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Career & Employment Opportunities

Implementing the Job Search API – Cloud Talent Solution


JENNY BROWN: In
our last video, we talked about what makes the
Cloud Talent Solution Job Search API a good
fit for improving the quality of job searching. Now, let’s look at the best
ways to implement the API into your job search solution
and go over planning details to get started. [MUSIC PLAYING] One important thing to note
about the Job Search API is that it’s a search
index of your jobs. This means it’s intended to
work alongside your existing database by mirroring
the contents and searching against
it in a manner that’s optimized to find the most
relevant results to a search query. This means it acts something
like a middle layer for the underlying system. In order for the API to best
determine relevant search results, it stores information
that you provide about the jobs and uses a pretrained
machine learning model with your provided data to
return relevant results. Once the API is integrated
into your system, your users will see high-quality
search results immediately. For many businesses, this
improvement in search quality is more than sufficient. If you want to further
refine the ordering, the API also offers
configuration options as well as the ability to
further train the model. We’ll talk about these
in depth in later videos. You’ll want to make sure
that you keep your job database as the
primary source of truth and use the Job Search
API as a search index. With that in mind,
let’s take a look at how to plan out
an implementation so you can connect your job
solution to the Job Search API. To get the data
into the API, you’ll connect it to your database,
but not to your front end quite yet. Using the Google
Client Library, which is available in a
number of languages, you can access the Job
Search API directly or you can implement a custom
connector through the REST or RPC interface. Once connected, you
can start by sending a list of jobs and
companies to the API, which then analyzes and indexes
them into a searchable form. When uploading the
data to the API, it’s important to consider
your capacity needs. Capacity planning makes
sure the API can provide the appropriate support for
all of the daily operations, such as adding and deleting
jobs and companies. Here are some
factors to consider when looking at capacity. How frequently are jobs
created, updated, and deleted? How often do job
seekers receive alerts? For both of these,
you’ll need to estimate the capacity for
batch data loads as well as normal daily usage. How frequently are job
seekers doing searches? Capacity here should be measured
for peak and daily usage. With capacity planning set
up you’re in good shape to start uploading data. Once your data is uploaded,
the Job Search API will be ready to use, thanks
to the built-in ML models. When designing your
integration, it’s important to ensure that
your solution is built in a fault-tolerant manner. That way, if the
Job Search API Index needs to be tweaked
for any reason, you can route traffic
back to your existing back end without interrupting your
users’ job search experience. We’ll go through and share more
information and tips to help you plan your implementation. Stay tuned for our
next video where we’ll dive a little deeper
and look at configuring search results from the API. Thanks for watching. And remember, when
looking for talent, it’s OK to keep your
head in the cloud.

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