After years of hype and promise, artificial intelligence (AI) has finally arrived. Organizations of all types and sizes are racing to integrate AI into their business processes to make their operations more powerful, more efficient, and more profitable. A data scientist andmachine learning engineer are two of the most exciting and cutting-edge professions in technology. While both involve realizing the promise of AI in business, choosing between becoming a machine learning engineer vs. a data scientist requires understanding how the two roles differ, and how they complement each other.Machine learning engineers and data scientists are members of the team behind a company’smachine learning (ML) platform . Each position fulfills critical duties in the development, implementation, and maintenance of machine learning applications.Yet the roles, skill sets, and responsibilities of a machine learning engineer vs. data scientist differ in important ways. Understanding the differences and similarities of the two positions helps you decide which role is a better match for your career goals.The Role of a Machine Learning Engineer vs. Data ScientistThe goal of machine learning and other AI-based activities is to create software applications that enhance our lives, whether in business settings or in our day-to-day activities outside of work. Machine learning engineers and data scientists are vital to the design and use of intelligent systems that naturally improve over time, with or without the assistance of humans.One way to distinguish the roles of machine learning engineers and data scientists in intelligent system design is by seeing data scientists as the architects of a structure and machine learning engineers as the builders who convert blueprints and models into a functioning system.Determine which business problems are suitable for ML solutionsVisualize the many stages of theML lifecycle(data gathering, data preparation, data wrangling, data analysis, modeling training, model testing, deployment)Design custom algorithms and data modelsIdentify complementary data sets and generate thesynthetic data that deep learning (DL) models requireDetermine the system’s data annotation requirementsMaintain ongoing communication with all stakeholdersCreate custom tools for optimizing the modeling workflowBy contrast, the role of machine learning engineers emphasizes the deployment and operation of ML and DL models:Deploy and optimize ML and DL models in production settingsMonitor the models’ performance to address latency, memory, throughput, and other operational parametersPerform inference testing on CPUs, GPUs, edge devices, and other hardwareMaintain and debug the ML and DL modelsManage version control for models, metadata, and experimentsData scientists are directly involved in theanalysis and interpretation of the insights extracted from ML and DL models by applying statistical and mathematical techniques to identify patterns, trends, and relationships in the data.Machine learning engineers rely more on their background in programming and engineering to transform data science concepts into functional systems that are flexible, scalable, and transparent.Machine Learning Engineer vs. Data Scientist: Skills, Education, and ResponsibilitiesThere is a considerable amount of overlap in the qualifications needed for careers in machine learning engineering and data science. For example, both fields require technical acumen, analytical thinking, and problem-solving skills. They also rely on programming experience that typically includes Python and R programming, cloud systems (AWS, Microsoft Azure, and Google Cloud Platform, or GPC), andmetadata storage andoptimization.Yet more important than the similarities in the education and skills of machine learning engineers and data scientists are the differences in their technical and educational backgrounds:Data scientists must be adept at statistics, data analytics, data visualization, written and verbal communications, and presentations.Machine learning engineers must possess in-depth knowledge of data structures, data modeling, software engineering, and the concepts underlying ML and DL models.Data scientists tend to have a broader set ofhard skills than machine learning engineers, including experience with statistical and mathematical software, query languages, data visualization tools, database management, Microsoft Excel, and data wrangling.Database design and programming, including NoSQL and cloud databasesData collection and cleaning tools, including business intelligence (BI) toolsStatistical analysis tools such as SPSS, Matlab, and SASDescriptive, diagnostic, predictive, and prescriptive statistical analysesLinear algebra and calculusModel validation and deployment tools (SAS, Neptune, Kubeflow, and Google AI)API development tools such as Amazon AWS (Amazon API Gateway) and IBM Cloud (IBM API Connect)The U.S. Bureau of Labor Statistics (BLS) points out that most data scien
Location: United States
Posted: Aug. 8, 2024, 10:21 p.m.
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