How To Develop Machine Learning Skills In Your Company | Ekeeda
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How To Develop Machine Learning Skills In Your Company?
Everyone is fond of AI Data Science Machine Learning and it’s probably not going to extinct in for a couple of decades. Thus, most people will now have a general idea of what Data Science is and what Machine or AI algorithms can do. It’s quite normal and a common phenomenon in every field of expertise. But do you really know what DevOps, Support, or NOC (Network Operation Center) actually do? Technology professionals can probably explain it better than people who aren’t part of the industry, and it most cases it is pretty difficult to truly grasp what other people do if you’ve never done it yourself.
In fact, in most cases, the additional knowledge might not help you. Data science is quite different from other examples since data is everywhere. It is easier than ever to store and manipulate data, and so Data Science and data-driven decision-making are always meaningful and valuable. Every department in every company can benefit from data-driven decision-making. DevOps can use Data Scientist Machine Learning (ML) algorithms to test their pipelines and detect anomalies, Support can use clustering algorithms to group similar customers’ requests and reduce their workload, and NOC (Network Operation Center) will use anomaly detection algorithms to detect malfunctioning networks. Since everyone will benefit from Data Science, our blog will discuss ways to empower employees in the organization with the best data science skills and spread some data science, love.
Things To Achieve In Data Science Workshop
We as data science professionals decided to create a workshop that will enable anyone with data science python basics skills to quickly get a hold of what Data Science is and to understand where, why, when, and how to use this superbly invented field. We set some goals for it:
We offered “Data Science Ambassadors” outside of our team with tools and a basic understanding of Data Science to work in collaboration.
Made the training short & highly effective so “ambassadors” can do it while on the job.
Started awareness of data-driven decision-making & communicate the advantages of using it
We tried to achieve these goals with the following points
Explain data science to machine learning and the basic algorithms
Demonstrate how to spot machine-learnable problems
Practice hands-on ML using Python and Sklearn
As we taught there would be a data science course online, we scanned through various courses but none of them suited so we intend to create the workshop ourselves. The reason was simple:
Very few courses cover as much material as we wanted in a short time
Most courses aren’t designed to be taught to a class but are meant for self-paced learning
We required theoretical explanations of ML models that could be understood by anyone
So, I’ll explain why we created our workshop and why it is so efficient to teach Data Science to newcomers in a short time.
Explain Data Scientist Machine Learning & Basic Algorithms
As the largest DS team at India's leading tech giant, we meet with different people from many different departments, such as DevOps, Product, Manufacturing Units, and Support, during our projects. One thing we found was that people were struggling to understand how our ML solution fits into the projects and what it can actually do. It was due to the fact that ML concepts were new and people had a hard time understanding how they fit into the project but later, in future projects, these issues didn’t repeat themselves & it became quite easy to explain how our ML solutions could fit into the project. We came to the conclusion that people didn’t struggle specifically with our ML solution, they struggled with ML in general, and once they managed to get hold of the basics, everything got easier.
Demonstration On Spot Machine-Learnable Problems
We are always surrounded by manual tasks no matter what post or position we hold. For the most part, these tasks can’t be automated & definitely needs human interaction. But, there are some tasks that seem like only a human can do, but skilled data scientist machine learning expert can probably create an ML model that can do the job.
Due to priorities, our DS team can only take on so many projects, and usually, these projects revolve around the company’s core products. When we do find some time to work on peripheral projects, we don’t want to waste it sifting through the thousands of manual tasks going on in the company and figuring out which ones can be automated using ML.
Our solution was to train employees from other departments to be Data Science Ambassadors. These Ambassadors will have the expertise to spot the problems which could be solved through ML and then based on the complexity, either create a Model themselves or simply relay the problem to us to add to our backlog.
Practice Machine Learning With Python & Sklearn
Instead of just explaining concepts, we wanted people to actually practice ML because we believe that hands-on practice is the best way to learn. Through this type of learning, participants will understand ML and spot ML-related issues. They could even start thinking about solving these problems themselves. It’ll also allow them to get a taste of Data Science Skills and figure out if that’s something they would like to do more often.
Use Of Presentations
We used online presentation tools and shared the slides to fix/edit them as we go along. We used the slides to explain the core ideas, data science to machine learning concepts, and algorithms in detail without any codes.
Examples Of Codes
We made use of Jupyter Notebooks to show live code examples. Each participant could clone the notebooks from our Git repository and run the code themselves. It helped them understand the different commands and could help them keep after the workshop ended. Since notebooks are common practice for Data Scientists, using a notebook was training by itself. It meant it had an environment set up for them if they wanted to further delve into DS. Thankfully the internet is loaded with a lot of datasets instances that we can use to demonstrate different concepts and algorithms.
Even for exercises, we used Jupyter Notebooks. It allowed us to start participants off with some basic commands for the exercise, such as data loading and letting them focus on the exercise itself. Also, because the code examples were in the same notebook as the exercise, it made it very easy to copy-paste the relevant commands required for each exercise.
On Concluding Note –
To conduct a workshop with good content is great, but you need more than good content to look great for operations!
So, we sent a survey to get feedback about the previous day and improve it on the following day of the workshop. We kept the scaling of 1-5 for it:
Logistics – schedule, refreshments, and breaks
Content quality – How good the exercises and slides were & did you learn anything
Content relevance – how relevant the content is to the participants’ everyday work
Other than this, we made additional efforts to make sure our workshop runs smoothly:
Instant Communication – We co-ordinated with our team on slack and a mailing list
Refreshment – Since the workshop is quite intense, we made sure everyone would stay focused throughout each session, and thus we ordered breakfast and lunch with yummy refreshments such as sandwiches, momos, fruits, and sweets.
Token Of Appreciation – To make our participants feel like a part of the project we gave them t-shirts and jackets