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While Data Science and Machine Learning demonstrate undeniable value in multiple industries, the long development times and low success rates can be daunting. However, with the right approach, Machine Learning models can be developed and deployed quickly to create substantial value. Creating a sound business case, intelligently weighing the tradeoff of model performance vs. development time, and picking the right tools and technology for the job lead to quicker, more efficient, and more valuable Data Science solutions.
Defining a business goal is the first, and most critical, step to a successful Data Science initiative. Addressing these foundational questions ensure we choose the right Machine Learning projects, with faster development times and more value added:
If we are confident that Machine Learning is the best way to solve our business problem in a timely manner, there are tradeoffs that need to be considered.
There is a wide array of Machine Learning algorithms at our disposal. Some models require more development and training time than others but might lead to better results in the end. For example, a deep neural network might yield better results than a linear regression model, but the time and cost to get value from the linear regression model is typically less than that of the neural network.
Ultimately, the question to answer is “what is the most time efficient, simplest, and cheapest model that can yield me the results that I desire?”. If a logistic regression returns 85% accuracy with less effort required, it may not be worth it to throw time and money at a neural network if it only increases your accuracy by 2%.
As seen in the chart above , taking longer to complete Data Science projects does not always lead to a proportional value add.
An ecosystem of tools and technologies has sprung up alongside the wave of Data Science that makes model development and deployment fast and efficient. Some of these include Docker, Python, SQL, cloud computing, and many others. Python has a vast number of data cleansing and Machine Learning algorithms. It is also a user-friendly and flexible language, which makes setting up a Machine Learning problem framework comparatively quick.
To speed up the time used on data exploration, the Python library pandas_profiling is an excellent tool to consider. It provides high level statistics on your features and visualizations to get a better feel for your data, quickly! Docker can create a script with a pre-configured environment to deploy on any machine or server.
Cloud computing allows for quick scaling of applications with a matter of a few button clicks. Plus, it keeps costs under control as we are only billed for resources utilized. The cloud ecosystem provides tools such as Azure ML, AWS SageMaker, and GCP’s AI & ML services to help expedite the data science lifecycle.
Used harmoniously, these tools and technologies significantly decrease time to insights, deployment, and ultimately boost business value for your organization.
Data Science and Machine Learning have helped companies unlock the power of their own data to make actionable insights and create immense business value. But to harness Data Science’s full potential, your team should have a clearly defined business goal, pick the models that will yield the most value in the minimum amount of time, and use the optimal combination of tools and technologies that save you money and time.
If you are considering Data Science to tackle your most challenging and high-value business objectives, RevGen Partners has the expertise, experience, and passion to help you succeed. Contact us today to schedule a quick chat.
 VB Staff. “Why Do 87% of Data Science Projects Never Make It into Production?” VentureBeat, VentureBeat, 22 July 2019, venturebeat.com/2019/07/19/why-do-87-of-data-science-projects-never-make-it-into-production/.
Samuel Brown is a Senior Consultant. He specializes in Data Science with three years of experience in Machine Learning.