How Data Science Projects Are Implemented Using Agile Methodology🤔 🤔 🤔 🤔 🤔 🤔 🤔
ฝัง
- เผยแพร่เมื่อ 5 ต.ค. 2024
- ⭐ Kite is a free AI-powered coding assistant that will help you code faster and smarter. The Kite plugin integrates with all the top editors and IDEs to give you smart completions and documentation while you’re typing. I've been using Kite for a few months and I love it! www.kite.com/g...
-----------------------------------------------------------------------------------------------------------------------
We at iNeuron are happy to announce multiple series of courses. Finally we are covering Big Data,
Cloud,AWS,AIops,Business Analytics and MLops. Check out the syllabus below.
30 Days Data Science Interview preparation - rb.gy/q1c58g
Big Data Master-rb.gy/xg1ob7
Business analytics-rb.gy/herdd8
Aws Cloud Masters- rb.gy/x5poyj
AI OPS- rb.gy/kionj6
Please note the duration of the batch will be upto 4-5 months. All the sessions will be live and the recording will be available.
To find more info regarding the course please go through the courses.
You can fill the below form to reserve your seats
ineuron1.viewpa...
Incase of any queries you can contact the below number. Happy Learning!!
8788503778
6260726925
9538303385
8660034247
9880055539
-------------------------------------------------------------------------------------------------------------------------
Connect with me here:
Twitter: / krishnaik06
Facebook: / krishnaik06
instagram: / krishnaik06
I understand that the Agile might be used by many companies for implementing DS projects. But this only works when you have something tangible to implement. As a larger part of a DS project might be experimenting with different methodologies it becomes difficult to estimate timeline for a task. In order to deal with this issue during experimentation phase we can take a hybrid approach between Kanban and Agile. So how this would work is during the experimentation phase we plan using Kanban and when we have something tangible to implement and deploy, we switch to Agile framework.
Yes I accept this . . .
You know kanban is Agile right? Agile is an Umbrella and Kanban, xp, scrum are underneath.
Look up data driven scrum
couple of questions:
1. Ideally in agile methodology we do build and test in one sprint itself and not do test of sprint 1 with Sprint 2 build. Is the above process exception for AI based projects?
2. What about the estimations? its not possible to do all 5 stories in one sprint can it? it totally depends on the effort so how do we do estimations in AI based projects?
3. in Data science, if you are in exploration phase then the estimations can be tricky, it will be more of spikes so which agile framework is suggested as I feel scrum might be a failure over here
4. How does one document user stories for data science projects? what are the guidelines.
5. what all testing is done for data science projects?
video which I was thinking from last 3 days 🙏thank you so much👍
Future is using many ML platforms which help you manage it's entire life cycle! Data Pipelines for Continues Training, Continues Deployment, gauging accuracy, and a RestAPI for accessing.. Software engineering is the same but usess different set of tools although some may be used for ML as well.. So basically Kafka /Spark/MapR for data enginering, KubeFlow Mlflow Airflow for CT CD pipelines and model management, SCM for code collaboration, Platform is mostly Kubernetes, Ezmeral, Seldon, etc Container monitoring stack like ELK or Prometheus Grafana stack..
Hi Krish. I taught you would show how the agile process is implemented in data science projects. For example how to come with the stories to develop.
I second that, any links to share about this ?
Thanks Krish for giving an overview of how the projects work in agile methodology in real life project scenarios. To the point and useful videos :)
@krishnaik Please make a video on how to apply for a data science job in the government sector.
Guys pls like if you want this video.
Krish....please make a video on jira tool using data science project..how the tasks, tickets, will happen like, right from data collection to deployment.
Good idea to use Agile in DS. What is the difference of Agile in Data Science compared with Software Engineering?
After production deployment , we need to maintain the application and monitor it , collect monitoring metrics, fix bugs in prod so work doesn't stop after deployment , it actually starts
Agile helps to incorporate client feedback easily 😊 Thanks for sharing 👍
working in tcs requires agile methodology. i am a java developer, but planning to switch in data science inside tcs. thanks for this beautiful help.
Great presentation and even better use of MS Whiteboard. Refreshing
Thank you Krish Naik. very useful information for explaning for students and freshers for understanding the SDLC for developing and ML project.
I belive CRISP DM is more relevant for DS projects than agile
Did you use it?
I've used both, and he is right
Hi krish
This is very much easy to understand.
Please some more videos regarding scrum in data science projects
You’ve described Waterfall using agile terminology.
Sir can you please showcase any of your real job project from start to end if possible?
Mr Krish wonderful explanation . The way you tell is more agile .I am happy to watch ....
Sir pls make one video,for how to use kaggle for beginners like me plzz 🙏🙂
Can u please make a video on MLOps or AIOps. Please make this video.
Please make a series on Explainable AI
Nicely explained Krish, Which software you used to recording this video.
Wow 😲 ... Exactly replica of wat I am doing but role is not developer it's QA
Many thanks for this explanation. It was really helpful!
Sir but the data science project itself have multiple pipelines ?? How pipelines can be mapped with sprints?
Sir I'm Anuj Pathak I wanted to purchase your book on hands on python for finance but it's not available on Amazon
Agile n water fall model r similar.
One doubt that after finding bug in sprint 6.how can we fix it.
Like when. We find bug in sprint 5 ,it will fix with sprint 6.
Pls clarify it.
Scrum teams are self-organizing and cross-functional. While the s/w engineers develops the story , the QA will test it and log defects parallelly. If the defect blocks the story from being marked as done, then the original story is "reopened" and the defect is inserted into the same sprint. If there are defects that are not blocking the story, these are added to product backlog. In the last sprint i.e. 6 in this case all the defects have to be fixed and tested for UAT to be conducted.
As Going through business analyst interview I face tech round of python and sql. How it is related towards the Business analyst role.?
Now people are looking who can understand the data insights too bro.
Sir django video kab aayegaa
Sir aapne kin kin company mee work kiya haai or aap ke salary kitne haii.
Salary is not the right thing to ask..that too on an open platform :P . If you want to know about a DS salary that you can ask but not his salary. @krish nayak has uploaded few videos explaining the salaries in detail for DS roles.
Really great work sir😀
Totally irrelevant content to the topic mentioned. I was looking for how the DS project PBIs will be different than normal application development as there are in features as such in DS projects.
Agile might be the worst choice for ML solutions.
many companies work in that way :)
@@krishnaik06 including mine. But they gotta learn, things generally go different from normal application development. We have faced a lot of issues in the past, may it be re-analysis, restraining of the models.
@@What-kn1sr then u sshould definitely make them understand what problems u faced?
You mean Agile or particularly Scrum? And yes, if we want to use it "as it is" process for DS projects it's very difficult. We need to customize it as per our need and also, better to break DS projects into phases and use different processes for the different stages. More often than not Pgm Manager get stuck with the idea of process so much that they actually nullify the point of agile - which actually says People over the process and working prototype over documentations. Depending upon what problem you are facing in your current company - you can explain the same to your manager. I know usually these people don't listen to because they are used to working in certain way - I have been on the both sides :)
@@vahiagyat management tends to use the "latest" thing, over what rightfully fits in.
Hi Krish, thanks a lot for the video? What tools (HW & SW) are you using for white/blackboarding?
Wonderful thank you
Well explained.
How to start my career as data scientist.
I m 0 at coding but have degree in cs.
Pls guide me
Hello Sushma,
You can follow Krish's Playlist of ML, but do watch the how to learn Data Science smartly first.
👌
'A' class video
@krishnaik Please make a video on how to apply for a data science job in the government sector. Guys pls like if you want this video.