TWO software engineering skills YOU MUST HAVE in 2023

Here are two skills you must have in 2023 to stay relevant in the tech industry. Also, checkout Educative if you are interested in leveling up your engineering skills. Visit the link educative.io/engineeringwithu... to get 10% off the annual subscription.
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[@mrigankabora4835]

Skills - 0:41 - machine learning 5:57 - distributed systems

[@no3lcodes]

To skip ad: It starts at 4:20 and ends at 5:30

[@1anre]

@@no3lcodes how do you mean?

[@chaddha69]

these are great book references for learning distributed computing! Thanks!

[@tamasbalint1597]

Hi Utsav, thank you for your thoughts and for your recommendations. It would be great if you could have 1-2 videos about front-end development. Even you had a conversation with someone who you respect in this domain. Thank you again for this and all the other great videos you have created containing invaluable advice.

[@anieudo5359]

Up vote this comment

[@georgeanton8567]

I absolutely love your book recommendations! I’ve followed through with some of the ones you’ve recommended on previous videos and they were spot on. Thank you so much

[@EngineeringwithUtsav]

Glad you like them!

[@mattmatt2417]

​@@EngineeringwithUtsav Abstract Artificial intelligence (AI) has had a significant impact on society and the way we interact with one another. It has enabled businesses to automate many of their processes, cutting down on the need for human labour, and making work more efficient. This, in turn, has led to a shift towards a moneyless society, as AI has made it possible for people to get whatever they need without having to spend money. The purpose of this paper is to examine the ways in which AI has facilitated this shift and to explore the implications of such a society. Introduction The concept of a moneyless society is one that has been explored by various thinkers and academics throughout history. Many have suggested that such a society would be an ideal one, in which people would be able to get whatever they needed without having to worry about the constraints of the monetary system. However, the idea of such a system has always been deemed unworkable, as it was believed that there would be no way to allocate resources in a fair and effective manner without the use of money. However, with the rise of artificial intelligence, this paradigm may be shifting. AI has made it possible for businesses to automate many of their processes, which has led to a reduction in the need for human labour. As such, people are no longer required to work in order to acquire the things they need, as they can be obtained through other means. This has, in turn, made it possible for people to shift towards a moneyless society. How AI has facilitated a moneyless society AI has led to a significant reduction in the need for human labour, which has in turn made it possible for people to acquire the things they need without having to spend money. There are several ways in which AI has facilitated this shift: Automation of production processes: AI has made it possible for businesses to automate many of their production processes, which has led to a reduction in the need for human labour. This means that products can be produced at a much faster rate and with greater efficiency, which has driven down their cost. As a result, people are now able to acquire the things they need without having to spend as much money. Distribution of resources: AI has also made it possible for resources to be distributed more efficiently. With the use of algorithms, it is now possible to allocate resources in a way that is both fair and effective. This means that people are able to get access to the things they need, even if they don't have the financial means to do so. Sharing and collaboration: AI has also facilitated the sharing and collaboration of resources. With the use of platforms such as Uber and Airbnb, people are able to share their resources with others, which has reduced the need for people to buy their own. This means that people are able to get access to the things they need without having to spend as much money. Virtual goods and services: AI has also made it possible for people to acquire goods and services that are entirely virtual. This means that they don't have to spend money on physical goods, but can instead access them through the internet. This has made it possible for people to acquire things like entertainment and education without having to spend a penny. Implications of a moneyless society The shift towards a moneyless society has several implications, both positive and negative. Some of the positive implications include: Increased equality: A moneyless society would be one in which everyone has equal access to the things they need. This would help to reduce income inequality, which is a significant problem in many countries. Reduced environmental impact: A moneyless society would also reduce the environmental impact of human consumption. Without the need to acquire goods through monetary means, there would be less need for the production of physical goods, which would reduce the amount of waste generated. Increased social connectedness: A moneyless society would also facilitate increased social connectedness. Without the need to worry about money, people would be able to spend more time engaging in social activities, which would help to build stronger communities. However, there are also several negative implications of a moneyless society: Lack of incentive: Without the need to work in order to acquire the things we need, people may lack the incentive to work. This could lead to a decrease in productivity and economic growth. Reduced innovation: A moneyless society may also lead to a reduction in innovation, as there would be less incentive for businesses to invest in research and development. Increased government control: A moneyless society would also require a significant amount of government control in order to ensure that resources are allocated in a fair and effective manner. This could lead to a loss of individual freedom and autonomy. Conclusion AI has made it possible for people to shift towards a moneyless society, in which goods and services can be acquired without the use of money. While this shift has several positive implications, it also has its drawbacks. As such, it is important to carefully consider the implications of a moneyless society before embarking upon such a path.

[@wilhelmngoma9009]

Thanks!

[@ASode]

guitar at the end was heavenly.

[@cohaya1]

I don’t agree with machine learning seems like they only want phd graduates or data science experts. I would recommend DS though

[@gaureesha9840]

That's only for R&D. Most Machine Learning Engineers require programming with Tensorflow, and similar libraries. Integration of existing models with different software applications.

[@suvobrotopal2024]

Very Informative video , Thank you very much , from Kolkata City , India . 🙏

[@dragonmax2000]

@Utsav, Great video, what do you use in your ipad for note taking. I like dark colored style that you have in the pictures. Would appreciate your insight.

[@codation]

Hi Utsav, a great and helpful video, as always. Thanks for the advice and the recommendations! Many Machine Learning courses also require us to be hands-on in high-school Mathematics (esp. Stats & Probability) and Python programming. I'm an experienced programmer in JavaScript. Python is relatively new to me, and now I need to remember the mathematics taught in high school. Can you please make a separate video on Machine Learning, including a complete roadmap (free /paid) and other connected resources? What order should a beginner in Machine Learning need to follow while learning Machine Learning?

[@dragonmax2000]

@arun, take a look at freecodecamp data science roadmap, it has everything and of high quality. I sponsored small part of the process.

[@user-rp6bi5qj1n]

Please tell me from scratch who is hard to learn from devops engineer or software engineer?? What direction do you need to study and know more to become a full-fledged engineer?

[@andreypopov6166]

I am not sure the big data was the reason ML is so popular. A lot of data was there for a decade. The reason is - people discovered the ways/use cases of it application to problem solving.

[@vhenjoseph]

sir do you have a podcast? I’d like to listen and learn while driving

[@nashs.4206]

Dai, embedded software engineering ko barey pani videos banaunu na :)

[@jelonidas7772]

Hey Ustav great video as always! I would also mention blockchain as a 3rd technology which is worth to learn and know, especially in the context of a distributed systems :)

[@1anre]

Blockchain is very important.

[@weirdwesteros1109]

Not that many companies are actually adopting machine learning. It’s really not that necessary as an SE. There’s plenty of projects you can work on

[@1anre]

That’s what you think for now. By next week when it takes off don’t come back crying that you’ve been left behind then.

[@SY27196]

What about Devops? Lot of demand but too less people in market Pls make a video on this too

[@jondinero837]

I always thought your version of “Distributed Systems” in this video was more defined as microservices. For me, distributed systems describes things like Hadoop, Spark, Redis, etc. I feel like what was mentioned in this video is more about scaling microservice architecture, even the course mentioned described it as scalability and system design.

[@EngineeringwithUtsav]

Pieces of the same puzzle. Micro services is a good entry point to get into it and this video essentially is an encouragement to get started with DS, so no point talking about complex architectures… it will just overwhelm people. But eventually to get to massive scale you would need to understand the infrastructures you mentioned.

[@karthick...]

Suggest me book for java & cpp

[@vickyd4807]

DSA for sure just do if not done

[@sammed.sankonatti]

I have a small doubt. If I am a full stock developer, then also I have to move towards Machine learning?? @EngineeringwithUtsav

[@funkdoc2001]

Love the content Utsav, lots of value as always. I had a question, preface i'm a junior developer trying to get a better grasp of the 'big' picture... whats the difference between distributed systems and microservices?

[@rsfllw]

#1 you don't need to know this as a junior but: microservices basically boils down as 'if we can split each individual thing our code needs to do into a separate service, then we can scale each of those things (functions) independently, which probably means we can have tonnes of customers" distributed architecture boils down as "we don't know where most of our customers will be, so lets code things up in a way that we can spin all the parts of the things they need (functions/databases) up without knowing that

[@SanusiAdewale]

This could have been a shorts!

[@davids2540]

Don’t think I’d put machine learning in my top 10 skills to learn. Not saying it won’t be important in the future though.

[@LA-MJ]

I call BS on ML. If you aren't one of the BAFANGs you don't get that slice of the BigData pie

[@1anre]

That’s what you think. Funny

[@sushandahal8957]

I wanted to ask you one question where are you originally from ?

[@1anre]

From heaven

[@ojomudamola6674]

I still don't understand how to make money as a developer

[@sculptscript]

🤔

[@user-wr4yl7tx3w]

Is it data engineer that does the job of collecting data?

[@EngineeringwithUtsav]

Don’t get too hung up on titles. Data engineer could be punching data in one company, but building complicated data pipelines in another.

[@jeromesimms]

Skills to learn: 1. Machine Learning 2. Distributed Systems Watch the full video guys it's very interesting

[@linonator]

Machine learning take seems to be a bit of hype to be honest. I disagree with that take. Is it useful? Yes, but most jobs aren’t going to require that for your normal software developer