In addition, there is a large variety of algorithms that can be used by machine learning practitioners to apply these diverse approaches to learning. There are various tradeoffs and performance characteristics of these algorithms. Additionally, a machine learning model is the end product of training a specific algorithm on specific training data. For a specific mission, the model reflects what the computer has learned. The machine learning algorithm, which tells machines the method they can use to encode learning, and the machine learning model, which is the result of that learning, seem to sometimes confuse people. As new approaches to learning are few and far between, new algorithms are not commonly developed. However since any new learning is encoded in a model, new models are created all the time, which can occur an infinite number of times.
Building machine learning models, in addition to the above challenges, can be particularly challenging, especially for those with limited data science and machine learning skills and understanding. Those with deep technological skills and good statistical knowledge can optimize and tweak models and with their experience, choose suitable algorithms with the correct settings (‘hyperparameters’), whereas others who are fairly new to model development can be stumped by all the choices that need to be made to choose the correct modeling approach. Market instruments for the production of machine learning models cater to a wide variety of needs, from beginners to experts, making the choice of resources even more difficult.
Five Major Approaches to ML Model Development
A recent report by AI market research and advisory firm Cognilytica identifies five major approaches to machine learning model development:
Machine Learning Toolkits
With decades of study from academicians, scholars, and data scientists, the field of machine learning and data science is not new. As a result, there is a broad set of toolkits that allow a wide range of low-level configurability algorithms to be implemented by knowledgeable machine learning professionals. These toolkits for machine learning are very common and many are open source.
Some toolkits concentrate on specific machine learning algorithms and applications, most notably Keras, Tensorflow, and PyTorch, which focus on deep learning models, while others include a variety of machine algorithms and resources, such as Apache Mahout and SciKit Learn. In turn, these toolkits are embedded in several larger frameworks for machine learning, including the ones listed below.
Furthermore, several of the machine learning toolkits have the funding and ongoing resources of large technology companies for growth.
For example, Facebook supports PyTorch, Google supports Keras and TensorFlow, Amazon supports MXNet, Microsoft supports CNTK Toolkit, and others are supported by companies like IBM, Baidu, Apple, Netflix, and others
Data Science Notebooks
The field of machine learning is that of data science, because after all, we’re trying to extract higher value insights from big data. The primary data science environment is the’ notebook,’ which is a collaborative, interactive, document-style environment that incorporates aspects of coding, data engineering, simulation of machine learning, visualization of data, and sharing of collaborative data. Open source notebooks like Jupyter and Apache Zeppelin have been widely embraced and have even made their way into the offerings of commercial platforms.
Through the support and integration of many of the popular machine learning toolkits mentioned above, data science notebooks offer the full range of machine learning algorithms. Although data science notebooks can be used to build models of any kind, they are mainly used during model creation testing and iteration phases, as data science notebooks are designed for that form of iterative experimentation rather than concentrating on management and implementation aspects around the enterprise.
Machine Learning Platforms
Organizations trying to make mission-critical use of machine learning recognize that not everything that needs to be taken into account for the needs of the ML model is simply creating a machine learning model. The full life cycle of the development of the machine learning model includes aspects of data preparation and engineering, iteration of the machine learning model, including the use of “AutoML” to automatically identify the best algorithms and settings to achieve the desired results, evaluation of the machine learning model, and iteration and versioning of the ML model, including the emerging “ML Ops” area.
As a consequence, the last decade has seen the explosive emergence of solutions for full-lifecycle machine learning systems that strive not only to simplify the development of the ML model, but also to solve these other areas of ML model lifecycle management. Many businesses in this sector have emerged as small start-ups to become global industry powerhouses with ever-increasing solutions that address a wider range of requirements for data scientists and machine learning engineers.
The world of analytics and business intelligence existed before data technology was called data science. Since then, some instruments that were once used for non-machine learning analytics have added machine learning model creation to their capabilities. Most of the analytics field is dominated by a few large commercial analytics firms, which are increasingly broadening their offerings. As such, data scientists who may have experience with those techniques will discover growing capabilities for model creation of machine learning and wider lifecycle capabilities.
Traditionally targeted at data analytics, statistics, and mathematics applications, these solutions have realized the power to apply machine learning capabilities to their existing offerings of statistics and/or analytics. Organizations that have already invested in analytics technologies can find that their current tools that now enable the creation and implementation of machine learning will maintain expertise, experience, and investment.
Cloud-based ML-as-a-Service (MLaaS)
Most of the large cloud providers have jumped in with both feet into the machine learning space in addition to the above approaches. Amazon, Google, IBM and Microsoft have all added core capabilities for the creation, management, and iteration of machine learning models, as well as data preparation, engineering, and augmentation capabilities. Many of the open source ML toolkits as well as the Data Science Notebooks popular in the field are also supported and used by these cloud vendors. As a result, the decision to use the cloud for the implementation of the ML model is rarely a “either/or” decision, but more of a tactical decision on whether to use cloud-based tools for computation, data storage, and ML lifecycle capabilities with added value is required.
The growth of machine learning model markets
The field of development of machine learning models continues to expand, no doubt. By 2025, Cognilytica expects the machine learning platform market to reach over $120 billion USD, growing at a rapid and furious rate. (Disclosure: I’m a principal analyst with Cognilytica) While there might be questions as to how long this latest wave of AI will last, there’s no doubt that the future of machine learning development and implementation looks bright.
My advice to you is to be open-minded and think outside of the box while you are looking for a career in data science. It will give you a competitive edge in your career in data science.
Bio: Shaik Sameeruddin I help businesses drive growth using Analytics & Data Science | Public speaker | Uplifting students in the field of tech and personal growth | Pursuing b-tech 3rd year in Computer Science and Engineering(Specialisation in Data Analytics) from “VELLORE INSTITUTE OF TECHNOLOGY(V.I.T)”