Why do the majority of Data Science projects never make it to production?

Every day, businesses across a wide variety of industries share data to support insights that drive efficiency and new business opportunities. However, existing methods for sharing data involve great effort on the part of data providers to share data, and involve great effort on the part of data customers to make use of that data. However, existing approaches to data sharing (such as e-mail, FTP, EDI, and APIs) have significant overhead and friction. For one, legacy approaches such as e-mail and FTP were never intended to support the big data volumes of today. Other data sharing methods also involve enormous effort. All of these methods require not only that the data be extracted, copied, transformed, and loaded, but also that related schemas and metadata must be transported as well. This creates a burden on data providers to deconstruct and stage data sets. This burden and effort is mirrored for the data recipient, who must reconstruct the data. As a result, companies are handicapped in their ability to fully realize the value in their data assets. Snowflake Data Sharing allows companies to grant instant access to ready-to-use data to any number of partners or data customers without any data movement, copying, or complex pipelines. Using Snowflake Data Sharing, companies can derive new insights and value from data much more quickly and with significantly less effort than current data sharing methods. As a result, companies now have a new approach and a powerful new tool to get the full value out of their data assets.

This document provides an overview and introduction to Snowflake's cloud data warehousing capabilities. It begins with the speaker's background and credentials. It then discusses common data challenges organizations face today around data silos, inflexibility, and complexity. The document defines what a cloud data warehouse as a service (DWaaS) is and explains how it can help address these challenges. It provides an agenda for the topics to be covered, including features of Snowflake's cloud DWaaS and how it enables use cases like data mart consolidation and integrated data analytics. The document highlights key aspects of Snowflake's architecture and technology.

Cloud computing is an emerging technology that offers opportunities for organisations to hire precisely those ICT services they need (SaaS/PaaS/IaaS). Small and medium sized enterprises (SMEs) can benefit a lot from software services that are managed in a professional way. Cloud computing enables them to overcome restrictions from low budgets and limited resources for ICT. However, cloud adoption is challenging and requires a clear cloud roadmap. Organisations lack knowledge of cloud computing and are usually challenged by the adoption of cloud services. In most cases, SMEs do not know what aspects they have to take into consideration for a sound decision in favour or against the cloud. A cloud readiness assessment is a general approach to facilitate this decision-making process. The presented study focuses on the development of an assessment framework for cloud services (SaaS) in the domain of enterprise content management (ECM) and social software (ecollaboration).

1) Databricks provides a machine learning platform for MLOps that includes tools for data ingestion, model training, runtime environments, and monitoring. 2) It offers a collaborative data science workspace for data engineers, data scientists, and ML engineers to work together on projects using notebooks. 3) The platform provides end-to-end governance for machine learning including experiment tracking, reproducibility, and model governance.

Organizations are struggling to make sense of their data within antiquated data platforms. Snowflake, the data warehouse built for the cloud, can help.

Capgemini Cloud Assessment is a Cloud agnostic, vendor aware methodology that focuses on low risk, high return business transformation. Additionally, it reduces TCO and provides an early view of ROI. This closed loop assessment leverages pre-built accelerators such as ROI calculators, risk models and portfolio analyzers utilizing our deep partner ecosystem. We deliver an end state architecture, business case and deployment roadmap in just six to eight weeks.

In this session, we'll cover how with delta lake we can store the data and tables in databricks in an optimized way

1. What are the different Master Data Management (MDM) architectures? 2. How can you identify the correct Master Data subject areas & tooling for your MDM initiative? 3. A reference architecture for MDM. 4. Selection criteria for MDM tooling. chris.bradley@dmadvisors.co.uk

Snowflake's Kent Graziano talks about what makes a data warehouse as a service and some of the key features of Snowflake's data warehouse as a service.

MLflow is an MLOps tool that enables data scientist to quickly productionize their Machine Learning projects. To achieve this, MLFlow has four major components which are Tracking, Projects, Models, and Registry. MLflow lets you train, reuse, and deploy models with any library and package them into reproducible steps. MLflow is designed to work with any machine learning library and require minimal changes to integrate into an existing codebase. In this session, we will cover the common pain points of machine learning developers such as tracking experiments, reproducibility, deployment tool and model versioning. Ready to get your hands dirty by doing quick ML project using mlflow and release to production to understand the ML-Ops lifecycle.

Data Mesh is a trending approach to building a decentralized data architecture by leveraging a domain-oriented, self-service design. However, the pure definition of Data Mesh lacks a center of excellence or central data team and doesn’t address the need for a common approach for sharing data products across teams. The semantic layer is emerging as a key component to supporting a Hub and Spoke style of organizing data teams by introducing data model sharing, collaboration, and distributed ownership controls. This session will explain how data teams can define common models and definitions with a semantic layer to decentralize analytics product creation using a Hub and Spoke architecture. Attend this session to learn about: - The role of a Data Mesh in the modern cloud architecture. - How a semantic layer can serve as the binding agent to support decentralization. - How to drive self service with consistency and control.

This document summarizes a webinar on building machine learning platforms. It discusses how operating ML models is complex, requiring tasks like monitoring performance, handling data drift, and ensuring governance and security. It then outlines common components of ML platforms, including data management, model management, and code/deployment management. The webinar will demonstrate how different organizations handle these components and include demos from four companies. It will also cover Databricks' approach to providing an ML platform that integrates various tools and simplifies the full ML lifecycle from data preparation to deployment.

The document provides information about an experienced machine learning solutions architect. It includes details about their experience and qualifications, including 12 AWS certifications and over 6 years of AWS experience. It also discusses their vision for MLOps and experience producing machine learning models at scale. Their role at Inawisdom as a principal solutions architect and head of practice is mentioned.

Data scientists face numerous challenges throughout the data science workflow that hinder productivity. As organizations continue to become more data-driven, a collaborative environment is more critical than ever — one that provides easier access and visibility into the data, reports and dashboards built against the data, reproducibility, and insights uncovered within the data.. Join us to hear how Databricks’ open and collaborative platform simplifies data science by enabling you to run all types of analytics workloads, from data preparation to exploratory analysis and predictive analytics, at scale — all on one unified platform.

Delta Lake, an open-source innovations which brings new capabilities for transactions, version control and indexing your data lakes. We uncover how Delta Lake benefits and why it matters to you. Through this session, we showcase some of its benefits and how they can improve your modern data engineering pipelines. Delta lake provides snapshot isolation which helps concurrent read/write operations and enables efficient insert, update, deletes, and rollback capabilities. It allows background file optimization through compaction and z-order partitioning achieving better performance improvements. In this presentation, we will learn the Delta Lake benefits and how it solves common data lake challenges, and most importantly new Delta Time Travel capability.