While implementation of machine learning (ML) is still misunderstood, there’s never been a better time to learn more about the technological tools and processes needed to facilitate the generation of data-derived insights. As such, ML is vital for enterprises, because it helps accommodate to the ever-growing number of data, thus paving the way for better insights. Once ML’s true infrastructure is realized, companies can learn to better understand the data, and use it to produce more efficient and revenue-generating products.
Today, we’ll talk about the four steps that you can follow, so that implementing ML in your enterprise will be a breeze.
Step 1: Data Sourcing
“When you’re sourcing data, you have to look at the various types of inputs to the algorithm,” says Ethel Velez, a tech writer at Nextcoursework and Originwritings, “as well as what technologies you’ll need to look into these sources, and through what processes.”
Here are some examples of data sources:
- Core transactions
- Information provided by customers
- External databases
- Market research data
- Social media
- Site traffic
Step 2: Create A Trusted Zone
Sourced data can then be curated through an SSOT, which structures it into a consistent place. With that said, validity and quality matter. Before data can be fed into ML, it has to be aggregated, reconciled, and validated.
Here are key attributes of a trusted zone:
- A central repository of data (aggregated from multiple channels)
- Data elements (clearly defined and documented) and data lineage
- Documentation of assumptions (In other words, recent data prevails, if new data conflicts with old)
- Protocol for addressing unintended exceptions
- Daily reporting
- Vertical and Horizontal Architecture
- A data store that:
- Houses the trusted zone
- Has high availability, AND
- Is resilient to failure.
Now, data stores can be hosted on cloud platforms, which have benefits like:
- High availability
- Cost-effective, AND
- Horizontal and vertical scaling
Though, there are still concerns, in regards to privacy and security. Now more than ever, data is valuable, and can be breached at any day, any time – and ML is no exception to this.
To ensure data compliance, your security and risk management teams must initiate and monitor best security practices, and even learn how to handle any possible breaches. Also, make sure that any cloud vendors that you do business with are held accountable, whenever something goes wrong. And, enable data encryption, before it’s transmitted to the cloud, even when done over a secured virtual private network.
Step 3: Model Building For ML
“Many companies are building ML-friendly models through software developing,” says Lara Payne, a journalist at Phdkingdom and 1Day2write. “However, building a model on your own comes with setbacks, if not done properly. The problem is that when they use software development in place of actually building an ML model, then they’re not getting the whole thing – it’s like buying milk that is half-empty.”
In that case, ML model building still needs the right tools, so that the ML’s entire lifecycle is accounted for. The tools you would still need are:
- Model deployment
Although model lifecycle management is still a work-in-progress itself, the data community is always updating the tools needed to make model development easier.
Step 4: Get Insights
Real-time insights are important, because they show valuable data like customer behavior and potential fraudulent transactions. And, these insights need to be processed, generated, and delivered within short time frames or in near real-time. All you need to do is create a web service-based API layer dedicated to the compute tier. And, you’ll need your real-time models registered to the API layer, which will enable applications to retrieve information on how to structure API requests and the expected structure of output.
Though, ML models differ from traditional ones, because they are always learning. To help ML continue learning, you would have to create a training feedback loop and save inputs passed to the model, along with the resulting outputs (which need to be meaningful).
As you read through these four steps, we hope that they helped you better understand machine learning, and how you can use it for your enterprise. As you can tell, ML has enormous potential, but it’s important to ensure that your organization can take advantage of it all. By hiring ML-specific experts, implementing success metrics, and efficient model-building, your organization will soon be ML-friendly.