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How Much Does It Cost to Hire Edge ML Engineers?
The cost of hiring edge ML engineers depends on factors, such as:
- Experience: More experienced edge ML engineers usually earn more. According to Glassdoor, edge ML engineers with 2-4 years of experience can earn between ₹6-14L per year, while those with 5-7 years of experience can earn between ₹14-39L per year.
- Location: The city where the job is located also affects the Edge ml engineer salary. Generally, bigger or more developed cities offer higher salaries than smaller or less developed areas. For example, the average Edge ML engineer salary in Bangalore is ₹10L per year.
How Much Does an Edge ML Engineer Make?
As with most jobs, the more experience an edge ML engineer has, the more they can expect to earn. According to Glassdoor, edge ML engineers with 2-4 years of experience can earn between ₹6-14L per year, while those with 5-7 years of experience can earn between ₹14-39L per year.
Is Edge ML Engineer Still in Demand?
The demand for edge ML engineers is high and growing. According to a report by Gartner, the global edge AI software market is projected to grow from $590 million in 2020 to $1.9 billion in 2024, at a compound annual growth rate (CAGR) of 36.5%. Another report by LinkedIn ranked machine learning engineer as the fourth fastest-growing job in the US in 2022.
Hire Edge ML Engineers
Machine learning (ML), which is fundamental to producing artificial intelligence (AI), employs algorithms to draw conclusions from both fresh/live data and historical data. To date, the majority of data processing in ML applications has been performed in the cloud.
Edge ML minimises or eliminates reliance on the cloud by allowing local IoT devices to analyse data, create models and predictions, and take actions. Furthermore, the machine can enhance its efficiency and accuracy on its own, with little or no human assistance.
Edge ML has the ability to significantly accelerate Industry 4.0, with real-time edge processing enhancing manufacturing efficiency and applications ranging from building automation to security and surveillance.
As a result, the potential for ML at the edge is enormous, as evidenced by recent ABI Research research, which predicts that the edge ML enablement market will top US$5 billion by 2027. Furthermore, whereas ML was originally confined to the mathematical and scientific communities, it is now becoming an integral part of the engineering process, particularly in embedded systems engineering.
What is an Edge ML Engineer?
Edge machine learning (edge ML) is the technique of running machine learning algorithms on computer devices at the network's perimeter to make choices and predictions as close to the source of data as possible.
Edge artificial intelligence, or edge AI, is another name for it.Traditional machine learning involves enormous servers processing massive amounts of data acquired from the Internet in order to deliver some benefit, such as predicting what movie to watch next or automatically labelling a cat video.
We can produce such predictions faster and without the need to transmit large amounts of raw data across a network by running machine learning algorithms on edge devices such as laptops, smartphones, and embedded systems (such as those found in smartwatches, washing machines, cars, manufacturing robots, and so on).
What is the Role of an Edge ML Engineer?
- Research and Adaptation: Dive into the world of data science and analytics. Continuously explore and refine prototypes to stay ahead in the field.
- Methodology Development: Craft innovative strategies and approaches for machine learning. It's all about creating smarter, more efficient systems.
- Data Analysis through Testing: Use test outcomes to conduct thorough statistical analyses. This step is crucial for enhancing and fine-tuning our models.
- Resource Hunting: Scour the internet for datasets that can be easily accessed and utilized. These resources are the building blocks of our machine learning projects.
- Ongoing Training and Retraining: Keep our ML systems and models in top shape through regular training sessions. Adaptation is key to staying relevant.
- Collaboration: Work hand in hand with fellow team members, stakeholders, and clients. Collaboration fosters innovation and ensures everyone is on the same page.
- Staying Informed: Keep your knowledge fresh and current. Stay abreast of the latest trends and developments in edge ML to deliver cutting-edge solutions.
What are the Skills for Edge ML Engineers?
Edge ML engineers need to have a combination of technical and soft skills, such as:
- Machine learning: Edge ML engineers need to have a solid understanding of machine learning concepts, algorithms, frameworks, and libraries, such as TensorFlow, PyTorch, Keras, Scikit-learn, etc. They also need to know how to train, test, deploy, and optimize machine learning models for edge devices, as well as how to handle challenges such as data quality, latency, bandwidth, and power consumption.
- Data science: Edge ML engineers need to have a good grasp of data science techniques, such as data collection, preprocessing, analysis, visualization, and interpretation. They also need to know how to use data science tools, such as Python, R, SQL, Jupyter, etc., to perform data manipulation, exploration, and inference.
- Software engineering: Edge ML engineers need to have a strong background in software engineering principles, such as object-oriented programming, data structures, algorithms, design patterns, testing, debugging, etc. They also need to be proficient in programming languages, such as Java, C++, C#, etc., as well as development tools, such as Git, Docker, Kubernetes, etc., to build, integrate, and maintain edge ML applications.
- Edge computing: Edge ML engineers need to have a familiarity with edge computing concepts, such as edge nodes, edge clusters, edge clouds, etc., as well as edge computing platforms and tools, such as AWS IoT Greengrass, Azure IoT Edge, Google Cloud IoT Edge, etc. They also need to know how to leverage edge computing capabilities, such as distributed processing, edge analytics, edge intelligence, etc., to enhance the performance and functionality of edge ML applications.
- Communication: Edge ML engineers need to have good communication skills, both verbal and written, to collaborate with other team members, stakeholders, and clients. They also need to be able to explain complex technical concepts and results in simple and clear terms, as well as to document and present their work effectively.
Other Frequently Asked Questions (FAQs)
1. What is the edge in machine learning?
Edge computing allows you to physically bring artificial intelligence/machine learning (AI/ML)-powered applications closer to data sources such as sensors, cameras, and mobile devices to gather insights faster, discover patterns, and start actions without relying on traditional cloud networks.
2. What is an AI edge engineer?
The AI edge engineer career way takes an engineering-led approach to deploying AI to edge devices within the cloud architecture. This learning path teaches participants AI capabilities (including machine learning and deep learning methods), IoT, and DevOps.
3. What is the role of edge AI?
Edge AI trains models using neural networks and deep learning to recognise, categorise, and describe objects in provided data. This training procedure typically makes use of a centralised data centre or the cloud to process the large volume of data required for model training.
4. What is edge learning?
The optimisation loop for edge learning is substantially shorter, and it eliminates the requirement to transfer images to another device for labelling and retraining. It also optimises labour utilisation and decreases the long-term maintenance required for data collection and management.
5. What is edge technology?
Edge computing is a computing paradigm that refers to a group of networks & devices located at or near the user. Edge computing is about processing data closer to where it is generated, allowing for larger processing and faster rates and volumes, resulting in more real time answers.
6. What are the benefits of edge machine learning?
However, running ML on devices with little power has a number of advantages: Raw data transmission consumes less network bandwidth. While some information must be transferred over a network (for example, inference findings), less communication often equals less power consumption.
The most significant benefit of edge AI is that it provides high-performance computing capabilities to the edge, where sensors and IoT devices reside. AI edge computing allows AI applications to run on field devices directly, analysing field data and running machine learning (ML) and deep learning (DL) algorithms.
