Building a new company or transforming an existing one into a data-driven enterprise is a growing process through multiple stages that takes time. The challenge is progressing into the next stage and, having attained the goal, maintaining a company culture that can remain there.
It is important to remember that there must be a business reason for even considering neural nets and it should not be because the C-Suite is feeling a bad case of FOMO.
Data structured as a network of relationships can be modeled as a graph, which can then help extract insights into the data through machine learning and rule-based approaches. While these graph representations provide a natural interface to transactional data for humans to appreciate, caution and context must be applied when leveraging machine-based interpretations of these connections.
The evolution of Big Data into machine learning applications ushered in an exciting era of new roles and skillsets that became necessary to implement these technologies. With the Machine Learning Engineer being such a crucial component today, where the evolution of this field will take us tomorrow should be fascinating.
We often discuss applying data science and machine learning techniques in term so of how they help your organization or business goals. But, these algorithms aren't limited to only increasing the bottom line. Developing new applications that leverage the predictive power of AI to benefit society and those communities in need is an equally valuable endeavor for Data Scientists that will further expand the positive impact of machine learning to the world.
When it comes to data science projects, the disconnect between business executives and data teams can lead to major tension. Keeping these challenges from arising in the first place through effective communication will help reduce friction with stakeholders.
"It's just about having more compute." Wait, is that really all there is to AI? As Richard Sutton's 'bitter lesson' sinks in for more AI researchers, a debate has stirred that considers a potentially more subtle relationship between advancements in AI based on ever-more-clever algorithms and massively scaled computational power.
You are intrigued by this exciting new field of Data Science, and you think you want in on the action. The demand remains very high and the salaries are strong. Before taking the leap onto this path, these questions will help you evaluate if you are ready for the challenges and opportunities.
Breaking into any new field or slogging through a career change is always a challenge, and requires focus and even a little grit. While transitioning to becoming a Data Scientist is no different, aspiring to this role is possible, even without a formal post-secondary degree, largely due to the vast amount of quality learning resources available today.
If machine learning models predict personal information about you, even if it is unintentional, then what sort of ethical dilemma exists in that model? Where does the line need to be drawn? There have already been many such cases, some of which have become overblown folk lore while others are potentially serious overreaches of governments.
I believe the “Predicting Heart Disease using Machine Learning” is a classic example of how not to apply machine learning to a problem, especially where a lot of domain experience is required.
Check out the author's informative list of courses and specializations on Coursera taken to get started on their data science and machine learning journey.
Machine learning algorithms are notoriously known for needing data, a lot of data -- the more data the better. But, much research has gone into developing new methods that need fewer examples to train a model, such as "few-shot" or "one-shot" learning that require only a handful or a few as one example for effective learning. Now, this lower boundary on training examples is being taken to the next extreme.
When predictive machine learning models are applied to real-life scenarios, especially those that directly impact humans, such as cancer detection and other medical-related applications, the risks involved with incorrect predictions carry very high stakes. These risks are also prominent in how machine learning is applied in law enforcement, and serious ethical questions must be considered.
The CDMP is the best data strategy certification you’ve never heard of. (And honestly, when you consider the fact that you’re probably working a job that didn’t exist ten years ago, it’s not surprising that this certification isn’t widespread just yet.)
To all those Data Scientists out there who thrive on discovering actionable insights from your data (all of you, right?), take heed from this cautionary tale of a data analysis, a dashboard, and a huge waste of resources.
Still today, too large a percent of data science projects fail, many of which can be attributed to the impacts of how hard missing data teams hit the data science team. Advocating for the missing data engineering and operations components to your team will make your professional life easier and more productive.
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