The world today is heavily dependent on data. The amount of data that we produce grows exponentially every year. At least 2.5 quintillion bytes of data.) are produced every day ― in case you didn't know, that's a number followed by 18 zeros! Inside this data, we can find important insights about how to get better results in a reduced amount of time, be it manufacturing, medicine, or education. Data science, data analytics, and machine learning are terms that are often used interchangeably when talking about making sense out of this data. In fact, machine learning, data science, and data analytics are different fields that pursue different goals.

You really can't avoid it, can you? It's mentioned wherever you look. Your LinkedIn feed, job market, news feeds, education programs trying to get your attention (and your enrollment fee). But what is data science really? It's often very vaguely described leaving much to be desired. This guide will try to avoid all that and provide you with the best possible, most direct, and clear answers to "What is data science?" and "What does a data scientist do?". So, what do data scientists do? To answer this question, we'll lead you through the various aspects of working in data science. The role of data science is to use unfathomable amounts of data every company is collecting nowadays and turn it into understandable and useful information.

If I've got a data scientist that can do all of those things, then I'll worry about getting that implemented through the data engineering team,

In recent years, machine learning has been very successful in solving a wide range of problems. In particular, neural networks have reached human, and sometimes super-human, levels of ability in tasks such as language translation, object recognition, game playing, and even driving cars. Aerospike is the global leader in next-generation, real-time NoSQL data solutions for any scale. Aerospike's patented Hybrid Memory Architecture delivers an unbreakable competitive advantage by unlocking the full potential of modern hardware, delivering previously unimaginable value from vast amounts of data at the edge, to the core and in the cloud. With this growth in capability has come a growth in complexity. Data scientists and machine learning engineers must perform feature engineering, design model architectures, and optimize hyperparameters. Since the purpose of the machine learning is to automate a task normally done by humans, naturally the next step is to automate the tasks of data scientists and engineers. This area of research is called automated machine learning, or AutoML. There have been many exciting developments in AutoML recently, and it's important to take a look at the current state of the art and learn about what's happening now and what's coming up in the future. InfoQ reached out to the following subject matter experts in the industry to discuss the current state and future trends in AutoML space. InfoQ: What is AutoML and why is it important?

In recent years, machine learning has been very successful in solving a wide range of problems. In particular, neural networks have reached human, and sometimes super-human, levels of ability in tasks such as language translation, object recognition, game playing, and even driving cars. Prevent out-of-control infrastructure and remove blockers to deployments. With this growth in capability has come a growth in complexity. Data scientists and machine learning engineers must perform feature engineering, design model architectures, and optimize hyperparameters. Since the purpose of the machine learning is to automate a task normally done by humans, naturally the next step is to automate the tasks of data scientists and engineers. This area of research is called automated machine learning, or AutoML. There have been many exciting developments in AutoML recently, and it's important to take a look at the current state of the art and learn about what's happening now and what's coming up in the future. InfoQ reached out to the following subject matter experts in the industry to discuss the current state and future trends in AutoML space. InfoQ: What is AutoML and why is it important? Francesca Lazzeri: AutoML is the process of automating the time consuming, iterative tasks of machine learning model development, including model selection and hyperparameter tuning.