It provides two software products that together make up its core DSML platform: Platform for AI (PAI) Studio and Data Science Workshop.
In addition to China, Alibaba has a strong customer base in the South and Southeast Asian markets, but it does not have many clients elsewhere. Its current platform focuses on applications for the retail, internet and data service sectors.
Alibaba Cloud’s product and roadmap are well-suited to expert data scientists and data engineers in sectors such as internet technology, data services, retail and government. Alibaba Cloud emphasizes support for augmentation of certain tasks in the DSML workflow, but its platform lacks functionality and ease of use for citizen data scientists, which slows its adoption by less mature organizations.
Strengths
• Strong community built in China: Alibaba Cloud showcases its community’s strength with the Tianchi platform, a Kaggle-like platform for collaboration, competition and knowledge sharing. The platform is widely adopted within the Chinese market.
• Advanced use-case modeling: Alibaba provides strong solutions for advanced use cases such as image labeling, image recognition and segmentation, and recommendation engines, which can be useful to expert data scientists.
• Seamless integration that creates coherence: Alibaba provides a coherent platform that integrates well with its other offerings for data preparation, exploration, ML, augmentation and delivery. It offers drag-and-drop interactive modeling features across its platforms, which can be used by expert data scientists to support the ML pipeline.
Cautions
• Geographic strategy: Although Alibaba Cloud has offices and service locations in many countries, the clients it serves are mostly in Asia/Pacific. Prospective customers should ensure they are satisfied with the vendor’s presence and support in their region.
• Product vision: Given the current pace of development by other vendors, Alibaba Cloud will have to be swift and agile, as this market is likely to remain highly competitive. Some key themes of, and items on, its product roadmap are already available as standard features from many other vendors.
• Narrow usage and lack of citizen data scientist support: The current PAI Studio and Data Science Workshop offerings offer limited ML and advanced analytics capabilities, such as agent-based modeling, discrete-event modeling, Monte Carlo simulation, support for generative adversarial networks and self-supervised learning. Currently, the platform is suitable for advanced users but may not be a good choice for citizen data scientists or business analysts.
#architecture #team #data #cloud #scalability #infrastructure #hardware #intelligence #business #data #payments #cloud #automation #salesforce #keepgoing #artificialintelligence #machinelearning #api
#Healthcare #Retail #Ecommerce #Food #Tech #Banking #Finance #Insurance #Logistics #Transpostation #Travel #Aviation #RealEstate #Entertainment #Gaming #Manufacuring #SocialMedia #Applications #Education #HumanResources #KEEPGOING #analytics #like #business #iot #cloud #5g #algorithms #technology #aws #capitalmarkets #investmentmanagement #banks #people #help #covid #change #innovation #dataanalytics #computing #software #aws #hardware #network #5g #data #business #innovation #sustainability #research #manufacturing #like #healthcare #database #people #aws #amazon #google #productivity #job #cloud #love #customerexperience #sales #france #marketing #team #formation #formation #deeplearning #ml #tensorflow #ai #language #personalization #video #management #strategy #businessintelligence #visualization #project #training #experience #docker #kubernetes #environment #testing #financialservices #bigdata #engagement #digital #machinelearning #digital #brand #happiness
