Start Your ModelOps Journey Today

Start Your ModelOps Journey Today

Introduction:

SAS Model Manager has been recognized as a top leader in the Machine Learning Operations Platforms MarketScape by IDC. With over 15 years of experience in providing proper governance, management, care, and maintenance of models, SAS Model Manager is the go-to solution for ModelOps. Whether you have just an hour or are looking for a comprehensive course, SAS offers a variety of educational resources to suit your learning preference. From webinars and tutorials to hands-on exercises and certifications, you can enhance your skills and knowledge in SAS Model Manager. Stay up to date with the latest features by subscribing to SAS Communities, where experts share resources and insights. Start your journey with ModelOps and SAS Model Manager today!

Full Article: Start Your ModelOps Journey Today

SAS Model Manager: A Comprehensive Guide to Learning ModelOps

SAS Model Manager has been making waves in the industry ever since IDC recognized it as a top leader in their Machine Learning Operations Platforms MarketScape. With its emphasis on governance, management, care, and maintenance of models, SAS Model Manager has become a go-to solution for organizations. Whether you’re new to ModelOps or an experienced practitioner, SAS offers a range of educational resources suited to different learning preferences.

Webinar: How Do I Use SAS Model Manager?

If you’re short on time and want to get a quick overview of SAS Model Manager, we recommend checking out the recent webinar titled “How Do I Use SAS Model Manager?”. This free webinar delves into topics like model registration, scoring, deployment, and monitoring. It provides detailed insights into key ModelOps steps, along with useful tips and best practices.

Quick Start Tutorial for Hands-On Learning

For those who prefer learning by doing, SAS offers a Quick Start Tutorial that allows you to explore the functionalities of SAS Model Manager in your own SAS Viya environment. This tutorial provides models and step-by-step instructions for activities like model comparison, testing, publishing, and monitoring. Even if you don’t have access to a SAS Viya environment, SAS has you covered. They have put together a guide to help you test drive ModelOps using the free trial version of SAS Viya. This trial offers a two-week window to explore all the capabilities that SAS Viya has to offer.

Instructor-Led Courses for In-Depth Knowledge

SAS’s education team offers both self-paced and instructor-led courses for individuals looking to gain comprehensive knowledge and hands-on experience with SAS Model Manager. While self-paced courses provide flexibility, instructor-led courses offer the advantage of direct interaction with course instructors. If you’re interested in getting hands-on instruction on SAS Model Manager, we recommend the “Managing Models in SAS Viya” course. This seven-hour course includes exercises and covers a wide range of topics. Additionally, for organizations looking to implement a robust ModelOps process, SAS offers a course specifically focused on ModelOps processes rather than technology.

ModelOps Specialist Certification for Expertise Validation

For individuals who have already mastered ModelOps, SAS offers the ModelOps Specialist certification. This certification serves as an excellent credential for those who have completed the earlier courses and want to validate their expertise. To help prepare for the certification exam, SAS has provided an exam guide.

Stay Up to Date with SAS Communities

Given that SAS Model Manager is updated monthly, SAS Communities is an excellent platform to stay updated on the newest features and resources. By subscribing to the SAS Model Manager tag on SAS Communities, you can access new resources and posts from experts in the field. This ensures that you stay in the loop and receive timely updates on the latest trends and developments in ModelOps.

Conclusion

Whether you prefer self-paced courses, instructor-led sessions, or hands-on practice, SAS provides a wealth of learning resources to help you master ModelOps and SAS Model Manager. In this article, we highlighted some of the key resources offered by SAS. However, we are interested in hearing from you. Have you come across any other helpful resources? Let us know in the comments below!

Summary: Start Your ModelOps Journey Today

SAS Model Manager is gaining recognition and popularity, especially after IDC named SAS as a leader in the Machine Learning Operations Platforms MarketScape. With over 15 years of experience in model governance and management, SAS continues to update and improve their Model Manager on a monthly basis to keep up with the fast-paced analytics industry. To help individuals become experts in ModelOps and SAS Model Manager, SAS provides a variety of educational resources. These resources include webinars, tutorials, trial environments, instructor-led courses, and even a ModelOps Specialist certification. By staying updated on the newest features and resources, individuals can enhance their knowledge and skills in ModelOps and SAS Model Manager.

Frequently Asked Questions:

1. Question: What is data science and why is it important?

Answer: Data science is a multidisciplinary field that involves extracting insights and knowledge from structured and unstructured data through various scientific methods, algorithms, and tools. It combines elements from statistics, mathematics, computer science, and domain knowledge to analyze data and make informed decisions. Data science is important because it allows organizations to uncover patterns, trends, and valuable insights from large volumes of data. This helps in making data-driven decisions, improving business processes, and gaining a competitive edge in today’s data-driven world.

2. Question: What are the key skills required for a career in data science?

Answer: A career in data science requires a combination of technical and non-technical skills. Some key skills include:

– Proficiency in programming languages such as Python, R, or SQL
– Strong statistical and mathematical knowledge
– Data visualization skills using tools like Tableau or Power BI
– Familiarity with machine learning algorithms and techniques
– Data cleaning and preprocessing abilities
– Effective communication and storytelling skills to present insights to non-technical stakeholders
– Curiosity and a problem-solving mindset

3. Question: How does data science benefit businesses?

Answer: Data science provides numerous benefits to businesses, including:

– Improved decision-making: Data-driven insights help businesses make informed decisions based on trends, patterns, and predictions.
– Enhanced customer understanding: By analyzing customer data, businesses can gain insights into their preferences, needs, and behaviors, allowing personalized marketing and improved customer experiences.
– Process optimization: Data science helps identify bottlenecks and inefficiencies in business processes, enabling companies to optimize and automate operations for increased productivity.
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– Competitive advantage: By harnessing data science, businesses can gain a competitive edge by identifying market trends, predicting consumer demands, and developing innovative products or services.

4. Question: What are the typical steps involved in a data science project?

Answer: A data science project generally follows these steps:

– Problem definition: Clearly define the problem and the goals of the project.
– Data collection: Gather relevant data from various sources and ensure its quality and reliability.
– Data cleaning and preprocessing: Clean and transform the data by handling missing values, outliers, and inconsistencies.
– Exploratory data analysis: Perform statistical analysis and visualization to gain initial insights and understand the data.
– Feature engineering: Create new features or transform existing ones to improve machine learning models’ performance.
– Model selection and training: Select appropriate machine learning algorithms, train them on the data, and evaluate their performance using suitable metrics.
– Model evaluation and refinement: Assess the model’s performance, fine-tune parameters, and iterate to achieve the desired outcome.
– Deployment and maintenance: Implement the model into production, monitor its performance, and update it regularly to adapt to changing data patterns.

5. Question: What are some real-world applications of data science?

Answer: Data science finds applications across various industries and domains. Some common examples include:

– Predictive maintenance in manufacturing: Analyzing sensor data to predict equipment failures and optimize maintenance activities, minimizing downtime and costs.
– Healthcare analytics: Analyzing electronic health records to identify patterns, predict disease outcomes, and optimize treatment plans.
– Fraud detection in finance: Applying anomaly detection techniques on financial transaction data to identify fraudulent activities and minimize risk.
– Personalized recommendation systems in e-commerce: Leveraging user data and machine learning to provide personalized product recommendations, improving customer satisfaction and sales.
– Sentiment analysis in social media: Analyzing social media data to determine public sentiment towards a brand, product, or event for better marketing strategies.

These are just a few examples, as data science has a broad range of applications in almost every industry, enabling organizations to leverage their data for better insights and decision-making.