(LSJ) What is Data Science Platform?

When building teams

Market Definition/Description

Data Science Platform definition

A cohesive software application that offers a mixture of basic building blocks essential for creating all kinds of data science solution, and for incorporating those solutions into business processes, surrounding infrastructure and products.

Artificial intelligence (AI) is hyped:

Hype about AI is at its peak, but AI must be distinguished from data science and ML. Of course, data science is the core discipline for the development of AI, and ML is a core development of AI, but this is not the whole story. ML is about creating and training models; AI is about using those models to infer conclusions under certain conditions. AI is on a different level of aggregation to data science and ML. AI is at the application level.

Data Science and ML models must be combined to work together with other capabilities, such as a UI and workflow management, to constitute an AI Application.

The Report finds variety of audiences to data science and ML platforms (modified):

  • Citizen data scientists who are accessing (open) data and building data science and ML models. They come from roles such as business analyst, line of business (LOB) analyst, data engineer, and application developer.

  • LOB data science teams who address initiatives in areas such as digital marketing, risk management, Customer Engagement Management.

  • Corporate data science teams who have strong executive sponsorship, and can take a cross-functional approach. They do model building and end-to-end process for building and deploying data science and ML models.

  • Star (Mavericks) Data Scientists. The Python, R, Apache Spark Gurus.

(Gartner Report Magic Quadrant for Data Science and Machine Learning Platforms, published 28 January 2019 ; by Analysts Carlie Idoine, Peter Krensky, Erick Bretheneoux, Alexander Linden).

(LSJ) Lifetime Classification of Consultants

IDC asked me for salary study our classification of Consultants and their payment expectations for 2019. Here is our Classification of Consultants for Secure Managed Sourcing.

1. Consultants (skills level 2,3,4)

Installation & administration of hardware, operating systems, Microsoft office 365 / G Suite software Administration, implementing security solutions on desktop, servers and LAN. Support of hardware, operating systems and office-software, troubleshooting and repair. Any new consultant is in this category before clearing Cloud Ready Teams Certification Program. Quality Assurance.

2. Developers (skills level 2,3,4)

1-3 years of experience

3-5 years of experience

5+ years of experience

of Coding,  test automation,  Applications Production, DevOps. 10 Programming languages.

3. Technical Project Managers

Thorough competence within a specific area. For instance JIRA, SAFe, cloud computing, networking, security, Linux. The specified person has a long experience and thorough technical understanding – but not a broader approach toward the solutions. System integrator specialists (Mulesoft, Openshift), system designers within a specific solution. Technical Project managers.

4. Cloud solutions architects

Cloud Solutions Architects cover a broader area, for instance combining server, network and application knowledge to design the overall infrastructure. Capable of creating requirement specifications based on for instance workflow descriptions etc. Senior project managers, large projects.

5. Senior consultants

Combining technical and smart business comprehension (not management consultants). Competence in organizational analysis combined with a technical understanding – the business part of cloud solutions architects. IT estimation, risk estimation, business continuity analysis – from a business and an IT perspectives.

6. Strategic Advisors

Management consulting. Advisor level. Strategy formation. Cloud Strategies. Please contact us.

(LSJ) Everyone wants to just focus on business logic

AWS re:invent Keynote with Werner Vogels, CTO, Amazon.com

AWS re:invent Keynote with Werner Vogels, CTO, Amazon.com

API Gateway gets Websocket Support

ALB Support for Lambda