We have Machine Learning systems capable of making data-driven decisions/predictions without the need for being explicitly programmed. By adopting Machine Learning systems, you are going to remove a large number of traditional programmers. And create dependency on a small number of expensive Machine Learning experts or Data Scientists. While Machine Learning offers advantages for nearly every industry in some way. Very few companies have actually adopted this Artificial Intelligence technology, and face several common barriers to entry, Many cite barriers to adoption include qualified staff, still-evolving tools, and frameworks. And a lack of huge datasets required to train algorithms and provide learnings.
The barriers are of two different categories. The first one is to understand the philosophy behind Machine Learning systems and the second one is to learn the process for the development of Machine learning-based solutions. Machine Learning system and the development process is totally different from that of traditional software development. To lower the barriers to entry, here are some “vectors of progress” that make it easier, faster, and less expensive to deploy machine learning in a project. Progress along these vectors can help overcome some of the Machine Learning adoption barriers.
Automate Data Science
Machine Learning solutions require Data Science skills, more precisely Data Scientists helping to overcome Machine Learning adoption barriers. The task of a Data Scientist data wrangling, exploratory data analysis, feature engineering and selection, and algorithm selection and evaluation. These tasks can be fully or partially automated with the use of ML technology. Automating these takes will increase the growth and productivity of different sectors. Deploying these strategies will help in growth even if there is a shortage of skilled professionals.
Reduce The Need For Training Data
Working for a machine learning model requires up to countless data elements. Acquiring and labeling these data are often time-consuming and costly. Companies may also use transfer learning, an approach within which a Machine Learning model is pre-trained on one dataset as a shortcut to learning on a replacement dataset in a very similar domain, like language translation or image recognition.
Accelerate training
Training a Machine Learning model may be a time-consuming process due to the massive amount of information and computations involved. Specialized processors like GPUs, field-programmable gate arrays, and application-specific integrated circuits reduce down the time required to train these models, by speeding the calculations and also the transfer of information and reducing cost.
Deploy Locally
The adoption of Machine Learning algorithms will increase together with the flexibility to deploy it where it can most improve efficiency and outcomes. Advances in software and hardware are making it easier to use the technology on mobile devices and IoT devices and sensors. Apple, Facebook, Google, and Microsoft are all creating more compact Machine Learning models which will handle complicated tasks like image recognition and language translation on mobile devices. Using these applications on Mobile devices expands it are of development. Helping to overcome Machine Learning adoption barriers.
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