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Five Competencies Required to Become a Machine Learning Engineer
Do you have a passion for machine learning? It’s not just you! Each day, more people express interest in machine learning. It’s actually difficult to find a field that is more in the news right now than this one.However, becoming interested in machine learning is one thing; beginning actual work in the field is quite another. You can learn more about the general mentality, the particular talents and competencies you’ll need to become a machine learning engineer from this page.
Since AlphaGo defeated the top Go player in the world in four out of five games in 2016, machine learning’s penetration into our culture has captivated our attention.
Since then, there has been no turning back for machine learning technology or the possibilities it presents. With a typical annual income of $125,000, it is currently the third-most sought-after artificial intelligence position in the United States, according to Indeed.com.
If you’re interested in learning machine learning skills to enter this field, your moment is now.
What is a Machine Learning Engineer?
To start, if you’re thinking about becoming a machine learning engineer, there are two extremely crucial things you should know.
It’s hardly a “pure” academic function, to start with. You don’t necessary need to have academic or research experience.
Second, experience in data science or software engineering is insufficient. Ideally, you want both.
The distinctions between a data analyst, data scientist, and machine learning engineer must also be understood. The primary difference, put simply, has to do with the final result.
As a data analyst, your goal is to use data analysis to convey a story and provide your team with actionable insights.
Humans conduct the analysis and communicate it to other human beings, who may subsequently use the information supplied to make business decisions. Your work’s “audience” consists of people.
Instead of the analyses or visualizations you might have to produce along the way, as a machine learning engineer, your final “output” is working software, and your “audience” for this output frequently consists of other software components that operate autonomously with little human oversight.
Although in the machine learning paradigm the decisions are made by machines and have an impact on how a product or service behaves, intelligence is still designed to be actionable.
This is why a job in machine learning requires a strong foundation in software engineering.
A data scientist straddles these two realities.
They need to be skilled in software engineering in order to gather, organise, and clean data for analysis and machine learning to extract insights.
Their ability to communicate effectively is essential to their success.
Understanding the Ecosystem is Key to Being a Successful Machine Learning Engineer
There is one more notion to cover before diving into specific skills. Understanding the full environment that you are building for is essential for machine learning engineers.
Consider that you work for a grocery chain that wants to start issuing targeted coupons based on information like customers’ historical purchase histories in an effort to provide coupons that consumers will actually use.
You may gather purchase data, do an analysis to identify trends, and then suggest tactics using a data analysis model.
Writing a programme to generate automated coupons would be the machine learning strategy.
But what is required to create that system and make it function?
You need to comprehend the entire ecosystem, including the point of sale and CRM software, as well as the inventory, catalogue, pricing, purchase orders, and bill production.
In the end, the approach is less about comprehending machine learning algorithms—or when and how to use them—and more about realizing the systemic interrelationships and creating software that will properly integrate and interface.
Do not forget that machine learning output is truly functional software!
What Does It Take to Be a Machine Learning Engineer?
Now, let’s get into the real details of what it takes to be a machine learning engineer. We’re going to part this into two primary sections:
- Languages and libraries
We’ll begin with the skills here, then in a follow up post we’ll address languages and libraries for machine learning.
So, what skills exactly i need ?
1. Computer Science Fundamentals and Programming
Computer science fundamentals important for machine learning engineers include:
- Data structures – stacks, queues, multi-dimensional arrays, trees, graphs, etc.
- Algorithms – searching, sorting, optimization, dynamic programming, etc.
- Computability and complexity – P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc.
- Computer architecture – memory, cache, bandwidth, deadlocks, distributed processing, etc.
When programming, you must be able to apply, implement, adapt, or address them (as necessary). Practice exercises, coding contests, and hackathons are excellent ways to improve your abilities.
2. Probability and Statistics
Many machine learning algorithms are based on a formal characterization of probability (conditional probability, Bayes rule, likelihood, independence, etc.), as well as techniques derived from it (Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc.).
These are ways to deal with uncertainty in the real world.
The study of statistics, which offers various measurements (mean, median, variance, etc.), distributions (uniform, normal, binomial, Poisson, etc.), and analysis techniques (ANOVA, hypothesis testing, etc.)
That are required for creating and validating models from observed data, is closely related to this.
Numerous machine learning techniques are essentially advanced statistical modelling techniques.
3. Data Modeling and Evaluation
To uncover valuable patterns (correlations, clusters, eigenvectors, etc.) and/or anticipate characteristics of previously unobserved occurrences, data modelling entails estimating the underlying structure of a given dataset (classification, regression, anomaly detection, etc.).
The technique of continuously assessing a given model’s performance is crucial to this estimate process.
You must select a suitable accuracy/error measure (such as log-loss for classification, sum-of-squared-errors for regression, etc.)
And an evaluation approach based on the task at hand (training-testing split, sequential vs. randomised cross-validation, etc.).
Understanding these measurements is crucial even when using only basic algorithms because iterative learning techniques frequently directly use subsequent mistakes to modify the model (e.g. back-propagation for neural networks).
4. Applying Machine Learning Algorithms and Libraries
Standard machine learning algorithms are widely available through libraries, packages, and APIs.
Such as scikit-learn, Theano, Spark MLlib, H2O, and TensorFlow, but effectively using them requires selecting an appropriate model (decision tree, nearest neighbour, neural net, support vector machine, ensemble of multiple models, etc.).
A learning method to fit the data (linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-selection techniques), and the data.
Additionally, you must be aware of the relative benefits and drawbacks of various strategies.
The countless traps that can catch you off guard (bias and variance, over-fitting and under-fitting, missing data, data leakage, etc.).
Kaggle challenges in data science and machine learning are an excellent method to learn about all types of issues and their intricacies.
5. Software Engineering and System Design
In the end, software is the normal output or deliverable of a machine learning engineer. Additionally, it frequently only makes up a minor part of a bigger ecosystem of goods and services.
You must comprehend how these many components interact, connect with them (via library calls, REST APIs, database queries, etc.), and create suitable interfaces for your component that others will rely on.
To prevent bottlenecks and allow your algorithms to scale efficiently with growing data volumes, careful system design may be required.
Best practices for software engineering, such as requirements analysis, system design, modularity, version control, testing, and documenting, are essential.
For efficiency, teamwork, quality, and maintainability.
What are Machine Learning Job Roles ?
Machine learning-related jobs are expanding quickly as businesses try to make the most of cutting-edge technologies.
The relative relevance of core competencies for these various sorts of roles is shown in the figure below, along with an example of a typical data analyst role, for comparison.
Get Ready for the Future of Machine Learning
The all-encompassing potential of machine learning is perhaps what makes it so alluring.
Machine learning is already having an impact on a wide range of industries, including education, banking, computer science, and more.
In some cases, machine learning techniques are desperately needed.
Healthcare is an obvious example.
Machine Learning techniques are already being applied to critical arenas within the healthcare sphere, impacting everything from care variation reduction efforts to medical scan analysis.
Undoubtedly, the world is changing quickly and dramatically, and as a result, demand for machine learning experts will continue to grow rapidly.
The world’s problems are complicated, and complex systems are needed to tackle them.
These systems are being developed by machine learning engineers.
There is no better time than the present to begin learning the skills and cultivating the mentality you will need to achieve if this is your destiny.
you can Enroll in Udacity Nanodegree programme for Machine Learning Engineers.
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