These 5 AI-driven Machine Learning Tools Can Make Enterprises More Productive
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Artificial Intelligence and Machine Learning have barged into our lives without our knowledge and have now taken a permanent spot in there. Despite the scare of machines taking over human jobs, the technology has worked in human kind’s favour majorly. The innovation of AI and ML has indeed made our lives earlier and convenient.
While the industry is at a nascent stage in India, people are aware of their presence. Every day these innovations are contributing to improving people’s way of living along with increasing their level of productivity. Enterprises are exploring distinctive tools for building intelligent machines and programs which can process data on their own without supervision.
Cost and feasibility considerations have been the hindering factors for their adoption but their growing importance in industries can’t be neglected either. Addressing the future implications of the technology’s adoption, DHL SmarTrucking’s CEO Neeraj Bansal conveyed, “AI and ML will play a crucial role in making digital workforces productive and agile by facilitation of allocation and reallocation of resources based on real-time, changing requirements.
From the business perspective, these technologies improve the quantity and quality of data collected, yield data-driven insights and forecasts and facilitate better decision-making. “The lack of bias in AI while evaluating performance and productivity and its ability to interpret and analyze big data to identify patterns and yield data-driven insights and forecasts is also an advantage,” Bansal added.
Apart from ensuring greater accuracy, safety and scale, AI also improves efficiency and productivity for companies. Every small and large tech company has taken it upon themselves to develop a distinctive tool for both the personal and business use. Entrepreneur India enlists 5 AI tools which can enhance an entrepreneur’s productivity:
1. Microsoft Azure Machine Learning
Considered as the next big thing in machine learning, Microsoft Azure provides a simplified platform to businesses for designing advanced analytics on the cloud using algorithms from Xbox, Bing, R or Python packages. “The data preparation tool helps in combining years of data into an organized and complete source,” quoted Rhetick Sengupta, Board President at Microsoft’s Snow Leopard Trust.
The tool acts as a convenient platform for data scientists to build custom web services, publish APIs and charge for its usage. It helps to execute machine learning experiments for building and training models focused on the construction of virtualized environments. Azure ML is known for its powerful cloud-based predictive analytics and for having a marketplace with over 25 machines learning APIs.
2. Google Cloud Machine Learning Engine
Are you looking to improve the customer experience of using your smart app? Google Cloud Machine Learning Engine is your solution. The service is used by multiple enterprises across the globe to solve problems ranging from identifying clouds in satellite images, ensuring food safety and responding to customer emails four times faster.
Cloud ML Engine offers training and prediction services to enable developers and data scientists to build superior machine learning models for production. Being a subfield of artificial intelligence, the goal of ML is to make computers learn from the data that you give them and that is exactly what the Cloud ML Engine aims to achieve.
3. Infosys Nia
The knowledge based Artificial Intelligence platform utilizes the organizational knowledge along with machine learning to drive automation in the enterprise. The goal is to automate repetitive processes based on the data collected from people, processes and legacy systems to solve higher-value customer problems creatively.
The tool helps organizations in leveraging their collective knowledge to generate deep insights and discover opportunities to optimize, simplify, and automate complex business processes ultimately liberating human from monotonous tasks, pushing them towards skilled practices and reducing the cost of maintenance for both physical and digital assets.
4. Wipro HOLMES
The growing requirement of automating transactional jobs has led to the creation of many smart AI-based tools and HOLMES is one of the most efficient and comprehensive ones out there. Apart from downsizing the employee numbers and cutting down processes, the platform offers a series of cognitive services that accelerate business processes through automation.
While it stands nowhere near IBM's Watson, a question-answering computer system that is capable of answering questions posed in natural language, it uses development of digital virtual agents, predictive systems, cognitive process automation, visual computing applications, knowledge virtualization, robotics and drones to automate service desk functions.
Sure machines can’t provide an emotional connection to customers but can surely guide them through certain routes. API.AI helps businesses in interacting with users using the AI-powered voice and text-based conversational interfaces, such as voice apps and chatbots built. Its inbuilt natural language understanding tools help in designing unique conversation scenarios.
The platform initially learns from the data and examples provided by the developer but eventually learn to improve through the experience from continuous interactions with the users. It uses years of domain knowledge and natural language understanding to analyze and understand the user's intent before responding.