Deploying Machine Learning Has Never Been This Easy
According to PwC, AI’s potential global economic impact will reach USD 15.7 trillion by 2030. However, the enterprises who look to deploy AI are often hampered by the lack of time, trust and talent. Especially, with the highly regulated sectors such as healthcare and finance, convincing the customers to imbibe AI methodologies is an uphill task. Of late, the AI community has seen a sporadic shift in AI adoption with the advent of AutoML tools and introduction of customised hardware to cater to the needs of the algorithms. One of the most widely used AutoML tools in the industry is H2O Driverless AI. And, when it comes to hardware Intel has been consistently updating its tool stack to meet the high computational demands of the AI workflows. To download the complete case study, Click Here. Now H2O.ai and Intel, two companies who have been spearheading the democratisation of the AI movement, join hands to develop solutions that leverage software and hardware capabilities respectively. How AutoML and Customised Hardware Can Accelerate AI Adoption AI and machine-learning workflows are complex and enterprises need more confidence in the validity of their AI models than a typical black-box environment can provide. The inexplicability and the complexity of feature engineering can be daunting to the non-experts. So far AutoML has proven to be the one stop solution to all these problems. These tools have alleviated the  challenges by providing automated workflows, code ready deployable models and many more. H2O.ai especially, has pioneered in the AutoML segment. They have developed an open source, distributed in-memory machine learning platform with linear scalability that includes a module called H2OAutoML, which can be used for automating the machine learning workflow,  that includes automatic training and tuning of many models within a user-specified time-limit. Whereas, H2O.ai’s flagship product Driverless AI can be used to fully automate some of the most challenging and productive tasks in applied data science such as feature engineering, model tuning, model ensembling and model deployment. But, for these AI based tools to work seamlessly, they need the backing of hardware that is dedicated to handle the computational intensity of machine learning operations. Intel has been at the forefront of digital revolution for over half a century. Today, Intel® flaunts a wide range of technologies, including its Xeon® Scalable processors, Optane™ Solid State Drives and optimized Intel® software libraries that bring in a much needed mix of enhanced performance, AI inference, network functions, persistent memory bandwidth, and security. Integrating H2O.ai’s software portfolio with hardware and software technologies from Intel has resulted in solutions that can handle almost all the woes of an AI enterprise from automated workflows to explainability to production ready code that can be deployed anywhere. For example, H2O Driverless AI, an automatic machine-learning platform enables data science experts and beginners to streamline their AI tasks within minutes that usually take months. Today, more than 18,000 companies use open source H2O in mission-critical use cases for finance, insurance, healthcare, retail, telco, sales, and marketing. The software capabilities of H2O.ai combined with hardware infrastructure of Intel, that includes 2nd Generation Xeon® Scalable processors, Optane™ Solid State Drives and Ethernet Network Adapters, can empower enterprises to optimize performance and accelerate deployment. Enterprises that are looking for increasing productivity while increasing the business value of to enjoy the competitive advantages of AI innovation no longer have to wait thanks to hardware backed AutoML solutions. To download the complete case study, Click Here
According to PwC, AI’s potential global economic impact will reach USD 15.7 trillion by 2030. However, the enterprises who look to deploy AI are often hampered by the lack of time, trust and talent. Especially, with the highly regulated sectors such as healthcare and finance, convincing the customers to imbibe AI methodologies is an uphill […]
According to PwC, AI’s potential global economic impact will reach USD 15.7 trillion by 2030. However, the enterprises who look to deploy AI are often
["deploying models", "Machine Learning"]
["data science", "Go", "machine learning", "deploying models", "AI", "ML", "Machine Learning", "Scala", "Git", "RAG", "Rust", "R"]
["AI", "machine learning", "ML", "data science", "RAG", "R", "Go", "Rust", "Scala", "Git"]
I have a master's degree in Robotics and I write about machine learning advancements.