When most people think of ML, they imagine a team of data scientists, high overhead costs for computer hardware and timelines that stretch into quarters or a year before seeing results. But today the market is filled with automation tools and cloud-based computing power. ML, AI, and Big Data are available more than ever. It is now easier for medium to large-size businesses to start using and applying these advancements in business intelligence technology to grow and improve their business.
On the other hand, the issue lies not in lack of technology, but rather with the overwhelming amount of it. There’s, SnowflakeDB, Databricks, Apache Spark, Pandas, Python, AWS Athena, DataRobot, I can go on for a while.
Where does one start?
Well the good thing is that companies now have flexibility and control over almost every part of the process, from connecting to an existing data and doing ETL, to running machine learning models. In order to stay competitive, businesses must take every possible opportunity in gaining deeper insights into their operations. Imagine taking almost all of your collected company data and applying a Gradient Boosting algorithm or better yet, multiple different algorithms all in a matter of hours or minutes. Something that took months before, is now available in a considerably shorter timeline. Imagine the marketing department having valuable insights into their recent campaign where they can gauge and optimize that campaign l within a day. Sounds interesting?
Let’s look at some of the scenarios and options that businesses have when it comes to getting into big data and the ML world of things. Making data available is the most important part of it If you don’t have the required/optimized backend, the end result will not be optimal and sometimes carry little benefit to the entire process. Gaining insight is not really beneficial if the data behind it was not validated tested and optimized for a specific feature.
Most businesses invest a great amount of effort and resources in figuring out if produced insights are correct. Any discrepancies or concerns usually results in going back to validate/compare those results again, thereby losing valuable time. That is why having a team of experts for all parts of the integration process plays an important role not only in the success, but also the overall cost of the project.