Leveraging machine learning to process data and workloads has proved to be significantly beneficial for diverse enterprise industries in recent years. Whether it be healthcare, BFSI or retail, machine learning systems turned out to be extremely promising to process millions of data and build complex models. Having said that, the traditional machine learning process involves humans to look after the operations, to code, and to build the models. But, with the crisis in hand, businesses are looking to reduce their workforce, some are even not equipped with resources to spend on employing an experienced data science team. And that's when AutoML can come to rescue for many.
AutoML, with its ability to perform data pre-processing, ETL tasks, and transformation, will likely become the most popular trend for the year 2020. With the advent of big data, advanced analytics, and predictive models, data scientists today are expected to possess more talent and updated skills when it comes to handling artificial intelligence and machine learning. But these highly skilled data scientists are rare to find. However, bridging the skills gap, the other side of the herd has not only been able to survive but are also capable of building models using the best diagnostic and predictive analytics tools, and part of the reason is AutoML. AutoML packages like auto-sklearn can automatically do the model selection, scoring, and hyperparameter optimisation.
Artificial Intelligence (AI) and Machine Learning (ML) are propelling advanced business solutions across industries. Algorithm-based machine learning development services are overcoming the business challenges of processing heavy data volumes. With accuracy and efficiency, cloud technologies like Google cloud AutoML are encouraging the development of dynamic machine learning solutions. At Oodles, we are testing the model performance of AutoML Natural Language to build domain-specific solutions for global businesses. Let's explore how cloud AutoML for machine learning solutions is triggering automation beyond human intelligence.
Take a look at the three ramen bowls below. Can you believe that a machine learning (ML) model can identify the exact shop each bowl is made at, out of 41 ramen shops, with 95% accuracy? Data scientist Kenji Doi built an AI-enabled ramen expert classifier that can discern the minute details that make one shop's bowl of ramen different from the next one's. Ramen Jiro is one of the most popular chain restaurant franchises for ramen fans in Japan, because of its generous portions of toppings, noodles, and soup served at low prices. They have 41 branches around Tokyo, and they serve the same basic menu at each shop.