AI Career Opportunities for Data Professionals - Time to Pivot?

Summary

In this insightful video, Dave Abelar, founder of Data Lumina, explores the booming AI industry's impact on data professionals. He discusses opportunities across various roles, including data scientists, engineers, and AI specialists, highlighting the need to upskill in software engineering and adapt to new technologies like large language models. Abelar also addresses the current hype cycle of generative AI, suggesting ways to pivot or upskill within the field, and offers guidance for leveraging AI in business strategies and considering freelance opportunities to future-proof careers.

Takeaways

• 🚀 The AI hype is still booming, offering numerous opportunities for data professionals to pivot or upskill their careers.
• 💡 Data professionals are considering whether to pivot or upskill in response to the current market trends and opportunities in AI.
• 🌐 Dave Abelar, founder of Data Lumina, discusses various data roles and their opportunities in the current market, emphasizing the potential for growth and change.
• 📈 The hype cycle of generative AI is currently at the peak of inflated expectations, suggesting a potential upcoming disillusionment phase where the technology's challenges become more apparent.
• 🔍 Despite the public hype dying down, business owners and managers still show strong interest in large language models and their transformative potential.
• 💻 Data scientists can leverage their skills in understanding business cases and data to work with pre-trained models, but may need to upskill in software engineering to implement these models in applications.
• 📊 Data analysts can explore opportunities in monitoring and evaluating large language models, using tools like LangSmith or LangFuse to ensure model performance over time.
• 🔧 Data engineers have a crucial role in providing the foundational data and infrastructure needed for AI models, including building data platforms and optimizing data feeds for AI applications.
• 🤖 AI Engineers focus on prompt engineering, handling chaotic mediums like large language models, and building event-driven architectures, often without needing deep machine learning expertise.
• 💼 Consulting in AI strategy can be a valuable role for data professionals, helping businesses navigate the implementation of AI effectively and safely.

Q & A

What is the current state of the AI hype cycle according to the video?

The video mentions that generative AI was at the peak of inflated expectations last year, and it is likely to be followed by a trough of disillusionment where people realize the technology is not as easily transformative as initially thought.

What is the role of Dave Abelar in the data community?

Dave Abelar is the founder of Data Lumina, where he has been building custom data solutions for clients for the past 5 years. He also runs a community called Data Freelancer, teaching data professionals how to get started with freelancing and scale it to six figures and beyond.

Why might a data professional consider upskilling in software engineering when looking into generative AI?

Upskilling in software engineering is important because when working with pre-trained large language models, the focus shifts from model training to application development. This requires understanding web applications, event-driven architectures, and deployment strategies like Docker containers and cloud services.

What opportunities are available for data scientists in the context of AI and large language models?

Data scientists can explore opportunities in prompt engineering, optimizing AI model outputs, and building applications around pre-trained models. They can also leverage their skills in data analysis and model evaluation to work on generative AI projects.

How can data analysts contribute to the monitoring and evaluation of AI applications?

Data analysts can use tools like LangSmith or LangFuse to create dashboards for monitoring AI applications, ensuring the performance of these applications does not decrease over time. They can also create evaluation datasets to assess model performance with different prompts.

What is the significance of data infrastructure for data engineers in leveraging AI opportunities?

Data engineers play a crucial role in establishing a solid data platform that feeds the AI models with the right data. They can focus on building robust data pipelines, working with vector databases, and ensuring data is well-prepared for AI models to use effectively.

What are the key skills an AI engineer should focus on according to the video?

AI engineers should focus on prompt engineering, tolerance for working with chaotic mediums like large language models, chaining agents, reactive UIs, and event-driven architectures. They should also understand software engineering fundamentals more than deep learning expertise.

What opportunities are there for machine learning engineers in the field of generative AI?

Machine learning engineers can explore optimization techniques for large language models using libraries like DSPI and TextGret. They can also focus on monitoring and operational aspects of AI models, leveraging their existing knowledge in machine learning to work with generative AI.

What consulting opportunities are available for data professionals in the realm of AI?

Data professionals can offer AI strategy consulting, helping businesses understand how to effectively, safely, and reliably use AI. This includes creating AI strategy roadmaps, addressing data management, technology infrastructure, and capability development within a company.

How can data professionals capitalize on the current AI hype to pivot their careers?

Data professionals can explore internal opportunities within their current roles, seek new job opportunities focusing on AI, or start freelancing to gain experience with generative AI. They can also consider consulting to help businesses navigate AI implementation.

Outlines

00:00

🚀 Opportunities in Data Careers Amidst AI Hype

Dave Abelar, founder of Data Lumina, discusses the booming AI industry and its impact on data professionals. He outlines the various roles such as data analysts, scientists, engineers, machine learning engineers, and AI engineers, and the opportunities available in the current market. Dave aims to provide insights to help viewers decide whether to upskill or pivot in their careers, possibly towards generative AI or freelancing. He shares his expertise based on hands-on experience and a professional network, emphasizing the ongoing interest from business owners despite a dip in public hype.

05:01

🔧 Upskilling for Data Scientists in the AI Era

The script highlights the need for data scientists to upskill in software engineering to leverage large language models (LLMs) effectively. Traditional data science focuses on modeling, but with pre-trained LLMs, the emphasis shifts to application development. Data scientists are encouraged to learn web application building, working with web hooks, event-driven architectures, and containerization for deployment. Practical skills like prompt optimization and using libraries for robust LLM applications are also discussed, positioning data scientists for new opportunities in AI engineering or freelancing.

10:03

📊 Data Analysts: Monitoring and Evaluating AI Applications

Data analysts are presented with opportunities in monitoring and evaluating AI applications, particularly large language models. The script suggests learning tools like LangSmith or LangFuse for dashboard creation and model performance evaluation. It also mentions the importance of creating evaluation datasets to prevent model drift over time, aligning with the existing skill set of data analysts and opening avenues for involvement in the AI space.

15:04

🌐 Data Engineers: Building Foundations for AI

Data engineers are poised to capitalize on the AI hype by focusing on data infrastructure, which is crucial for feeding and managing LLMs. The script discusses the need for solid data platforms, the use of vector databases, and technologies like Pinecone, Weaviate, and Milvus. It also touches on data enrichment using LLMs and the potential of open-source models to reduce costs, positioning data engineers as essential in enabling AI adoption.

20:06

🛠 AI Engineers: The New Role in AI Implementation

AI engineers are described as a new role that overlaps with machine learning experts and full-stack engineers, focusing on prompt engineering, tolerance for chaotic mediums like LLMs, and building reactive UIs and event-driven architectures. The script emphasizes that deep ML expertise is not required, and software engineering fundamentals are more valuable. It also discusses the importance of understanding full-scale end-to-end solutions, robust application monitoring, and the strategic placement of AI within a company's architecture.

🤖 Machine Learning Engineers and the Optimization of AI

Machine learning engineers are encouraged to explore libraries like DSPI and TextGret for optimizing large language models using backpropagation, similar to neural network optimization. The script highlights the importance of post-production model monitoring and the unique advantage machine learning engineers have in understanding these processes. It also suggests considering a move towards AI strategy consulting to help businesses navigate AI implementation effectively.

💼 Navigating Career Pivots in the AI Landscape

The final paragraph discusses personal strategies for capitalizing on the AI hype, whether through internal pivots within one's current job, seeking new job opportunities, or starting a freelancing side gig. It emphasizes the importance of understanding one's personal situation and leveraging the current skills to explore generative AI projects. The script also promotes a training program for data professionals interested in freelancing and ends with a call to action for viewers to learn more about Data Lumina Solutions' approach to AI project delivery.

Keywords

💡AI Hype

The term 'AI Hype' refers to the widespread excitement and attention surrounding artificial intelligence technologies, particularly in the media and public discourse. In the video, it is mentioned that despite the hype potentially dying down for the general public, business owners and managers still show strong interest in AI, especially in large language models, due to their transformative potential.

💡Data Professionals

Data professionals encompass a range of roles, including data analysts, scientists, engineers, and those involved in machine learning and AI. The video discusses various opportunities available to these professionals in the current AI market, suggesting they might consider upskilling or pivoting their careers to align with the latest trends.

💡Upskilling

Upskilling is the process of acquiring new skills or improving existing ones to stay relevant in a changing job market. The script encourages data professionals to consider upskilling in areas such as software engineering or understanding large language models to capitalize on opportunities in the AI field.

💡Generative AI

Generative AI refers to artificial intelligence systems that can create new content, such as text, images, or music. The video discusses how generative AI, and AI in general, presents new career paths and opportunities for data professionals, such as working on projects that involve these innovative technologies.

💡Data Analysts

Data analysts are professionals who collect, process, and interpret data to help businesses make decisions. The script mentions that data analysts can explore opportunities in monitoring and evaluation of large language models, which is crucial for ensuring the reliability and correctness of AI applications.

💡Data Engineers

Data engineers are responsible for building and maintaining the infrastructure that supports data analytics. The video highlights the importance of data engineers in preparing data for AI models and the potential for them to work with new technologies like vector databases and AI-specific platforms.

💡Machine Learning Engineers

Machine learning engineers develop algorithms and systems that enable computers to learn from data. The script suggests that these engineers can leverage their skills in optimizing and working with large language models, as well as monitoring these models once they are in production.

💡AI Engineers

AI engineers are a new category of professionals who specialize in working with AI technologies, particularly focusing on prompt engineering and the application of AI models in various contexts. The video describes the role of AI engineers as not requiring deep machine learning expertise but rather a focus on software engineering and practical application of AI.

💡Freelancing

Freelancing refers to the practice of working independently, often on a contract basis, for different clients. The script encourages data professionals to consider freelancing as a way to explore AI opportunities, gain experience, and potentially earn extra income while testing the waters of the AI job market.

💡Monitoring Tools

Monitoring tools are software applications used to track the performance and health of systems, including AI models. The video mentions specific tools like Lang Smith and Lang fuse, which are important for data professionals to learn in order to effectively monitor and maintain AI applications.

💡Event-Driven Architectures

Event-driven architectures are a type of software design pattern where the flow of the program is determined by events such as user actions or messages from other systems. The script points out the significance of understanding and implementing event-driven architectures for AI engineers working on generative AI applications.

Highlights

The AI hype is booming, presenting numerous opportunities for data professionals.

Data professionals are considering career pivots and upskilling in response to the AI market.

Dave Abelar, founder of Data Lumina, discusses various data roles and their opportunities in the current market.

Generative AI is at the peak of inflated expectations, likely to be followed by a trough of disillusionment.

Public interest in generative AI is waning, but business interest remains high.

Data scientists can pivot towards AI engineering by upskilling in software engineering.

Data scientists should focus on understanding how to build web applications and work with web hooks.

Data analysts can explore opportunities in monitoring and evaluating large language models.

Data engineers have opportunities in building data platforms and pipelines for AI models.

AI engineers should focus on prompt engineering and building event-driven architectures.

Machine learning engineers can leverage their skills in optimizing large language models using libraries like DSPI and TextGret.

Consulting in AI strategy can be a viable option for data professionals interested in advising businesses on AI implementation.

Data professionals can consider freelancing to explore AI opportunities while maintaining their current positions.

Data Lumina Solutions offers a training program to help data professionals start freelancing in AI.

Data professionals should consider pivoting within their current roles or exploring new job opportunities in AI.

Event-driven architectures are crucial for AI applications, requiring skills in software engineering.

Transcripts

the AI hype is still booming and there

are so many opportunities for data

professionals right now and if you are

one probably over the last couple of

months you have thought to yourself at

least a couple of times should I pivot

should I upskill what should I do with

my career where do I want to go so what

I want to do in this video I want to go

over each of the various data roles so

analysts scientists engineers machine

learning engineers and AI engineers and

I want to talk about the opportunities

that I see for each of those roles with

in the current market right now and my

goal with this video is to hopefully

give you some ideas to figure out

whether right now would be a good moment

to upscale in a certain direction maybe

pivot within your current role maybe

look for a completely new role or maybe

even start some Freel like freelance

gigs on the side to see if generative AI

or AI in general is the route that you

want to go with your career and now for

those of you that are new here my name

is Dave abelar I'm the founder of data

Lumina and I've been building custom

data Andi solution for my clients for

the past 5 years already and next to

that we also run a community called Data

freelancer where I teach data

professionals how to get started with

freelancing and then scale it to six

figures and Beyond and because of my

position in the market right now having

uh both hands-on experience working with

clients and talking to a lot of business

owners and also having that Professional

Network around me of lots of Freelancers

I would say have a pretty good pulse on

what's currently going on in the market

what what's hot what's trending what

companies are looking for and also does

what kind of skill sets as a data

professional do you need and before we

dive in let's quickly talk about the

current hype cycle of generative Ai and

where things are at right now to also

assess for you where you should Mo move

towards and to talk about this I I'm

going to pull up a picture I have it

here on my laptop but I will put it here

on the screen this is the hype cycle

from Gardner that all new technologies

follow and last year you can see Gardner

Place generative AI at the peak here of

inflated expectations meaning that it is

likely going to follow with uh or is be

followed by a Thro of disillusion

disillusionment where people are

figuring out okay the technology it's

not as transformational as we thought it

was going to be it's actually pretty

hard to to implement and where we are

currently at on this hype cycle I can

tell you for sure the only the only data

that I can provide personally uh for you

is for example if you look at uh if I

last year would create a video on a new

like General AI framework or giup

repository it would easily get like

100,000 views if I create the same video

right now it will probably get only like

5,000 or so views so that's kind of like

a 20x in the amount of attention the

amount of people that are interested in

this but then on the other hand if we

look at data luminous Solutions or

development company we still have an

ever increasing amount of inquiries that

from from potential clients from leads

that want to work with us and are are

looking for a custombuilt data Ori

solution so the O I would say the

overall hype is dying down a little bit

for the like general public but if you

look at business owners

managers there the the sentiment is

still pretty pretty high pretty strong

with regards to large language models

and the opportunities that are there and

also for a good reason this technology

is still transformational but it's also

challenging to Implement at scale but

that's where we as data Professionals of

course come in so with that out of the

way let's now talk about these

opportunities and like I've said I'm

going to split these up into the various

data roles now um I will also link time

stamp so you can maybe if you're a data

scientist for example you can jump to

the data science part Etc but I think it

could also be interesting to um or I

would definitely recommend watching all

of these and not just to get fuse on

this video but it might trigger some

ideas on how a certain skill set can be

leveraged in this AI hype that's going

on so let's start with data scientists

and let me Begin by stating that the

traditional role of a data science

working on traditional classical machine

learning problems is still there's still

so much demand for that probably even

more than generative AI so please also

don't forget that but given this new

technology and given large language

models there are various new

opportunities that are interested uh

interesting for data scientists and you

also see this trend there's also this

nice meme I will put it on the screen

here of data scientists rebranding

themselves towards AI engineers and I'm

personally guilty of that like for sure

I've been trained as a data scientist

but right now what I spot right uh right

now especially for my business I enjoy

uh I enjoy working on generative AI

projects more than working on classical

machine learning projects we still do

those as well but there is just a whole

lot there are just much more

opportunities right now and also if you

look at the skill set of a data

scientist and how you are trained it

aligns very well with what you can do

with these these new technologies but

there are some caveats here and that is

you probably need to upscale on software

engineering because while data

scientists uh previously data science is

is much more focused on the modeling

part right asking the right questions

getting the right data getting to that

final model that works for your specific

use case now when we take large language

models the model is already pre-trained

so this powerful engine is already here

and the the like the same mindset and

skill set early on in the projects of

figuring out uh the business case

figuring out the true problem getting

the right data that is all still the

same but the whole modeling part is now

very much different because the model is

already there so you almost immediately

jump to putting it into putting it into

an application create creating an

application around that already existing

model and like I've said what that means

in in practice is that you need to

understand how to build web applications

you need to understand how to for

example work with with web Hops and

triggers to build event driven

architectures you probably need to

understand if you put all of this

together how to put this into for

example a Docker container and then put

it on a server or run it in a container

app on Azure or AWS those are now all

skills that you need in order to really

put these applications into production

whereas before it was maybe just your

machine learning model that you would

make available and then another team

would create an application around that

so I would say that is the most

important skill and therefore also

opportunity that you could look into if

you want to explore generative AI so the

software engineering part is definitely

the I would say the most important one

to look into but next to that you also

have more practical things like

understanding how rack works how to

build rack pipelines how to optimize

prompts how to work with libraries uh

like instructor and penic in order to

improve the robustness of your llm

applications those are all things that I

would say really naturally fit into the

core skill set of a data scientist and

then if you if you really master that

and then also look into infrastructure

and building web application

you set yourself up for a lot of new

opportunities whether that's inside your

current role an entirely new role where

you could look for AI engineering

positions for example or like I've said

even Explore some freelancing gigs on

the side to really figure out if this is

the direction you uh you like to learn

more about these Technologies and of

course maybe make a little bit of extra

money on the side all right and then

next let's talk about data analysts and

one big opportunity that I see right

here for for data analyst is it has to

do with large language models monitoring

and evaluation and why does this maybe

something you shouldn't immediately

think about what you see in the real

world right now is that putting these

large language models into applications

at scale it it becomes really tricky

it's really tricky to monitor them and

to make sure that the answers stay

correct that the applications keep

running and I think this could be an

interesting opportunity for data

analysts to look into tools like for

example Lang Smith or an open source

version Lang fuse and figure out and

learn these tools and understand how you

can use these tools to create simple

dashboards to monitor llm applications

then next to that you can also look into

creating evaluation data sets so how you

can evaluate various versions of models

uh in combination with various promps to

ensure that the performance of these

applications is not decreasing so you

have a constant evaluation to this it's

also something you have with machine

learning it's called Model drift over

time you always have to keep monitoring

your model same is true with large

language models with the model fions

with the prompts all right and then next

let's talk about data engineers and

there are so many great opportunities

for data Engineers right now because

while large language models are

pre-trained there is no training

required typically if you go fine

shooting at Route but typically you can

use the models out of the box but still

you need data to feed those models you

need prom to feed those models and

especially the combination and with that

what you see right now is there AI is

fancy AI is what companies want right

they want the fancy model they want the

automation but what they don't

understand is that in order to fully

leverage this technology they need those

foundations in place they need good data

and whatever and this of course depends

on the size of the company but this

could even mean for larger organizations

that you need a solid like data platform

you need everything in place in order to

then unify that and make it uh available

to these AI models and there is so much

work to be done in that area where data

Engineers can really leverage that and

position themselves as data Engineers

that help companies to basically enable

them to use Ai and a little more

practical also of course here you can

focus on rack pipelines and then more

specifically getting to some more

advanced algorithms and techniques like

query expansion uh self query hybrid

search reranking those are all things

you could look into then of course you

have the vector databases for example

you have pine cone we8 quadrant you

could look into PG factor or PG Factor

scale those are all entirely new

technologies and platforms that you can

look into as a data engineer to expand

that skill set that you already have

again minimizing the effort required in

order to learn something new and learn

new skill and take on uh a whole set of

new projects and next to that if you're

working for larger organizations you

have platforms like data bricks or

snowflake or uh just the data uh

database tools that are available in the

major Cloud providers like Azure AWS or

Google those are all technologies that

really fit well into that whole

Narrative of getting your data first in

the right place maybe having like bronze

silver gold layers um getting that data

ready for these AI models to use next to

that one other thing also data data

enrichment using large language model so

generating metadata using llms on Big

Data is also really interesting it can

get really expensive depending on the

size and this is also I think where open

source models are going to play a big

role in order to make that cheaper uh

and maybe don't need as much power just

to for example create simple tags uh to

add some additional element to that data

all things that you can look into as a

data engineer to capitalize on this AI

hype and then let's talk about the AI

Engineers which is a relatively like new

term that has been coined even though AI

has already been around for years but

what you see right now online is that

the AI Engineers are kind of like

positioned like this and let me actually

pull up an an article here um I will I

will put it on the screen this is about

how to hire AI Engineers but I recently

came AC Ross this and they have this

very cool diagram over here how an AI

engineer what what kind of like the

focus is and they highlighted here as as

an AI engineer you focus on prompt

engineering fa to tolerance for a

chaotic medium AKA large language models

chaining agents reactive uis and event

driven architectures and this also uh

illustrates that there's a lot of

overlap with uh machine learning experts

and full stack Engineers lot of overlap

there and I think also one interesting

point is that deep ml expertise is not

required and I definitely agree with

that if you want to focus on AI

Engineering in the typical role that I

just described working with large

language models you don't need uh deep

learning or machine learning expertise

you don't really also don't need any

like statistics or math knowledge it

could help but really truly you don't

need it because these models are already

pre-trained and you're much better off

learning more about software engineering

fundamentals than it uh than learning

math or statistics and with this new

role what you see is of course it builds

on top of everything that we've already

discussed so everything that I've

discussed is also relevant for an AI

engineer to understand but something uh

important here to consider is if you

really position yourself as an AI

engineer and also being able to for

example deliver full scale end to-end

Solutions what you should figure out is

we are

we're right now we're past the phase

where companies are just okay with

spending lots of money on proof of

Concepts because that was last year it

was really the case like every like

major company would just have an R&D

budget was like go do something with

this and right now what we see people

have of companies have spent that money

and right now what they want is they

want real business value so they

actually want something that works in

production and in order to do that you

first of all you need experience because

it's it's challenging to to do this

properly and you need to understand a

whole lot of Concepts that can help you

to make these uh applications using

large language models more robust so it

becomes really important to understand

proper Arrow handling proper monitoring

large language models Ops using the the

tools that I've uh just described so

Lang fuse L Smith those kind of

monitoring tools putting libraries like

instructor to work to leverage for

example uh penic data models for for

validations um but then also considering

all of that and then looking how that

fits in the overall architecture of the

company and where we could eventually

like put this into production whether

that's on a server using Docker or using

some resource within some kind of cloud

provider these are all questions and

things that you have to consider and

skill sets that you need to understand

if you really want to become an AI

engineer in precision yourself as

someone that can really deliver these

Solutions end to end and one other thing

that I would like to elaborate on is

this notion of event driven

architectures and this is why this is so

important is that most of the generative

AI applications are event driven meaning

there is some kind of like application

that is waiting for a trigger waiting

for a response waiting for an event

either from a user or from a process and

that triggers the whole pipeline so

think about for example a customer care

automation solution the application is

running in the background and whenever

there is a new ticket coming in that

ticket has information and the AI needs

to process that information in order to

for example then generate an automated

reply which is then sent back to the

ticketing system so this is event driven

and you can set this up in your web

application but you need to be able to

handle this at scill so that maybe means

putting uh workers or cues in place like

celery and being able to handle all of

that traffic correctly at scale monitor

that oversee that so event driven

architectures uh like I've said it's

much more in the software engineering

side really important to to look into

understand uh how to build those

understand the design patterns that you

can leverage here really interesting

stuff I really enjoy learning and Diving

more into that all right and then let's

get into the machine learning engineer

now here again there's a lot of over lab

with the data engineer and the data

scientist but I think given the the

skill set of uh classical machine

learning engineer there are some other

opportunities that that could work well

for this role and that has a lot more to

do with the optimization part of the

whole process of working with large

language models and two particular

libraries that are really interesting to

look into are dspi and text Gret which

are both libraries I believe from

Stanford researchers and they both based

around the idea of of back propagation

uh about similar to how pytorch is set

up to optimize neural networks but now

you can do so with large language models

using prompts using text and both of

these libraries are really interesting

to look into they have a slightly

different angle but they are both uh

created around the idea of optimizing

large language models similar to how you

would do with neural networks so I think

that's an a unique advantage that really

ties into the skills and the knowledge

that you already have as a machine

learning engineer but then also really

of course what happens after you put the

model into production monitoring it so

again here the whole llm llm Ops comes

into play monitoring so depending on

what you like depending on how much

experience you for example have with

monitoring machine learning algorithms

monitoring large language models is I

would say relatively similar although

much more chaotic so that is something

you have to adjust for that is something

you uh you have to consider other than

that really great opportunities for

machine learning Engineers as well they

have a great skill set to really thrive

in this AI hype and then one last thing

I want to talk about is Consulting

within the realm of AI and I think this

is something any data role can

potentially do if you're interested in

that if you like that because what you

see right now business owners companies

they have a lot of questions they want

to know how AI can be used effectively

safely reliably without sharing data

with open AI all of those questions they

they want to get answers to that and so

there is a lot of uh room for AI

strategy Consulting and what you could

think or offer for example would be to

create AI strategy road maps and within

such a road map you we're building one

right now for a company you could really

think about so the Strategic use of AI

data management and governance the

technology infrastructure skills and

capability development are really about

the talent that they uh need within a

company in order to um Implement AI at

scale effectively you can talk about

change management risk management

management budgets and fenders those are

all things that you can talk about and

help companies with now if you're

currently a data professional and you

work for example in a large organization

and you maybe already see some of the

things that are going on you can take

all of that information and maybe talk

to small business owners where of course

the landscape the situation is very

different but also uh since it's smaller

usually a lot simpler so you can use all

of that that knowledge and even start

some Consulting gigs on the site and

help companies like that and the cool

thing about starting with that is that

by starting as in with a consulting job

or Consulting role for example you get

to know these these companies and their

pain points and their challenges and

also their opportunities and then that

could open the door to then maybe

position yourself as also the expert to

come and implement this so those are all

things that you can consider and this is

much more for really if you want to do

things on the side probably not so much

within your own role but hey who knows

all right and with those opportunities

covered let's now talk about whether

it's time for you to piot how you can

capitalize on this current AI hype and

whether or if you shoot and of course

this answer is going to really depend on

your personal situation but really my

goal here is to present you all of these

opportunities and help you think outside

of the box to see what's possible here

and also really think about that for a

lot of or almost all of the data roles

there is something that is I would say

right around the corner so meaning it it

fits really well into your current skill

set you maybe just have to look up a few

tutorials and then you could probably

take on a project within uh that

particular like area Direction in

generative Ai and because of that

possibility there are a lot of things

that you can potentially do so you have

of course pivoting within your own

current job so you could figure out okay

my company my role right now do I like

it do I want to pursue generative AI do

I want to move towards that direction

see if there are opportunities talk with

your manager talk with your boss take on

an internal project even an innovation

project R&D project figure out if you

can uh move that way to see if you like

it I think it can almost uh help any

company really literally every company

could can benefit from generative AI if

implemented correctly that's what I

believe so go and search for that

opportunity now if you feel like this is

really so exciting and my current role

I'm not sure I don't really like it

anymore I feel like I'm kind of like

stuck then what you could of course

explore is looking for an entirely new

uh job opportunity somewhere else where

you could do more with this new

technology but but that's of course is a

big step and then another thing you can

also of course try is start freelancing

on the side to maybe take on some small

projects and work with this generative

AI technology to see if you like it

maybe to learn more about this maybe you

get paid to learn even if you just like

start out small and this could be as as

simple as helping out a friend or a

family member with a simple simple

application maybe even starting out for

free and then maybe start charging later

on once you've proved that you can do

this maybe you got some testimonials so

that could also be an excellent IDE to

learn more about this technology to

Future proof your career while still for

example keeping the the safety and the

security of your current position and

now also if that's something you're

interested in you could check out the

first link in the description like I've

said I help dat the professionals to do

this we have a dedicated training

program to literally get you up and

running in 60 days it will teach you

everything you need to know in in order

to get started and land your first

paying client as a data professional so

if you want to check that out first link

in the description all right and that

wraps up this video if you found it

helpful please leave a like and also

consider subscribing and then if you

want to learn more about how we a data

Lumina Solutions find build and deliver

these generative AI projects all the way

from the beginning to the whole like

architecture check out this video next

where I will go over that entire process

that we use inside our company right now