How to Break into AI Product Management without experience

Summary

本视频由产品经理Diego主讲，探讨了在人工智能和机器学习领域成为产品经理的策略。Diego强调，尽管AI和机器学习产品的团队构成略有不同，但无需深厚的工程或计算机科学背景。他通过简单示例讲解了机器学习模型的创建过程，并建议通过Kaggle等平台学习和实践。Diego还提供了如何通过项目和网络建设进入这一领域的建议，鼓励观众了解AI产品经理的多样性和可能性。

Takeaways

• 🚀 作为AI和机器学习领域的产品经理，与一般产品经理的主要区别在于团队构成和工作重点。
• 🌟 AI和机器学习产品的团队中通常会加入数据科学家这一新角色，负责理解数据和构建、优化机器学习模型。
• 📊 产品经理不需要是工程师或拥有计算机科学学位，但需要了解不同技术概念和所涉及的技术栈。
• 🛠️ 即使没有技术背景，通过学习也能对应用程序的技术细节有所了解，这有助于产品管理工作。
• 🏠 通过简单示例，展示了如何使用机器学习模型预测房价，说明了模型创建的基本过程。
• 🔍 创建机器学习模型不仅仅是编写代码，还涉及到数据清洗、模型选择、结果解释和改进等多个步骤。
• 📈 评估机器学习模型时，需要考虑多种指标，而不仅仅是模型的绝对误差。
• 🔑 产品经理需要理解算法的创建过程和评估方法，以便更好地与团队沟通和规划产品特性。
• 🌐 AI和机器学习已广泛应用于各种应用程序中，如搜索引擎、智能家居、电子邮件过滤等。
• 📚 通过在线课程、参与数据科学竞赛和实际项目，即使没有机器学习背景的人也可以学习并进入AI产品管理领域。
• 💼 建立公共作品集和参与社区活动，可以提升个人在AI和机器学习产品管理领域的专业度和可信度。

Q & A

AI和机器学习领域产品经理与其他产品经理有何不同？

AI和机器学习领域的产品经理在团队构成上有所不同，他们会与数据科学家合作，关注模型的构建、部署和整合。团队比例也会有所变化，每个产品经理可能会配5到10个数据科学家和2到5个工程师。

成为AI或机器学习产品经理需要哪些技能？

虽然不需要计算机科学或数据科学背景，但需要理解算法的创建过程和评估方法，以便与团队进行有效沟通，评估权衡，规划产品特性，并为用户创造最佳体验。

产品经理需要了解哪些技术概念？

产品经理需要了解与其产品相关的技术概念和科技栈，例如数据库、数据处理、跨平台编程工具、前后端组件等。

如何通过简单的代码创建一个机器学习模型？

可以通过导入决策树回归器库，创建基于该算法的模型，使用模型特征训练模型，然后进行预测和结果验证。

机器学习模型的开发时间是否很短？

尽管编码模型相对简单，但整个过程比想象中复杂，需要考虑数据质量、模型选择、性能评估等多个方面。

如何验证机器学习模型的结果？

可以通过选择合适的评估指标，如平均绝对误差，来衡量模型的性能。

AI和机器学习在哪些产品中得到应用？

AI和机器学习应用于多种产品，如搜索引擎、语音识别设备、电子邮件过滤、推荐系统和导航应用等。

如何进入AI和机器学习产品管理领域？

可以通过学习如何将机器学习应用于产品，理解模型创建过程，并通过实际项目或参与数据科学竞赛来展示自己的能力。

Kaggle.com对于学习机器学习模型有何帮助？

Kaggle提供了免费的机器学习模型编码课程，基础的Python编程语言教学，以及数据科学竞赛，有助于实践和建立公共作品集。

如何通过实际项目展示自己的机器学习能力？

可以创建一个使用机器学习解决问题的产品或站点项目，记录整个过程，包括问题识别、用户交流、原型开发和产品发布等，并将其作为公共作品集的一部分。

如果我想更深入地学习AI和机器学习，有哪些途径？

可以选择回到学校学习计算机科学或数据科学，专注于AI和机器学习领域，这可能需要投入时间和金钱，但也能开启职业发展的其他可能性。

Outlines

00:00

🤖 人工智能产品经理的工作介绍

本段落介绍了作为人工智能产品经理的工作内容，包括与数据科学家合作、产品团队构成的变化、以及即使没有计算机科学背景也能成为产品经理的可能性。强调了产品经理需要了解技术概念和产品堆栈，以及如何通过可视化技术来理解产品。

05:01

🏠 机器学习模型的创建与评估

这一部分详细描述了机器学习模型的创建过程，包括导入决策树回归器、创建和训练模型、进行预测以及验证模型的准确性。同时，提出了关于模型评估的多个问题，如数据质量、获取更多数据的途径、数据使用的伦理性等，说明了机器学习产品管理的复杂性。

10:02

🚀 进入AI和机器学习产品管理领域的途径

最后一段讨论了如何进入AI和机器学习产品管理领域，无论背景或经验如何。提到了通过学习机器学习模型的创建过程、参与数据科学竞赛、建立公共投资组合、寻找非盈利组织进行志愿工作等方法。同时，提供了学习资源和建议，鼓励观众通过实践和网络来提升自己的技能和知识。

Keywords

💡产品管理

产品管理是指对产品从构思、设计、开发到市场推广的全过程进行规划、组织、协调和控制的过程。在视频中，Diego作为产品经理，探讨了如何突破进入产品管理领域，并解释了产品经理在AI和机器学习领域的工作特点。

💡AI和机器学习

AI（人工智能）和机器学习是计算机科学的一个分支，它们使计算机系统能够通过经验学习和改进。在产品管理领域，AI和机器学习的应用非常广泛，如搜索引擎、推荐系统、语音识别等。

💡数据科学家

数据科学家是专业人员，他们利用统计学、数据分析和机器学习技术从大量数据中提取有价值的信息。在产品团队中，数据科学家负责理解数据和构建、优化机器学习模型。

💡技术栈

技术栈是指用于构建和运行一个应用程序或系统的不同技术和工具的集合。对于产品经理来说，了解他们所负责产品使用的技术栈是非常重要的，这有助于他们更好地理解产品的功能和限制。

💡用户体验

用户体验（User Experience，简称UX）是指用户在使用产品过程中的感受和体验。产品经理需要关注产品的用户体验，确保产品易于使用，满足用户需求，并提供愉悦的使用体验。

💡模型

在AI和机器学习领域，模型是指通过算法和数据训练得到的系统，它能够进行预测或决策。产品经理需要理解模型的创建过程、评估标准以及如何将模型集成到产品中。

💡特征

在机器学习中，特征是输入数据的属性或变量，用于训练模型。特征对于模型能否准确进行预测或分类至关重要。

💡算法

算法是一系列定义明确的操作步骤，用于解决特定问题或执行特定任务。在AI和机器学习中，算法是构建模型的基础，它们决定了模型如何处理数据和做出预测。

💡数据清洗

数据清洗是指在数据分析或机器学习项目中，对原始数据进行处理以消除错误、不一致和不完整的数据记录的过程。数据清洗对于确保模型训练和预测的准确性至关重要。

💡度量评估

度量评估是指使用特定的度量标准来评估模型的性能和准确性。在机器学习中，度量评估帮助确定模型是否足够好，以及是否需要进一步优化。

💡产品特性

产品特性是指产品所具有的独特属性或功能，它们定义了产品与市场上其他产品的区别。产品经理需要规划和优先考虑产品特性的开发，以满足用户需求和业务目标。

Highlights

产品管理在AI和机器学习领域的工作特点，与一般产品经理有何不同

AI和机器学习产品团队的构成，包括数据科学家的角色

即使没有工程或计算机科学背景，也可以成为产品经理

产品经理需要了解的技术概念和技术栈

如何通过可视化技术概念来理解应用程序

在AI和机器学习领域，产品经理需要理解数据科学家创建模型的过程

简单的机器学习模型编码示例

机器学习模型开发过程比编码更复杂

AI和机器学习在各种应用程序中的广泛应用

产品经理在AI空间中的各种项目类型

如何进入AI和机器学习领域的产品管理

通过Kaggle学习编码机器学习模型

参与数据科学竞赛并建立公共作品集

创建使用机器学习的产品或项目以解决问题

通过AngelList或Product Hunt寻找AI项目进行网络拓展和志愿工作

返回学校学习计算机或数据科学以开启更多职业机会

Transcripts

hey friends welcome back to the channel

if you're new here my name is diego i'm

a product manager and on this channel we

explore strategies to break into product

management and what it actually means to

be a product manager today we're going

to talk about what it's like to work as

a product manager in the ai and machine

learning space what is different about

it from more of a generalist kind of

product manager and more importantly

however become a pm that works in this

space regardless of your degree or your

experience

one of the first differences that you'll

notice is that in ai and machine

learning products and features the

product team is slightly different in a

typical product team at a large tech

company you'll have for every product

manager anywhere from five to ten

software engineers one or two designers

and access to share resources like user

research teams marketing finance sales

operations support legal and many other

roles that pms will most likely interact

with and when you work in teams that

have to do with ai and machine learning

we actually introduce a new role in the

mix besides the software engineers

you're going to work with data

scientists who will be in charge of

understanding the data and building and

fine-tuning machine learning models that

will end up being used in the product

and with this new role in the team the

focus now shifts towards building models

and deploying them and integrating them

the ratios are gonna change a little bit

because of this so now for every product

manager you're going to have about five

to ten data scientists two to five

engineers and the rest actually remains

pretty similar you can have one or two

designers and the other functions or

roles that are going to be part of the

broader product team

i want to emphasize that you do not need

to be an engineer or have a degree in

computer science to become a product

manager actually depending on the

product that you work on you're going to

need to learn about the different

technical concepts and the technology

stacks if you were to work for example

on a mobile application you don't need

to know exactly how the app is coded but

you'll probably need to learn high level

concepts about databases and handling

data cross-platform programming tools

like react native to deployed in android

and apple store authentication and

security and other components about the

back end and the front end of your

application i'm sure you have used many

many applications on your phone in fact

you might actually be watching this

video in one of them as you learn the

different technical concepts needed to

create apps it is relatively easy to

sort of visualize the concepts that

you're learning think of the times that

you have logged into an app while you

may not know exactly how it works you

can probably imagine how your data your

user preferences and even information

about you are stored somewhere in a

database you can also understand what to

actually expect from the user experience

of logging into an app now for sure

there will be many more complicated

products and technology stacks than a

mobile application but i hope this gives

you the idea that in many products you

have the opportunity to visualize the

technology you're working with

especially if you don't have that

technical background

so while you don't need to be an expert

in technology and certainly you don't

need a computer science degree to be a

product manager when it comes to machine

learning it's sometimes harder to

actually see or visualize what you're

working on because you may or may not

actually have a user interface in your

product besides that you actually do

need to get a little bit deeper into the

technical details of the models or the

algorithms so as a product manager in ai

or machine learning you need to

understand the process that data

scientists go through to actually create

good models this goes beyond

understanding how to code machine

learning models in fact coding a simple

machine learning model can take about

three to five lines of code here let me

actually show you that all right so in

this very simple example i'm going to

show you how you can use a very simple

machine learning model to predict

house pricing so everything about this

line is simply

making sure that i have the data for

this example in the format that i needed

so that the model can run so let's look

at how i can code a very simple machine

learning model in five steps step number

one is i'm simply going to import the

library that is going to be called

decision tree regressor and that's the

algorithm that i'm going to use for this

example step number two is i'm going to

create a machine learning model that is

going to be called house pricing model

based on the decision tree regressor

which again is the algorithm that i

imported from the library

step number three is i'm going to train

my model i'm going to teach my model

using the model features like the size

of the house the number of rooms the

number of bathrooms and i'm going to

also teach my model about the prices of

existing houses so all i'm doing here is

telling my model to find the patterns in

the data and then step number four is

i'm going to make the predictions so

giving the model some data that the

model doesn't know the prices for

because it has already learned about the

previous patterns i'm simply going to

tell it to look to estimate or to

predict what are going to be the prices

for these houses

and so for that i'm simply going to

print these are the prices that my model

has estimated

but is this result good enough well to

do that we'll go to step number five

which is simply validating the metric

that i chose for this example which is

the mean absolute error and it's close

to 33

and that's it that's all it took to

create a model that is going to learn

from the data make some predictions and

then give me some results to measure

whether it was a good model or not so

now that you see that coding models is

relatively simple without understanding

the process of everything else that

happens before and after these few lines

of code could actually lead you to think

that development time for a machine

learning feature is

quite fast however when we start asking

questions

we'll realize that the process is much

more complicated than what we thought

for example i mentioned that the root

mean square error being almost 33 000.

is that good

are there other metrics to measure how

good the model is or is this the only

kind of model or the best model for this

problem

is the data that we're using to test

actually good is it good enough do we

need more data how do we actually get

more data ethically speaking are we

using the data the way we're supposed to

so the more questions we start to ask

the more you hopefully realize that the

process of creating machine learning

models is more than just five lines of

code and then moving that into an

application and thinking through how the

users will actually consume the models

just keeps piling on and increasing the

amount of tasks and things that a

product manager in this space needs to

consider when prioritizing things so

once again when it comes to ai and

machine learning you don't need to have

a computer science or a data science

background but understanding how these

algorithms are created and how you

evaluate them will actually help you

have better conversations with your team

too evaluate trade-offs limitations have

a better planning for your features

prioritize better your backlog have a

solid growth map and create the best

experience for your users

there are all kinds of projects in the

ai and machine learning space that

product managers can work on in fact

almost every app and many of the

products that you have used recently

have some components of machine learning

for example if you recently searched for

something on facebook tiktok amazon

linkedin or any other app or website

that includes a search feature there are

product managers engineers and data

scientists that work on these

functionalities to make sure that you

have a great experience with the product

and that the results are actually

relevant to your search

devices like google home and alexa they

need ai to work for speech recognition

even your email uses machine learning to

do something as common as a spam filter

self-driving cars and assisted driving

cars require ai and machine learning to

work properly and products like netflix

use machine learning to determine what

series and movies they'll actually

recommend to you even apps like google

maps use a lot of ai resources to show

you things like the best route to get to

your destination or the best time to

leave your home and arrive on time but

not all product managers in the ai space

actually work in this kind of products

in other cases pms work on projects that

don't always have a user interface some

pms actually work on internal or

sometimes external tools that may for

example make a prediction of which

customers are going to completely stop

buying from a company this is also known

as a churn prediction and then marketing

is gonna take that prediction or the

information from the model to know whom

should they reach out to and offer a

discount or a promotion before they

actually start purchasing altogether i'm

sure you've probably received some of

those emails if so most likely a machine

learning model may actually have

predicted something about you and the

company and there's likely a pm behind

that prediction so just like any other

kind of product manager those who work

in ai and machine learning applications

actually have a wide range of types of

projects companies and problems to solve

alright so now that you know more about

what product managers in the ai and

machine learning space do let's talk

about how you can enter this space

regardless of your background or your

experience with machine learning

there are in general multiple ways to

break into product management and i'm

actually going to leave you a video up

here in the corner that you can check

out to learn more details about things

like how to do internal transfers site

projects and other ways to break into

product management the best way to

become a product manager in this space

is to show that you know how to use

machine learning in a product and for

that you need to demonstrate that you

understand the process of creating

models this goes from cleaning the data

understanding which model is best for

what problem and all the way to

interpret the results of your model and

how to actually improve them

one of the best websites that i found to

learn how to code machine learning

models for free is kaggle.com they have

courses on the basics of python as a

programming language with very easy to

follow exercise a good introduction to

machine learning concepts and many other

tutorials that will actually get you

started you don't need to go through the

entire course even by doing the first

four or five modules should actually get

you started kaggle is also a place with

data science competitions where you can

participate and practice what you have

learned in the course your main goal is

not to spend countless hours trying to

get the best possible score ever but

actually to participate in the

competitions you are interested perhaps

for a specific industry and document

everything in your public notebooks a

notebook is where you will code your

models and test your results make sure

that everything is very well documented

because this is going to be your public

portfolio now you need to start thinking

about creating a product or a site

project that will use machine learning

to solve it what problems around you

your school or your job can you solve

now that you know a little bit more

about machine learning how can you use

this technology to solve the problem and

put it in your product don't forget that

the whole point about this scythe

project is not to launch the best

looking product out there but actually

to show your journey identifying the

problems talking to users prototyping

developing and launching a product all

of this can be documented and used

during your networking interviews and

even in your resume it's still going to

be part of your public portfolio that

being said i understand that creating

something brand new from scratch can be

very daunting and you can also feel

overwhelmed so another thing that you

can do is go to a website like angel.com

or product hunt or search for any local

non-profits around you identify the ones

that you are interested in that use ai

and machine learning in their products

and actually find people who work there

on linkedin do some networking talk with

them and finally volunteer as a product

manager for ai and machine learning for

their products now that you have some

expertise of the courses and your public

python notebooks you'll have more

credibility when talking with them and

last but not least there's always

another option and that is to go back to

school and study computer or data

science focusing on ai and machine

learning this option can be pretty

expensive and time consuming but it can

also open doors to other alternatives

besides product management so it's never

a bad decision if you want to put the

time and money to it working in products

with machine learning is very exciting

and i actually hope this video helped

you understand more about what pms in

this space do as well as how to become

one if you like this video and you have

more questions about product management

and interviews you're going to love

these custom playlists that i've made

for you that has my most popular videos

about pm interviews and how to impress

your interviewers with answers to

behavioral strategy estimations and

product case or product design questions

this playlist has actually helped many

job seekers land offers in product

management thank you so much for

watching hit that subscribe button and

i'll see you next time

you