How to Break into AI Product Management without experience
Summary
TLDR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
🤖 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。
🏠 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。
🚀 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。
Mindmap
Keywords
💡产品管理
💡AI和机器学习
💡数据科学家
💡技术栈
💡用户体验
💡模型
💡特征
💡算法
💡数据清洗
💡度量评估
💡产品特性
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
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