How to Build a Successful Machine Learning Team?

How to Build a Successful Machine Learning Team?

Build-a-Successful-Machine-Learning-Team

 

Machine learning has become the tech scene influencer right now, and you only have to look at how much the companies of all sizes are investing in this technology to see how vital a role it’s going to play in our future lives – both personally and professionally. As per one of the Gartner’s prediction, “By the year 2020, consumers will manage 85% of their relationships with organizations without interacting with humans”. With 20% of the C-Suite already using machine learning, businesses are planning to grow their teams with Machine Learning experts. But an excellent ML team isn’t just about the engineers; it’s a different combination of talents and perspectives. If you’re one of those planning to build a successful Machine Learning team, here we will help you grow your organization.

 

Standard Job Roles within Machine Learning Teams

 

Before creating a team, you need to know the current job roles that are best suited in a Machine Learning team. Usually, Machine Learning teams consist of engineers, scientists, analysts, and managers. Below we have listed down the individual responsibilities of each team member.

 

  • Data Engineers – Data engineers build and maintain the “big data” infrastructure needed for data modeling, predictions, and analysis that is later verified by data scientists.
  • Data Scientists – Data scientists are analytical data experts with an ability to solve complex problems using data-driven techniques. They take specifications from product leads to understanding the business objective.  They are mainly responsible for gathering massive amounts of disorderly data and changing it into a more usable format.
  • Data Analysts –Analysts are responsible for monitoring processes and production model performance, plus evaluating data quality.
  • Machine Learning Engineers – Having a background and skills in applied and data science, and intense coding, these experts execute the operations of an ML project and are responsible for running the data pipelines and infrastructure needed to conduct code to production.

 

Now, if you are trying to build a great Machine Learning team, you need to understand the necessary skills required. The ideal ML team members should be proficient in understanding the wide range of algorithms and applied mathematics, plus they must have analytical and problem-solving skills. Along with this, in-depth knowledge of some statistics and programming languages is a must. We will further explain the list of skill sets required. But before that, you must understand that knowing some programming languages isn’t enough.

A Machine Learning expert should understand how to build end-to-end machine learning solutions to existing problems. That means along with the curation of data; they need to absorb it, explore it, and cleanse it. Besides, they need to train and assess it, iterate on it, and then correctly execute it. Only then, they can claim to be the masters of Machine Learning.

When you hire member in your machine learning team, it’s essential that you know who can do research for ML and who can apply it to your business challenges. For a stronger team, hire brilliant programmers who can make use of existing libraries and frameworks, but can overcome inherently ambiguous data science.

 

Here Is a List of Primary Skill Sets Required

Here-Is-a-List-of-Primary-Skill-Sets-Required

1. Python/C++/R/Java: In a Machine Learning team, the members need to learn all these programming languages. Python and C++ help in speeding up the code, whereas R is necessary for statistics and plot. Besides, Hadoop is Java-based, so you may need to apply mappers and reducers in Java.

2. Probability and Statistics: Theories are essential for learning about algorithms. Some best examples are; Gaussian Mixture Model, Naive Bayes, and Hidden Markov Models. The team members need to firmly understand the Probability and Statistics to grasp these algorithmic models.

3. Algorithms and Applied Mathematics: Standard implementation of ML algorithms and knowing its working can help you discriminate supervised learning models. For that, you need to thoroughly study subjects such as convex optimization, gradient descent, partial differential equations, Lagrange’s theorem, quadratic programming, and more.

4. Distributed Computing: Usually, ML teams require working with large data sets. They can’t operate this data using a single machine, so it should be distributed across the whole cluster. To make the process easier, you can use Amazon’s EC2 and Apache Hadoop.

5. Expertise in Unix Tools: Your team members must have the knowledge in Unix tools such as grep, cat, fund, sort, se, tr, cut, head, tail and more. Since all the processing is on a Linux-based machine, professionals must have access to all these tools. Hence, it’s essential to learn the functions and use those.

 

Added Skills

 

Just having this technical expertise isn’t enough to make your team a successful one. It must stay up-to-date with the imminent transformations. That means the members should be well aware of the news about development tools, theories, and algorithms. For this, they can read papers like Google File System, Google Bigtable, and MapReduce plus several online books for ML.

 

Conclusion

 

We hope the above points help you build a greabout creating data science solution for a specified business problem. However, hiring Machine Learning experts can be costly and requires a lot of work. As the demand is more and resources arat Machine Learning team that takes your project to the next level. The best team members not only know the techniques to develop models and extract data from insights but also have the comprehension e less.

To overcome these challenges, several online marketplaces like RemotePanda are providing a cost-effective solution. We, at RemotePanda, help you conveniently connect with Machine Learning contractors. Hire from our wide range of resource base who can help you successfully build your next critical ML project.

 

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