The next generation enterprise

MaxthonSnap20170316054703

I got this chart from MIT class.

Keep in mind this chart and think which area your company belong to. How deep are your company understand your customers, and what’s the value you can creat for them.

As individual, this chart also useful to consider what the relationship between your self and your company. To what extent do you understand your company’s need?   Let’s think.

 

鑑識會計

MaxthonSnap20170318184506

MaxthonSnap20170318185916

Top 10 highest tech investment priorities

MaxthonSnap20170316053757

It makes sense.  I am interested in 1.2.4.5.6.8.9.10

Business plan process

MaxthonSnap20170314083105

流程圖可以簡化實務,但無法取代對內涵本質的理解. 除非真的很熟每一個要素, 不然流程圖沒有幫助而且也並非絕對. 其實用價值鏈來看也許會更好. 商業跟純技術上的學習又有點不太一樣.  技術是學原理,對象是機器, 商業是面對人與社會.

這圖可以留起來, 要用時再拿出來參考.

 

Talent management

MaxthonSnap20170314081339

很不實際的定義,應該有一些假設前提. 以台灣的情況根本不適用,實務上沒人會用這樣想事情.

 

Stripe

  • https://stripe.com/
  • A payment service provider
  • a US technology company,operating in over 25 countries, that allows both private individuals and businesses to accept payments over the Internet.
  • Stripe focuses on providing the technical, fraud prevention, and banking infrastructure required to operate online payment systems (note1.3)

Stripe 的金流服務可以收取全球 139 種幣別,不過目前該服務只在 12 國提供,除了美國、加拿大、英國和愛爾蘭之外,其他如澳洲、德國、法國、荷蘭和西班牙等八個國家尚在 beta 階段。現在他們已經有幾個大客戶像是 Salesforce、Lyft、Shopify、Squarespace 和 TED 等 (note2)

(note1: https://en.wikipedia.org/wiki/Stripe_(company)

(note2: Stripe:連 PayPal 創辦人都投資的金流公司)

(note3:https://zh.wikipedia.org/wiki/Stripe)

Amazon Marketplace Web Service (Amazon MWS)

  • 亚马逊商城网络服务 (Amazon MWS)

an integrated Web service API that helps Amazon sellers to programmatically exchange data on listings, orders, payments, reports, and more. XML data integration with Amazon enables higher levels of selling automation, which helps sellers grow their business. By using Amazon MWS, sellers can increase selling efficiency, reduce labor requirements, and improve response time to customers.

(Reference: https://developer.amazonservices.com/)

(Reference: https://developer.amazonservices.com.cn/)

Google, AI, Machine Learning

Google cloud platform (note5)

Target : the enterprise cloud market

  • Position- To be a developer-friendly platform
  • Weakness

1) Not strong and no impression  on cloud service and the enterprise segment

2) No contribution to the open source community before.

(note1)

  • Stength : ASIC, GPU and TPU hardware in its cloud
  • Opportunity

1) begin to work with open source projects (note3)

  • Cloud Native Computing Foundation- the open-source container management tool

run by the Linux Foundation (note4)

Other partners in the new foundation include AT&T, Box, Cisco, Cloud Foundry Foundation, CoreOS, Cycle Computing, Docker, eBay, Goldman Sachs, Huawei, IBM, Intel, Joyent, Kismatic, Mesosphere, Red Hat, Switch SUPERNAP, Twitter, Univa, VMware and Weaveworks.

Absent : Microsoft, Amazon, Pivotal, and  Taiwanese tech companies)

the popular open source container orchestration system

  • TensorFlow for machine learning

-Spanner for launching massive distributed databases

-Draco for 3D graphics compression

2) To be a  developer-friendly platform

  • “OPENNESS" 

1)letting customers run whatever open source stack they choose on Google’s infrastructure,

2)releasing and supporting open source projects and making the ecosystem

3)the partners who build tools and technologies on top of GCP, a first class citizen on the platform.

4) treating them as part of the whole and the net is bringing the tech you want and using Google technology or using any of the [partner] services

The KSF :

1)Being open to win the mind shares of developers

2) much more supportive of the open source community makes people feel better about Google and makes developers feel better about working with their tools because they can avoid lock-in

  • Threat : peers: AWS (2006. 1st public cloud, market leader, 1st mover), Microsoft, IBM
  • Strategy

UsingKubernetes, the popular open source container orchestration system offer robust open source tools, something that surprised some people in this market.

  • 4 ways Google will enable enterprises to adopt machine learning and AI (note2)

1). Machine learning computing in Google Cloud

a deep learning algorithm can have tens of millions of parameters, training these machine learning models requires enormous computational resource

the Cloud Machine Learning Engine. This capability is designed for companies with data scientists and machine learning experts who are able to build their own unique machine learning models with libraries such as Tensorflow.

Google’s infrastructure as the solution to speed training times and improve the return on investment. Google has specialized ASIC, GPU and TPUhardware in its cloud to accelerate training and improve the ROI with on-demand cloud resource utilization. After the model is trained, it is deployed in range of platforms—from on-premise to mobile devices.

2. Algorithms and pretrained machine learning models

建ML model 需用 the machine learning engine, 用 Google’s pre-trained models (full list) using APIs to add machine learning capability to their applications, such as understanding natural language, images and natural language.

An API beta for understanding videos

demo: This 3-minute video of the demonstration of the Cloud Video Intelligence beta

3. Google acquires Kaggle for data

Google acquired Kaggle for data sets and talent. Kaggle, founded in 2010, is a community of 850,000 data scientists from around the world that hosts competitions to create the most accurate predictive models and market models, as well as to acquire new public data sets in a variety of fields.

4. Expertise

the Advanced Solutions Lab for customers with ambitious goals to develop machine learning to solve complex problems.

(note1: https://techcrunch.com/2017/03/09/google-in-the-cloud/)

(note2: http://www.networkworld.com/article/3179127/cloud-computing/4-ways-google-cloud-will-bring-ai-machine-learning-to-the-enterprise.html)

(note3:https://techcrunch.com/2015/07/21/as-kubernetes-hits-1-0-google-donates-technology-to-newly-formed-cloud-native-computing-foundation-with-ibm-intel-twitter-and-others/)

(note4: The mission of Linux foundation :The mission of this new foundation is to “help facilitate collaboration among developers and operators on common technologies for deploying cloud native applications and services,” )

(note5: https://cloud.google.com/)

(Reference : Google Cloud Platform 入門)

(Reference: https://technews.tw/tag/google-cloud-platform/)

 

Machine Learning for Marketing

The marketing big data ecosystem being impacted by machine learning in four major areas:

  1. Automated data visualization (including ML results) will become more rich, and user-friendly.
  2. Content analysis (textual, lexical, multimedia/rich) will be used to drive better marketing conversations.
  3. Incremental ML techniques will become more prevalent, leading to real-time, not just on-going and automated, changes in marketing execution.
  4. Learning from ML results will accelerate the growth and skills of marketing professionals.
  • Automated Data Visualization tools: Tableau and Qlikview

Predictive model : The objective of ML is to build predictive model for forecast.

the ability to modify a solution that is already in place by introducing new data rather than having to stop using the current solution before building a new model from scratch.

(Source from How Machine Learning Will Be Used For Marketing In 2017)

TCP/UDP ports

MaxthonSnap20170309074408

%d 位部落客按了讚: