My experience as technical content

Introducing Llama 3.3 The model is available to be downloaded from Meta’s website and on Hugging Face, an online model repository. Oracle Cloud Infrastructure (OCI) Data Science is a platform for data scientists and developers to work with open source models powered by OCI’s compute infrastructure with features that support the entire machine learning lifecycle. You can bring in Llama 3.3 70B Instruct from either Hugging Face or Meta to use inside OCI Data Science. OCI Data Science offers AI Quick Actions, a no-code solution for customers to seamlessly manage, deploy, fine- tune, and evaluate large language models inside our platform. I strongly believe, growing as a community is more important than growing as an individual. Let us make it simple, if I share my knowledge about statistics topic in a community, machine learning enthusiast will use it for a different purpose whereas someone who is working on IoT project uses it in a different way AI Quick Actions You can access AI Quick Actions from a Data Science notebook AI Quick Actions, see About AI Quick Actions AI Quick Actions has a Hugging Face integration AI Quick Actions One Healthcare organizations handle large volumes of sensitive data HIPAA and HITECH regulations workflows across departments The importance of access Healthcare organizations handle large volumes of sensitive data on a regular basis. With growing cyber threats, regulatory requirements, such as HIPAA and HITECH regulations, and complex workflows across departments, managing access to patient data is crucial. Integration with Oracle Health EHR Visibility into who has access Automated access controls Role and policy management Access request Compliance auditing Risk analysis Approach to managing user access across Enhanced security Streamlined onboarding and offboarding Dynamic access control Time and cost efficiency Compliance management Model class BlogCategory(models.Model): name = models.CharField(max_length=100, unique=True) description = models.TextField(blank=True, null=True) slug = models.SlugField(max_length=100, unique=True, blank=True) created_at = models.DateTimeField(auto_now_add=True) updated_at = models.DateTimeField(auto_now=True) def save(self, *args, **kwargs): if not self.slug: self.slug = slugify(self.name) super().save(*args, **kwargs) def str(self): return self.name

Apr 4, 2025 - 05:02

Introducing Llama 3.3

The model is available to be downloaded from Meta’s website and on Hugging Face, an online model repository. Oracle Cloud Infrastructure (OCI) Data Science is a platform for data scientists and developers to work with open source models powered by OCI’s compute infrastructure with features that support the entire machine learning lifecycle. You can bring in Llama 3.3 70B Instruct from either Hugging Face or Meta to use inside OCI Data Science. OCI Data Science offers AI Quick Actions, a no-code
solution for customers to seamlessly manage, deploy, fine-
tune, and evaluate large language models inside our
platform.

I strongly believe, growing as a community is more important than growing as an individual. Let us make it simple, if I share my knowledge about statistics topic in a community, machine learning enthusiast will use it for a different purpose whereas someone who is working on IoT project uses it in a different way

AI Quick Actions

You can access AI Quick Actions from a Data Science notebook
AI Quick Actions, see About AI Quick Actions
AI Quick Actions has a Hugging Face integration

AI Quick Actions One

Healthcare organizations handle large volumes of sensitive data
HIPAA and HITECH regulations
workflows across departments

The importance of access

Healthcare organizations handle large volumes of sensitive data on a regular basis. With growing cyber threats, regulatory requirements, such as HIPAA and HITECH regulations, and complex workflows across departments, managing access to patient data is crucial.

Integration with Oracle Health EHR

Visibility into who has access
- Automated access controls
- Role and policy management
- Access request
Compliance auditing
Risk analysis

Approach to managing user access across

Enhanced security
1. Streamlined onboarding and offboarding
2. Dynamic access control
3. Time and cost efficiency
Compliance management

Model

class BlogCategory(models.Model):
        name = models.CharField(max_length=100, unique=True)
        description = models.TextField(blank=True, null=True)
        slug = models.SlugField(max_length=100, unique=True, blank=True)
        created_at = models.DateTimeField(auto_now_add=True)
        updated_at = models.DateTimeField(auto_now=True)

        def save(self, *args, **kwargs):
            if not self.slug:
                self.slug = slugify(self.name)  
            super().save(*args, **kwargs)

        def __str__(self):
            return self.name