyTorch, Apache MXNet, Microsoft Cognitive Toolkit, and TensorFlow. Once a model is trained and tested in a particular framework, it is tough to port the trained model to another framework.
Open Neural Network Exchange (ONNX), which solve the interoperability problems and possible to reuse trained neural network models across multiple frameworks. In addition, ONNX will become an essential technology for the industries in 2019. Researchers to edge device manufacturers and all the technical key players of the ecosystem will rely on ONNX.
Above all, you can understand the importance of Machine Learning. However, I suggest some blogs for your enhancement of knowledge.
Igor Bobriakov shares his views in his blog about The Top 10 Technology Trends of 2018. Machine learning and artificial intelligence will become the key technology movement of 2019.
As a result of these aspects, we can understand the technological and opportunity growth in Artificial Intelligence and Machine Learning. By this, the demand for Machine Learning Certification Training is also increasing rapidly.
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