This post is a mix review of Machine Learning type of solutions the market offers, and a quick review of some players I have in my mind.

Machine Learning Wardley Map

Components:

1. Machine Learning can be used by companies and individuals. The B2B and B2C is important when you look at the perspective that many individuals don’t know the are using solutions based on machine learning every day.
2. Data scientific: I have centered as the element creating the machine learning solution, which is not completely true, as around the person or team creating a machine learning solution for a big organization there are many stakeholders working around it: operations, data analysts, marketing, legal department….
3. Specific solution: it’s a solution for a given problem that is pre-build or build and it’s available to be acquired by a company or a person. Usually they are centered on an industry and they are very specific to a niche or a given problem. We will not find here generic problems or generic solutions.
4. Machine learning development kit and platform: they are the tool and the environment available for the data scientific. The existence of cloud solutions and industrialized solutions enable an individual to have available environments and development kits to work on Machine Learning. We cannot forget the amount of resources available are incredible. Tom Kerwin commented me on the map that data lakes are in “custom build” stage more than as a “basic service”, due to the fact that to gather all data with the right quality is so many times a project it self (Thanks Tom!).

After drawing this simple map, I got distracted by stocks and I put my attention on components 3 and 4: specific solutions and tool + platform. The result of being 2 hours looking around, I got these 2 tables:This second table have some spaces, which mean there is not solution on that area for the given company (at least I have not seen it).

Some notes that I have not included in the pictures:

• Impressive work done by Google on many areas, their capacity and amount of available resources offered to a person like me are infinite.
• Facebook is a refined machine of data and algorithms that seems to work in a perfect way taking into account that what they are managing is very difficult: people’s opinions and behaviors. They are investing a lot on virtual reality and augmented reality.
• Robotics, you have Amazon, and then the rest of the world. The day they deciede to sell their robotic solutions, it’s going to be interesting what happens in the industry.
• Palantir has a good bunch of solutions and they are very closed to big clients. They are attending customer issues and have a lot of work to be done.
• C3.ai, same thing as Palantir, they are attending end customer issues with their solutions. By the moment they have so many industrial customers, they have to demonstrate they can attend other type of customers with a good volume of Purchase Orders.
• There is space for all of them and other players. This just have started.

What are your thoughts about Machine Learning solutions and its players?

Update 29/September/2022

Update 1: I found this article from Ergest Xheblati that illustrate very well so many of the components when working on Data:

https://www.ergestx.com/data-landscape-wardley-mapping/

Update 2: This article, written by Max Langenkamp “How Open Source Machine Learning Software Shapes AI” reviews how important is Machine Learning Open Source Software (MLOSS).

https://maxlangenkamp.me/posts/mloss_essay/

Business incentices for MLOSS (from the same paper)