Changing Machine Learning applications with Google’s AutoML

By Naveen Joshi – Founder and CEO of Allerin.

Works in Big Data,IoT , AI and Blockchain.

• Without in-depth knowledge of the technology, businesses can still build robust ML (Machine Learning) applications, with high processing power, accuracy, and efficiency, thanks to Google’s AutoML.

Are you an ML developer? If yes, then you will better understand the difficulty of creating an ML-based solution. The time that you invest in data collection, modeling, and experimentation before developing the ready-to-use ML applications is pretty long. Even the most experienced and skillful ML engineers take time to produce an accurate and efficient ML system. Despite the benefits offered by the end product, developing an ML system is time-consuming, complicated, and challenging.

However, there is a high demand for AI deployments in businesses, which also means that there should be enough skilled ML engineers and experts. To address this gap and to bring back the lost smile of ML experts, AI leaders such as Google have made considerable investments in this area. The company has realized the need for an ML model or software that would work without ML experts. Hence, the great minds at Google have introduced an ML software, AutoML, that can replicate the algorithm and develop ML models by itself.

Explaining everything about AutoML

By now, we know what triggered the necessity for an ML tool like AutoML. AutoML, or automated machine learning, can replicate algorithms and develop robust, reliable, and efficient ML models without the need of human help. Google developed the ML tool using an approach called reinforcement learning. Here, AutoML behaves as a controller, which further develops the child ML model. This child ML model, called NASNet, is then tested and evaluated for its accuracy, efficiency, and its performance. The entire process is repeated many times until an efficient model is created.

Changing human-made ML applications with Google’s AutoML

Google researchers carried out a test for AutoML based on image-recognition. The child ML model had to recognize objects like shoes, bags, people, etc. in given images in real-time. AutoML successfully detected objects in a given image with 82.7 percent accuracy. The complicated, human-made process of creating ML models, including

  • data collection,
  • data analysis,
  • data modeling, and
  • data experimentation
  • is now eliminated due to Google’s AutoML. Now, companies need not have proficient ML experts for leveraging this technology. Just by uploading a few pictures for training the model, they will get a highly efficient, ready-to-use, customized ML model. Even though ML specialists now have the assistance of this easy-to-use ML tool, their job does not end here. It is true that, with AutoML, companies can skip over the initial complicated process of developing the ML system. But, ML experts are expected to focus on extra lines of business, which means streamlining their focus on designing and developing the end product. The end product should answer yes to all of these questions: Is the end product reliable, easy-to-use, and fast?
  • Is the product well-formulated?
  • Does the product work better than competitors’?

Doing so will help companies engineer optimized ML models at a faster rate, that will, in turn, enable them to stay relevant in the competitive space.

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