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OUR LATEST ARTICLES

Now Accepting Applications. Earn Your Google Cloud Architect Certification for Free.

Portland, OR — Manceps, the artificial intelligence research and engineering firm, announced Monday that they are now accepting applications for their 2020 Scholarship. They year, they are giving data scientists and AI Professionals the chance to earn their Google Cloud Architect Certification for free. The prestigious certification was ranked the highest paying IT credential of 2019.

Here’s how it works: simply apply for the scholarship by clicking the link below, prepare for your exam, pass your test, and we’ll reimburse you your test registration fees. As the economy continues to struggle, now is the perfect time to add a well-known and highly regarded certification to your resume -- all for free. Spots are limited.

About the Google Cloud Architect Certification

A Professional Cloud Architect enables organizations to leverage Google Cloud technologies. With a thorough understanding of cloud architecture and Google Cloud Platform, this individual can design, develop, and manage robust, secure, scalable, highly available, and dynamic solutions to drive business objectives.

The Google Cloud Certified Professional Cloud Architect exam assesses your ability to:

• Design and plan a cloud solution architecture
• Manage and provision the cloud solution infrastructure
• Design for security and compliance
• Analyze and optimize technical and business processes
• Manage implementations of cloud architecture
• Ensure solution and operations reliability

Click here to apply. →

13.04.2020

The Complete Guide to Bringing AI to Your Organization

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Our Most Recent Articles

MedGemma: A New Era for Healthcare AI

🧠 Host Your Own AI Model - In-House

How to extract knowledge from documents with Google PaLM 2 LLM

DevFest West Coast 2020

50 AI Secrets: How Every Fortune 50 Company is Using AI Right Now

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OUR LATEST RESOURCES

OUR LATEST ARTICLES

MedGemma: A New Era for Healthcare AI

MedGemma is Google's revolutionary open AI model for healthcare, offering unprecedented control and data sovereignty. Its self-hosting capability ensures privacy and governance, enabling custom fine-tuning with proprietary data. This democratizes advanced medical AI, empowering organizations to revolutionize patient care and research with purpose-built precision.

🧠 Host Your Own AI Model - In-House

In an era dominated by cloud computing, there are still compelling reasons to host AI models on-premises. While cloud-based solutions offer scalability and convenience, certain environments demand more control, reliability, and privacy. Hosting models locally ensures greater data governance, allows compliance with industry or regulatory standards, and enhances security by keeping sensitive information within a closed network. It also becomes essential in situations where internet connectivity is unreliable or unavailable, such as in remote facilities, secure government operations, or offline field deployments. Additionally, on-prem hosting can offer reduced latency, cost predictability, and full control over model execution and updates—making it a critical choice for organizations with strict operational or compliance requirements. This will show you how to run a basic document Q&A offline using: Ollama + local LLM (Gemma3, Mistral, Llama3.3, etc.) LangChain FAISS (vector DB) SentenceTransformers (embeddings) PyPDF (PDF loading)

How to extract knowledge from documents with Google PaLM 2 LLM

PaLM 2 is Google's next generation large language model that builds on Google’s legacy of breakthrough research in machine learning and responsible AI. It excels at advanced reasoning tasks, including code and math, classification and question answering, translation and multilingual proficiency, and natural language generation better than previous state-of-the-art LLMs. It can accomplish these tasks because of the way it was built – bringing together compute-optimal scaling, an improved dataset mixture, and model architecture improvements. This article offers a quick and straightforward method for leveraging the PaLM 2 API to extract knowledge and ask questions from text.

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