Data Science is driving recent advances in Machine Learning and Artificial Intelligence. Due to this, there is a significant growth of ML libraries and the Python programming language. Data Science is about producing insights, whereas AI produces actions, and ML makes predictions. All the three fields overlap enormously. To better understand the core use of data science in ML and AI, one needs to know how to use the machine learning engineering stack.
Machine Learning and especially compute-intensive deep learning technology’s use is booming across industries and research. With this, the machine learning stack’s market is growing. Tech giants like Amazon, Microsoft, and Google are releasing targeted cloud products to develop Machine learning technologies quickly. The tools are equipped for data preparation to model optimization to deployment. As a part of machine learning training, it is essential to know about the leading ML stack. This blog focuses on the best ML stack for getting hired at Google.
Table of Contents
- What is an ML stack?
- Dask-ML
- GitHub
- Docker
- CometML
- Hadoop
- Pandas
- Luigi
- Google’s Strategy
- Conclusion
If you wish to work for one of the technology giants in the future, having a machine learning certification is exemplary.
What is an ML stack?
As a machine learning expert, you have various tools at your disposal for developing new ML capabilities. ML stack is a sufficient and necessary collection of such tools. Machine learning is a subset of AI that enables a machine to learn from data without explicit programming. Neural networks are the core of machine learning systems, and artificial neural networks (ANN) are its computing units. The algorithms used for AI are constant, but the tools are changing. Today, you don’t need to build a stack from scratch but can instead use libraries and ML platforms. Some of these capabilities can also be consumed as a service directly. Here is a list of selected tools and resources :
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Dask-ML
It is a tool developed to provide advanced parallelism for analytics and boosting performance for NumPy and Pandas workflows. It is a useful tool to overcome large data sets and long training times. The algorithms can be scaled according to Dask-ML arrays.
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GitHub
It is a repository development program and hosting service where both open-source communities and businesses can access and review projects. It has a user-friendly web-based GUI, which is excellent when it comes to machine learning for beginners. This platform boasts a wealth of resources for the benefit of all.
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Docker
Docker simplifies the installation process. It is a blessing for AI developers that spend a significant amount of time trying to resolve configuration problems. It is an open-source platform that makes it easier to deploy and manage the developed virtual machines on popular operating systems. Docker revolutionized ML because it allowed the creation of capable application architecture.
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CometML
CometML wishes to work for ML-like GitHub work for code. It allows developers and data scientists to track, compare, and collaborate on machine learning experiments. You can track code changes and graph the results.
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Hadoop
It can be described as a software library and framework, enabling distributed data processing of large datasets using simple programming models. It is ideal for companies that want to process complex datasets quickly to identify patterns.
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Pandas
It is an open-source library with easy to use data structures and tools for data analysis using Python. Pandas is the backbone of many big data projects. It is critical to know Pandas for data cleaning, transformation, and analysis.
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Luigi
It is a tool used by Spotify to build complex pipelines of batch jobs. It addresses challenges associated with long-running batch processes and makes it easier to manage and automate.
Google’s Strategy
If you wish to work for Google after completing a machine learning course, it is beneficial to know how Google works and what ML stack it employs. Google’s business model depends on advertising revenue. New interfaces such as virtual assistants are widely adopted as new platforms emerge. Google has launched several products to limit the impact on traditional ways of search. Two strategic opportunities are emerging in the ML space. First is building revenue through ML-added services and the other being conquering the cloud market. Google has significant expertise in the culture, internal infrastructure, and the field to develop and launch AI-driven products. This has created an enormous market opportunity. There is also room for new-gen cloud products, and Google is tapping into the opportunity.
Google has released and has been using products across the various layers of the stack. TensorFlow is by far the most popular machine learning framework, and Google cloud is the choice of every certified machine learning expert. TensorFlow has also acted as good marketing for a highly-priced Machine Learning Cloud platform called Google ML Engine. Kubeflow is an alternative for on-premise users. Google is working on product differentiation and lower switching costs. To win over AI developers at Google, along with the knowledge of value-added AI infrastructure services and products, a reliable stack approach and leadership strategy is needed. Google also wishes to ease the switch between cloud services by imposing its container workload orchestration system. For this, there is a requirement of professionals who can significantly redesign the software.
Conclusion
The machine learning engineer’s toolbox is robust. The available technology is potentially overwhelming and quite significant. For a professional with python certification, it is easy to pick up the ML stack’s main components. Predicting the evolution of stack is a tricky exercise. Some standardization of stack is beneficial to build tools and applications. The collaboration of open and closed source on defining standards maximize the chances of success. If you need help figuring the ML stack out, you can sign up for a machine learning certification.
