¿Cuáles son las funciones de aprendizaje automático más importantes que hay que tener en cuenta?

Other roles that might share space with ML are: MLOps Engineer and LLMOps Engineer who is responsible for building pipelines and systems for the entire ML lifecycle, from data preparation and model training to deployment and monitoring, and retraining. AI product manager: This would be an intersection of tech and business teams for driving AI/ML products. Full Stack MLE/ Data scientist: Does all the work of Data Scientists, MLE, and MLOps. This is gaining momentum as a lot of early-stage startups are looking for all-round developers.

Data Scientists analyze and interpret complex data to help organizations make better and more timely decisions. They are skilled in statistics, machine learning, and data visualization, and often have domain-specific knowledge that allows them to offer insights beyond the data itself. My observation is that this role is pivotal for businesses looking to leverage data for strategic advantages.

A data scientist is a professional who uses machine learning to analyze and gain insights from complex data sets. They require expertise in math, stats, programming, and domain knowledge, as well as effective communication skills. Their work includes projects like recommender systems, fraud detection, customer segmentation, and natural language processing.

A data scientist plays an important role in machine learning. They contribute to various stages of machine learning lifecycle. Here are some of the contributions by them: They formulate the problem to be solved by working with stakeholders, and then they collect the data and explore it to understand its nature. Then they extract and select important features from them. Next, they select an optimal model and train it. Then they evaluate and validate the model by hyperparameter tuning to get the optimal model. They make the model Interpretable and Explainable. Next, they deploy the production model and monitor and maintain them. They Communicate the findings and insights to non-technical stakeholders and solve concerns about Ethical issues.

As someone who has been a Data Scientist, this profile focuses on understanding data, trends in it and learning to find patterns which shall deliver value.

Machine Learning Engineers focus on designing and implementing machine learning applications. Unlike Data Scientists, who may focus more on analysis and less on deployment, Machine Learning Engineers are experts in applying algorithms to code and integrating them into products or services. They need strong programming skills and an understanding of software engineering practices. This role is crucial for translating machine learning models into usable products.

In my new role as MLE, I am more focused on optimisation, scalability, availability of the deployed solutions using the best software engineering practices. Also, designing a good architectural solution is the prime step in approaching the problem.

Machine learning experts create algorithms, programs, predictive models, and software that allow machines to recognize patterns and act on their own initiative without needing human guidance to carry out these particular jobs. In addition to software engineering concepts, a they must possess a thorough understanding of machine learning algorithms, frameworks, and tools. In addition, a machine learning engineer must be able to integrate machine learning tool with other existing systems while optimizing the performance, and scalability of solution. Machine learning engineers can work on a variety of technologies, including speech recognition, facial identification, chatbots, self-driving automobiles and others.

This role is for people with deep understanding of engineering principles along with ML knowledge, data and domain knowledge. As an MLE, you will design, build and implement the ML models and deploy them while also creating ETL pipelines and leveraging MLOps. (There might be other specialized teams for some of these tasks) As an MLE, you will most likely also require further specializations like Deep Learning, Reinforcement Learning, Computer Vision, or NLP along with expertise in ML concepts. These days, this role is also beginning to include Prompt Engineering on Pre-trained LLMs like ChatGPT, LLaMA, Gemini, Mixtral, etc. but it is still crucial to understand the concept behind how these LLMs are built.

Researchers in machine learning push the boundaries of what is possible by developing new algorithms and techniques. They often work in academia or specialized research departments within companies. The advancements and breakthroughs made by these individuals drive the field forward and can lead to significant technological innovations. Their work is foundational for future applications and improvements in machine learning.

A machine learning researcher conducts original research and creates new methods and theories in the field. They require extensive knowledge of machine learning concepts and trends, critical thinking skills, and the ability to publish findings and collaborate with peers. Research areas may include deep learning, reinforcement learning, generative models, and computer vision.

ML Researchers are very closely related to the Machine Learning Engineer role. They will often work with MLEs to implement their research findings. While MLEs focus more on the system design, engineering and development part of the process and is heavily tool-based, ML Researchers are more theoretical and academic in nature. They often have higher education like Masters, doctoral or Post-doctoral degrees. They also have backgrounds in academia and mostly have specialization beyond ML and into different domains like Astrophysics, Biology, Chemistry, Genetics, Maths, Finance, etc. They work with business stakeholders to understand business problems so they can research innovative solutions which then get implemented by the ML team.

Data Engineers build and maintain the infrastructure and tools needed to handle large amounts of data. They ensure that data flows smoothly from source to database to analytics, and that it is accessible and usable for analysts, scientists, and engineers. This role is critical because, without well-architected and reliable data pipelines, data science and machine learning projects can suffer from data quality issues or lack of scalability.

Data Engineers are where it all pretty much starts. They create the ETL and ELT pipelines and infrastructure. These pipelines and infrastructure are then used by the rest of the ML, AI and Data Science team to source data from multiple sources, develop and deploy solutions. This also aids the Data Analytics teams to get the data for reporting and analytics which is then used in decision-making processes. A good data engineer can increase the efficiency of the rest of the data team many fold!

Machine Learning Consultants leverage their expertise to advise companies on how to best use machine learning to solve their business problems. They must understand both the technical and business aspects of machine learning and be able to communicate effectively with stakeholders. This role is important for organizations that are new to machine learning or looking to expand their use of the technology.

As a specialist, the Machine Learning Consultant collaborates with clients, comprehending their unique requirements, spotting opportunities for leveraging machine learning and executing valuable organizational solutions. Their role encompasses client consultations, requirement understanding, solution design, feasibility assessments, tool recommendations, data collection strategies, model selection, deployment, performance evaluation, and ongoing model maintenance. Furthermore, they contribute to model customization and educate clients on the implementation and utilization of their machine-learning solutions.

Machine Learning consultants have customer empathy, considerable work experience of ML life cycle and excellent communication skills.

Beyond these roles, it's essential to consider how interdisciplinary teams work together in machine learning projects. For example, User Experience (UX) Designers ensure that ML-driven products are usable and meet user needs, Product Managers guide the development of products that leverage ML, and Ethicists or AI Governance Specialists address ethical considerations in ML applications. The collaboration across these roles is crucial for the responsible and effective use of machine learning.

Before you start hiring engineers, you need a strategist and a leader. So you need to find someone who is a leader in the AI strategic planning space. This person will be able to look at where you currently are with your talent, resources, budget, business problems, capabilities, scope and then guide you on what are the best people to hire. There is no one shoe fits all solution to this. Someone might need a MLE who has expertise in Research and another might need an MLE who’s a rockstar in predictive analytics or an MLE who is great with MLOps. The team you hire is unique to what your business requires at that time. You need an expert to help you with that decision so you don’t create a team that doesn’t fit into your business.

Key machine learning roles include Machine Learning Engineer, responsible for model deployment and infrastructure; Data Scientist, focusing on data analysis and algorithm development; Research Scientist, contributing to ML advancements; NLP Engineer, specializing in natural language processing; Computer Vision Engineer, working on visual data interpretation; and AI Ethicist, ensuring responsible and unbiased ML practices. Additionally, roles like Data Engineer, responsible for data pipeline development, and ML Product Manager, overseeing ML project strategy, are crucial. Each role demands a unique skill set, combining expertise in programming, statistics, domain knowledge, and ethical considerations, contributing to diverse aspects.

These terms are often used interchangeably but that is due to a lack of understanding and planning and not because they are the same. That being said, there is definitely an overlap and most companies don’t need all of these roles at once but need more than one role to function properly. For instance, an MLE with a specialization in NLP can create ETL pipelines to get data for their project but since their specialization is not in data engineering, the pipeline might not be the most efficient and the data might not be the best which in turn affects the quality of the model. While looking for a job, don’t get stuck on titles. Make sure that the projects, roles and responsibilities align with your experience, expectations and career goals.

The most important machine learning roles to consider are: Data Scientist: Responsible for data analysis, model development, and interpretation. They possess strong analytical skills and domain knowledge. Machine Learning Engineer: Focuses on implementing ML models into production systems, optimizing performance, and scaling solutions. Data Engineer: Manages data infrastructure, ensuring data availability, quality, and reliability for ML pipelines. Research Scientist: Conducts research to advance ML algorithms, explore new techniques, and improve model performance. AI Ethicist: Addresses ethical concerns in AI development, ensuring fairness, transparency, and responsible use of AI technologies.