In this lesson, we are going to talk about some real LM ops solution for you to to understand the kind
of things you are going to find in the market when you are looking for solutions.
Remember that we, uh.
We do not work with any provider at all.
We, when we recommend you uh, some tools, is because we think these are the most efficient tools.
These are going to be the better tools for you.
But as you know, this program is not sponsored by any company.
So we are totally independent in our advice.
Uh, when we when we, uh, talk about, uh, tools and alternatives in, in, in cases like this one.
So the first thing, uh, you need to know about the LM ops market is that, uh, the LM tools, the
LM ops tools market is that it is extremely recent, and it is in full expansion with new alternatives
appearing frequently.
So I will I will say it again, the market for LM ops tools is very recent.
And in full expansion, with new alternatives appearing frequently.
So market is booming.
There are more comprehensive tools like Wildlife's, like Wild Labs, and more specific ones like guardrails.
I in this lesson we will analyze two popular products from the company Wild Labs the product line kit
and the product LM Security Management.
So you will see that in the LM ops industry, uh, you find different kinds of tools.
There are some very specific tools, like the one provided by the company, guardrails I.
And you have other approaches that are more comprehensive that cover all the LM ops, uh, stages,
like some of the products that the company Y labs is offering.
So just as an example, we are going to talk about a couple of these solutions.
Uh, these are solutions, uh, uh, provided by the company Y labs, which is one of the companies
is one of them is one of the popular companies lately, but is not the best or the worst is just one
of them.
So we are going to talk about two products just for you to see the features they may offer, the things
they are, uh, right now solving the things that are still not on the table, etc..
So why, uh, the first of these products, uh, Lang Kit, focuses on extracting actionable insights
for content moderation and observability.
City.
The other one, LM Security Management, focuses on protecting LM applications against a wider range
of security risks, including prompt injections, data leaks, and misinformation.
So let's talk a little bit about each of them.
What are the main features of language?
As you know, one of the products offered by Y labs.
Language uses natural language techniques to extract actionable insights from prompts and responses.
Identifying and mitigating malicious prompts.
Sensitive data.
Toxic responses.
Problematic topics.
Hallucinations, and jail jailbreak attempts.
Language allows defining limits.
And detecting problematic prompts and responses in real time, taking appropriate actions in case of
failures.
Blanket validates how LLM applications respond to known prompts, both continuously and adhoc, to ensure
consistency when modifying prompts or changing models.
It extracts key telemetry data and compares it with intelligent baselines over time, aiding in debugging
and fine tuning of the LLM application.
Blanket integrates easily with public APIs.
And proprietary models.
It provides over 50 telemetry signals to assess the quality, relevance, sentiment, and safety of
prompts and responses.
Okay, so as we said, Lang Kit is focused on extracting actionable insights for content moderation
and observability.
Okay, so one one of the areas of liveops.
What about LM Security Management, which is the name of the second product of wildlife labs we are
going to analyze.
So this product includes protection against malicious attacks, prevention of data leaks, defense against
prompt injections, mitigation of disinformation.
And it adopts best security practices.
It implements telemetry to capture security risks.
Define it on all the you know here they are citing, you know, the name of the law that they are covering,
you know, allowing inline guardrails, continuous evaluations and observability.
This product handles various security risks like.
Insecure.
Handling of outputs.
Training.
Data poisoning.
Denial of service.
Supply chain issues and overreliance on LMS.
And finally, it includes guardrails and customizable logging.
It implements inline guardrails with customizable metrics, thresholds, and actions, and logs each
prompt respond.
Unlocks each prompt response pair.
So this is the literature you are going to find when you are trying to learn about the different tools
you have available in the Mops market.
As you see, I think the interesting, the interesting part of this review is that even when LM applications
are still so young, we are a finding a lot of areas where we can optimize them, where we can monetize
them, and where we can solve problems.
So you are going to find a large number of tools associated with LM application development and also
with LM ops.
So I think it was interesting to take a look at this couple of examples.
In the next lesson we are going to talk about a very important question, which is once you are working
as a artificial intelligence engineer or an LM application engineer, how do you, uh, stay up to date
in an industry, uh, with so many changes, so many news, so many new tools, so many new things to
learn.
Okay, so in the next lesson, we are going to talk about the top information channels for artificial
intelligence engineers in their own, uh, responses.
So we are going to see the results of a survey, uh, among artificial intelligence engineers regarding
top information channels in the next lesson.